Know The What, Why, and How of Big Data?

Index

• What is big data?
• How much data does it take to be called Big Data?
• Why big data analytics is important?
• What are the 5 vs of big data analytics?
• Benefits and Advantages of Big Data Analytics
• How does big data analytics work?
• Is data visualization gaining popularity?
• Who uses big data analytics?
• Big data analytics uses and examples
• How does big data analytics add value to business organizations?
• How big data analytics is used in healthcare?
• How big data analytics are used in the banking industry?
• What is big data analytics in accounting?
• Where is the big data trend going?
• What is the role of intuition in the era of big data? Have machines and data supplanted the human mind?

What Is Big Data and How Did We Get Here?

Big data is a term for the enormous amount of structured and unstructured data businesses are now collecting and analyzing to speed up decision-making, improve efficiency, gain insights from customer data and to predict consumer behavior.

Introduction

The more data we have, the better our understanding of the world and our ability to make informed decisions. As many as 2.5 quintillion bytes of data are generated daily, with 90 percent of this data being unstructured. This number is expected to grow exponentially over the next few years due to big data technologies such as machine learning and deep neural networks. These technologies can analyze massive amounts of unstructured information for insights, ranging from customer behavior to predicting disease outbreaks.

What is Big Data?

Big data is simply a term that refers to the large volumes of data generated in all industries. It describes the technology used to process big data, called big data analytics.

 

What is the big data analytics process? Big data analytics is a process by which large amounts of information are analyzed for patterns and trends so businesses can make better decisions about their operations. Data from multiple sources are combined, allowing for more accurate analysis than possible with smaller datasets or single sources. Big data analytics enables predicting future trends, optimizing business processes, and improving customer satisfaction.

Why is Big Data Analytics Important?

Big data analytics is essential to business intelligence because it helps enterprises make better decisions. . First, it gives organizations the ability to better understand their businesses and customers by providing insights into how they behave and what they want. These insights fuel successful marketing strategies to enhance profit and grow business

 

Second, big data analytics allows companies to make predictions about future trends based on historical data collected from the past. For example, a company can leverage data to understand customer behavior for creating new products or improving existing ones based on market demands (this is called predictive modeling).

 

Businesses, small or big, have always tried to apply various techniques to make better decisions. With the emergence of predictive analytics, enterprises can now make informed decisions to improve their operational efficiencies, such as production schedules or manufacturing processes. Business analysts can now use predictive analysis to streamline operations, enhance revenue, and minimize risks.

 

There are two main types of big data analytics:

● Descriptive – Descriptive analysis focuses on understanding what happened in a specific situation by identifying patterns or connections within the dataset.
● Predictive – Predictive modeling builds models that help predict what will happen in the future by analyzing historical trends in the dataset.
 

Other types of big data analytics include exploratory (where patterns are spotted without a clear purpose), machine learning (which uses algorithms to learn about data over time), and prescriptive (which predicts outcomes based on historical events).

What is Big Data in Cloud Computing?

Large volumes of data require increased storage, processing power, and more. Cloud technology provides readily-available infrastructure and the ability to quickly scale the infrastructure to manage large spikes in usage or traffic.

 

So, how does cloud computing help with big data challenges?
Natural calamities can destroy office buildings or spaces, thus, wiping off all valuable data. But cloud storage facilitates making data safe and accessible in one location. In addition, cloud storage costs can be significantly lower than other options like on-premises or hybrid solutions.Many worry about the safety and security of their sensitive data, like medical data. However, a safe cloud environment ensures data privacy and safety compliance.

Conclusion

Businesses are constantly at risk due to technological changes, consumer behavior, changing market trends, etc. Big data analytics help identify the varying risks and develop risk management strategies by studying data patterns to deal with such challenges. Eventually, big data provides enterprises with the necessary insights to make quicker and better decisions.

Big Data Volume: How Much Data Does it Take to be Called Big Data?

Introduction

There’s no single definition of big data. Still, generally speaking, it means data sets that are so large and complex that they require to be processed by specially created hardware and software systems instead of the traditional enterprise servers or personal computers used by most organizations.

 

It represents the massive volume of data that consists of different forms of data collected, generated, or transferred through streams or any other form of communication that can be processed in real-time.

 

Big data management is a complex field with many variations depending on the type. It refers to high volume, high velocity, and a large variety of information assets that require new types of processing to enhance decision-making, insight discovery, and process optimization.

How Much Data is Big Data?

Big data is primarily driven by five pillars: volume, velocity, variety, value, and variability. Amongst all, volume is the most crucial parameter in determining whether or not, a specific form of data qualifies as big data.

 

Big data volume, which was formerly measured in gigabytes (GB), is now measured in zettabytes (ZB) or even yottabytes (YB).

Characteristics of Big Data

These five pillars represent major characteristics of big data, which we will discuss below.

● Big Data VolumeThe quantity of data produced by businesses or individuals is called big data volume. Big Data volume, which was formerly measured in gigabytes (GB), is now measured in zettabytes (ZB) or even yottabytes (YB). Data is growing exponentially due to the IoT (Internet of Things). In the upcoming years, a considerable shift in the amount of data is predicted.

● VarietyIt generally represents a type of data. Variety symbolizes the diversity and multitude of information sources that make big data. It includes traditional numerical data, audio and video streams, financial transactions, email documents, and metadata.

● VelocityVelocity is the rate of generating new information and the speed required to process it to provide timely and relevant insights. The velocity of processing and analyzing big data is often critical because of the mission-critical information that comes from RFID tags, connected sensors, and smart meters.

● ValueA piece of data cannot be considered big data if it has no value, which indicates that it cannot be used to extract information that would be useful in and of itself. It will be called big data only when it gives some value to the organization. Precise and accurate data provides high values and help the organization in making the right decisions.

● VariabilityVariety is distinct from variation. There may be six different coffee blends available at a coffee shop, but variability occurs if you consistently order the same blend and it tastes different each time. The same is true for data; if the meaning changes frequently, it can significantly affect your data homogeneity.

Final Thoughts

From a big data volume perspective, data can seem like an elusive term with vague definitions and boundaries. However, understanding the various types of big data analytics tools and how they help solve problems can help you understand what big data is. Various open-source Big Data analytics tools are available to further your knowledge! And if you feel overwhelmed with the skills required to manage big data, you can reach out to MSPs to manage it for you.

What is the Importance of Big Data Analytics in the Global Market?

Introduction

A Global research article released on 22 June 2022 by Techjury claims that the Big Data Analytics market is estimated to reach a value of $103 billion by the year 2023, with 97.2% of businesses investing in Big Data Analytics. The global market is flooded with advanced analytics, stating that about 45% of organizations are running Big Data on the cloud.

 

To understand the importance of Big Data Analytics in the tech market, let us first get a clear picture of what is Big Data Analytics and why it is creating waves in the industry

What is Big Data Analytics?

Big Data Analytics helps companies make decisions based on numbers and systems that work in the market. It is a complex process through which large, unstructured information is decoded to understand hidden patterns, analogies, customer behaviors, and market trends. This information helps organizations make data-driven decisions that can help their businesses grow.

 

In other words, Big Data is the term used for data whose type, structure, and size are way different and large for any traditional database to process, store and capture. Big data sources are the internet, artificial Intelligence (AI), sensors, devices, networks, log files, the web, and social media.

Here are a few examples of Big Data:

• 65 Billion messages are generated daily on WhatsApp
• 3.5 Billion searches daily on Google
• 80% to 90% of unstructured data in the market
• 2.80 Billion active users monthly on Facebook

Sounds daunting, right? However, this data is a gold mine of valuable information that can help organizations in making informed decisions through actionable insights deduced from Big Data Analytics.

The Importance of Big Data Analytics

Statistics show that the market value for Big Data Analytics will rise to $62.10 billion by the year 2025. What makes Big Data Analytics so important in the current economy? Let’s have a look.

● Helps with Business Risk Management: Data from larger sample sizes is proven to be accurate and helps companies make accurate decisions, thus, facilitating lower risks and higher returns.
For example: Banco de Oro a banking company, uses Big Data Analytics to identify fraudulent activities from their schemes.

● Supports Product Development and Innovation: Advanced analytics helps companies understand their consumers’ needs and concerns while providing them with information about the performance of their products in the market. This provision helps companies create and develop useful offerings for their prospects.
For example: Rolls-Royce uses Big Data Analytics to analyze and improve the design of their jet engines.

● Helps organizations with Quicker and Better Decision Making: Big Data provides data across metrics like location, demography, people’s preferences, etc. which helps companies to make better decisions regarding their products.
For example: Starbucks uses advanced analytics to decide which products on their menu would be suitable for different stores across locations.

● Improves CX: Advanced data analytics span all social media and internet comments which help companies monitor public reviews on a larger scale. Quick decisions are taken depending on these analytics which improves customers’ experience.
For example: Delta Airlines monitors customer tweets and reviews about their journey and offers compensations and services based on the insights received from the analytics.

Which is the best Big Data Analytics for your organization?

Let’s understand the four different types of Big Data Analytics that top organizations use to expand and decide which will suit your firm best:

● Descriptive Analytics: Descriptive analytics is used to make company reports, revenue, profit, and sales. It is made in a form that makes it easy to read and acts as a summary of the data received from different sources.

● Diagnostic Analytics: As the name suggests, diagnostic analytics is the problem detector in the Big Data system. Tools like drill down, data mining, and data recovery are diagnostic analytics that identifies a problem’s cause.

● Predictive Analytics: Predictive analytics is advanced analytics that helps predict data through historical and present data. Analysts use data mining, AI, and machine learning tools to analyze the presented data to make predictions that drive major decisions.

● Prescriptive Analytics: Like a prescription is a list of remedies, prescriptive analytics is a solution to a particular problem. It works with descriptive and predictive analytics using AI and machine learning to recommend actionable insights or solutions.

Conclusion

While Big Data Analytics is the key player in the growth of any organization, there are various challenges that companies face in conducting such analysis. As the name suggests, the huge amount of data that needs to be collected is highly difficult to retain. Organizations may also find it tedious to maintain the quality of the data they receive. Much information can be fake because the data comes in from extended sources. Maintaining quality helps organizations get accurate results.

 

Your company can greatly benefit from the results of Big Data Analytics. However, it is essential to keep your data secure, as it contains highly confidential customer reviews gained from various sources. Organizations can reach out to IT consulting services and solutions to help them maintain the security and smooth processing of their Big Data Analytics.

The 5 Pillars and challenges of Big Data

Introduction

We’re heading towards a data-driven world. Oberlo reports that the number of digital buyers was 2.14 billion in 2021. That is a whopping 27.6% of the world’s population. However, this number can be over 50% because many people hop on and off the net, prefer to shop offline, and are not yet net savvy.

 

As a Chief Experience Officer (CXO) or marketer, this may not twang your bow, but here’s something quirky about big data that definitely will. The 5 pillars of big data, also called the 5 Vs of big data, are its biggest challenges. Confused? Let’s take a look:

What are the Vs in Big Data?

Gartner first introduced the idea of three Vs of big data- Volume, Velocity, and Variety. As the adoption of big data increased, so did concerns about authenticity, which gave birth to the 4th V of big data – veracity. Together, these characteristics came to be called the 4 V’s of big data. Over time, several big data analytics tools emerged, and the idea of data mining was introduced. Much later, value was added to the list of 5 Vs.

Where did the 5Vs of Big Data Come From?

Big data is a humungous data set – often called a data lake or data warehouse – that is of little use in and of itself in its original format. When data miners sort, clean, filter, integrate, and structure the data, it becomes helpful to analysts who transform it into snapshots of business intelligence that help CXOs create and execute business strategies. Gradually, big data transitioned from the big data 3 Vs to the 5 Vs to help data scientists communicate and articulate the significant characteristics of big data.

The Five Vs of Big Data

The transition from big data 3v to big data 5v was rapid. Later, more characteristics were added, and people talked about big data’s 8 or 10 Vs. But the 5 vs of big data are the most significant. Let’s dive into each one of them:

Volume

As data volumes grow, so does their significance until the sheer number of records to be processed becomes challenging. Thus, big data analytics tools clean, sort, and integrate data from diverse sources. These tools include customer data platforms, data integration, and analytics software. Businesses are increasingly adopting these solutions to drive, track, and get ROI from their marketing efforts.

Velocity

Not long ago, data was dumped into data lakes until the decision makers asked for it, and miners found time to mine it. In 2022, marketers and decision-makers are demanding data streaming in real time. Data scientists and handlers are now integrating AI and ML into big data analytics tools to meet this demand for real-time data. This is posing new challenges in the area of big data management and analytics. The biggest challenge is to match the speed at which data pours in, structure it, clean it, and present it in a form that makes sense to CXOs and other stakeholders when they need it the most.

Variety

Variety ranked among the initial big data 3 Vs. Variety means the diversity of data types. An enterprise has several sources to collect information, both from inside and outside. However, the challenge in variety is the distribution and standardization of all collected data.

Veracity

As the volume of consumer data increases, concerns about privacy and misuse rise. Customers today are adopting innovative ways to keep their data personal. Consumers adopt different methods to protect their privacy, from using pseudonyms to outright refusal to share data. Moreover, consumers are fickle beings. The same consumers may explore sports shoes on an app and look at laptops on the website. They may add a T-Shirt into their cart from a website only to abandon it later and buy a significant amount of food items ending up making a huge purchase.

 

Developers of big data analytics tools are thus challenged with ensuring that the data presented to decision-makers is authentic. Other regulatory laws and norms to protect consumers’ data are evolving, and keeping up with compliance has become a big challenge for data scientists, miners, analysts, and decision-makers alike.

Value

All activities and investments around big data are worthless if they do not deliver value or ROI from the investment. ROI can be determined after data handlers run the big data analytics tools. So, deriving value from your big data becomes the fifth pillar of big data.

Conclusion

While understanding the 5 Vs of big data is essential, the more important question is how to leverage this knowledge to benefit your business. At Techwave, we provide complete data analytics solutions and digital value mapping to drive your business. Reach out for a consultation now.

How can companies leverage the benefits of big data analytics?

Introduction

Customers regularly are inundated with numerous ads for different products and services. The number of choices to make a decision is exhausting. So, what makes a customer stop and pay attention to a particular brand, product, or service?

 

As brands pay attention to answering this question, they have become more innovative and creative. Many are attracted to the endless benefits of big data analytics. According to a 2022 survey, ‘the global big data and analytics market is worth $274 billion.’

 

The concept of data analytics has been around; however, it was not appreciated and recognized until the benefit of data revolutionized businesses. Many reputed brands now rely on their data pool and apply big data analytics solutions to transform the same into actionable and valuable insights.

 

For example, in 2016, Starbucks used AI to send personalized offerings and messages to all its customers via email. Besides personalizing the drinks, the brand also relied on and used its app and loyalty cards to analyze customer data and offer customized drinks. With its efforts, the brand aimed to upgrade its customer’s next mobile coffee order assuring maximum satisfaction.

 

With digital evolution, all the major reputed brands are using big data analytics to achieve success with a competitive advantage. Here are some common examples of reputed brands using data analytics to reach the next level of success –

● Amazon – uses its massive pool of data to gain access to its customer names, addresses, payments, and search history to improve customer relationships using advertisement algorithms

 

● The American Express Company – relies on data analytics for business professionals to analyze and observe customer behavior

 

● Netflix – gains insights into its customer’s preferences, likings, and watch history using the benefit of data to examine their viewing habits.

 

Similar to these brands, many others are leveraging the benefits of big data as a significant part of their businesses. If you are a brand considering investing in big data analytics, let us explore the possible benefits and advantages that can be expected. What are you waiting for?

Benefits and advantages of big data analytics

Thanks to technological innovation, the rules and regulations have changed regarding data analytics and the advantages of big data analytics. The advanced software has reduced analytical time, ensuring speedy and strategic business decisions to increase revenue and growth and reduce costs. Let’s look at how big data analytics has helped organizations achieve these critical objectives.

1.Acquisition and Retention of CustomersOrganizations maintain a unique marketing approach to ensure a distinguished product and service identity. By gaining an in-depth understanding of big data, the brands can completely understand what their customers are looking for and leverage the benefit of data to accelerate their sales.
How?
By employing the various available big data analytics solutions, brands analyze their customer’s search and buy patterns. The same pattern is used to attract brand loyalty and enhance customer satisfaction.
For example – Do you know an increase of 2% in customer retention can lower the business cost by 10%? Moreover, a business loses 71% of its customers because of inadequate customer service.
Big data’s insurmountable power can be used by businesses to connect with their customers and to understand what they are thinking and want based on the statistics of the real-time customer reviews, enquiries, complaints, and similar other valuable data.

2.Focused Ad CampaignsAre you tired of spending money on ad campaigns and still not getting desired results? Forget spending on ad campaigns that don’t pay you off! Big data analytics ensures a sophisticated way to analyze and observe customer trends by monitoring online buy and point of sale transactions.
As a result, businesses can deliver highly customized products based on customers’ preferences and likings. Companies can rely on these analyzed data and trends to create successful campaigns and garner customer loyalty.
For example – The Croatian camping agency – AdriaCamps uses big data to identify the best communication channel to connect with its customer base. The brand uses big data solutions to coordinate SEO, email, and content marketing to generate better potential leads and nurture its potential customers toward a sale.

3.Better Focus on Innovative ProductsBig data analytics also supports companies to update the existing products and focus on innovating new ones simultaneously. By collecting real-time customer data, companies can easily distinguish what works best in their favor.
Besides adding a spark of instinct, organizations now focus on tracking customer feedback, competitor moves, product success, etc., on creating a lasting impression.
For instance – pCloud, a popular secure cloud storage platform, uses big data analytics for testing and improving its products. After launching a strategic advertising campaign for promoting its platform’s new feature, the brand analyzed its customer interactions.
Based on it, the brand optimized its steps to improve its platform further to boost conversion rate and generate better sales.

4.Identification of potential risksNowadays, businesses are functioning in high-risk environments that require attention to the minutest detail. This competitive environment calls for an accurate and efficient risk management process.
According to the present need, big data business analytics can improve risk management effectiveness to make strategic planning and risk management models.
For example – One of the popular companies using big data, The Weather Co. – an IBM unit, analyzes user behavior from 3 million worldwide locations. The brand identifies potential business risks to boost its sales by combining climate prediction and consumer behavior data.

5.Viable Solution for Complex Supplier NetworkCompanies also enjoy the benefits of big data analytics to offer high precision, insights, and accuracy to the supplier network. Big data is extremely helpful in limiting the usual constraints faced for retaining and attracting customers.
For example – According to Alex Baldock, CEO of Very – the British online retailer, “We know that relevance wins in retail, and right now, customers are drowning in a sea of irrelevant choices. We’re making it easier for them to shop by tailoring our websites for them.”
With more than 1.2 million variations of its homepage for different customers, Very relies on the benefits of big data powering personalization.

6.Improved EfficiencyBig data analytics is also helpful in improving the operational efficiency of reputed brands. Meaningful data patterns deduced from the analytics can be used to create personalized customer products and services. Various data analytics tools can automate the entire process to free the employees’ valuable time, which can be used for other essential business tasks.
For example – Pantene and Walgreens provided targeted ads for their anti-fizz products to customers in humid climates under the promotional campaign ‘hair cast.’ The campaign focused on ensuring solutions to customers’ real-world hair challenges. As a result of the targeted ads, the brand experienced a 10% increase in sales.

7.Cost OptimizationAre you still looking for a cost-effective way to analyze and handle large volumes of data? Leverage the benefit of data to store, process, manage and analyze valuable customer data without any hassles.
Data analytics also help reduce the product return cost by accurately calculating the product return chances.
For example – In the eCommerce space, the product return cost is usually 1.5-times higher than the overall shipping cost. By delivering tailored products and services to customers, companies use the advantages of big data analytics to ensure cost optimization.

How are these benefits of data analytics helpful for businesses?

Big data analytics boosts sales and revenue by empowering businesses with better decision-making, irrespective of their size, customer base, and industry. If you are a business with plenty of data and are unsure how to use it, leverage the benefits of big data analytics.

 

Data is useless unless there is a skill to analyze it. Big data enhances business performance by personalizing the data to –

• Identify and analyze the problem you are trying to target
• Filter and sort the available data in the structural form before putting it into use
• Focus beyond just visualizing the data. Big data is helpful to get the insights that drive the required solution to a business problem
• Prepare a budget and plan beforehand for modeling data

Concluding Thoughts:

With the ability to access and analyze data from disparate systems, businesses are better equipped to make the best decision that fosters innovation and drives revenue. The added benefits of customer retention, real-time insights resulting in quicker improvisations, faster time-to-market, and more have defined the future of big data analytics and professionals – they are here to stay!

Big Data Analytics: A New Way of Transforming Insights Into Meaningful Action

Introduction

Big Data Analytics is the process of recognizing trends, patterns, and correlations in massive amounts of unprocessed data to support data-informed decision-making. To perform data analytics, prominent data engineers use specialist software and hardware. As a result, traditional data analysis systems are undergoing drastic modifications.

 

The industry across sectors uses data analytics technologies and techniques that help businesses make better decisions. Analytics also allows companies to react swiftly to changing market trends and acquire an advantage over rival companies.

 

The evaluated data is processed to perform real-time analytics or historical records, which may comprise new data, depending on the specific application. Additionally, it might originate from internal systems and outside data sources.

How does Big Data Analytics Work?

Big data analytics is the process of capturing, aggregating, and analyzing huge amounts of collected raw data to make it a valuable resource for businesses.

1. Capture Data: Every organization has a different approach to data collection. Thanks to modern technology, organizations may collect structured and unstructured data from various sources, which include cloud storage, in-store IoT sensors, and more.
Data warehouses will store some of the data, so business intelligence tools and solutions can quickly access it. A data lake may hold raw or unstructured data that is too diverse or complicated to be stored in a warehouse.

2. Aggregate Data: For analytical queries to yield correct answers, data must be appropriately organized after it has been gathered and stored, especially if the data is big and unstructured.
Data processing is becoming more difficult for enterprises as data availability increases exponentially. Batch processing, which examines big data chunks over time, is one processing choice.
When there is a longer gap between data collection and analysis, batch processing is advantageous. Small batches of data are examined all at once using stream processing, which reduces the time between data collection and analysis to enable quicker decision-making. Stream processing is more expensive and complex.
Besides these, cleaning and organizing data at every possible level are inevitable to ease the data analysis process. All data, regardless of size, must be scrubbed to increase data quality and produce more robust results. Redundant or staple data must be removed or accounted for, and all data must be structured correctly. Stale data can conceal and deceive, leading to inaccurate insights.

3. Analyze Data: It takes time to transform massive data into a usable form. Advanced analytics techniques can transform massive data into significant insights once they are ready. Among these extensive data analysis techniques are:

• Data mining It goes through vast datasets to identify patterns and correlations by identifying anomalies and creating data clusters.
• Predictive Analytics It examines future projections using previous information of an organization to identify potential risks and opportunities.
• Deep Learning It layers algorithms to uncover patterns in even the most complex and abstract data, emulating human learning patterns in the process.

Key Takeaways:

In the coming years, the global big data analytics market will grow at a CAGR of nearly 30%, with revenue exceeding 68 billion US dollars by 2025, up from around 15 billion US dollars in 2019. Hence, big data analytics has become indispensable. They allow businesses to leverage enormous amounts of data in many forms from several sources to detect possibilities and threats, enabling them to act swiftly and boost their bottom lines.

Is Data Visualization Gaining Popularity?

What is Data Visualization?

Businesses today generate massive volumes of data. Rivers of data gush into a data lake or data warehouse, waiting to be utilized.

 

Data visualization is the process by which the data is made meaningful. Data scientists use big data visualization tools and techniques like maps and graphs to present the data to the decision-makers in a way that makes it easy for them to identify trends and patterns.

Importance of data visualization

Data in graphic or pictorial form is easily understood. That is the primary objective of data visualization. Graphical data representation helps businesses identify consumer pain points, behavioral traits, preferences, and so on, on one hand, and changes required in user experience, products, services, etc., on the other. They can also make predictions that drive business growth.

 

Data analytics and visualization are, therefore, vital steps in the process of business intelligence. However, there’s another angle to data visualization.

 

For Example, during online shopping, it is generally noticed that eCommerce sites show up higher on the Search Engine Result Page (SERP) than the brand websites. Amazon, for instance, almost always ranks at the top in product search, followed closely by Flipkart and Snapdeal.

 

That’s because not only do these sites arguably collect the most significant amount of consumer data, but they also can visualize it in a way that benefits the consumer. Ergo they can show the user exactly what they want when they want it. The more relevant we can be, the deeper our insights can be. Remember that Google and most other search engines consider relevance when ranking on SERP.

 

Data analysts clean, structure and present multiple data representations of raw data from the data warehouse to enable critical business decisions. A lot is happening behind the scenes in this type of data analytics and visualization. Artificial Intelligence (AI) and Machine Learning (ML) are now included in big data visualization tools and techniques.

How does visual data representation aid business?

Serving the consumer well is paramount for any business. To do so, businesses must know what their customer is looking for, what they want, and, most importantly, what they desire. And then, accordingly, they should present it to them at the appropriate moment.

 

Let’s take an example of a food delivery app. Suppose one of their customers keeps searching for seafood starters. If the app could show them these at the moment, they open it, and the app hooks them for life!

 

Now think a bit further. If that particular customer is looking only for appetizers, perhaps he is cooking a simple main course at home and ordering dessert elsewhere. So why not offer him all this in a single deal that fits his pocket? In this way, we’ll have one happy customer there! By grabbing a few more like him, we will have booming sales! But that’s the easy part.

 

So, how can this be achieved? That’s where data visualization tools and techniques come in handy. Here’s how data visualization intelligence can benefit businesses:

● Establishing Correlations
It’s hard to discern patterns unless businesses establish relationships between variables. Data analytics and visualization can help them do that. In the above scenario, they can establish a relationship between the customer’s preference for appetizers to their budget and visualize their need for a main course and dessert.

● History and Projection
Well, only when that picky customer picked seafood starters repeatedly could we discern his preference for seafood, right? So, historical data can help make predictions. Let’s say that guy is aged 25 – 35. Now that we know this, we could show all customers within those demographics the same suggestions for seafood. That’s projection using visualization intelligence.

● RFM
Recency, Frequency, and Monetary value that’s what RFM stands for, and that’s what big data visualization tools and techniques can provide. Data about how often a business’s customers purchase, the ticket price, and their most recent purchases can help them segregate their customers into high-value, frequent fliers, fly-byes, and more segments.
Think about how businesses can use this information to drive sales through upselling, cross-selling, and check-out suggestions, to name just a few strategies.

● Market Insight
Not only can data visualization intelligence help businesses understand their consumers better, but it can also give them deep insights into new markets they may be exploring. Moreover, it may also open windows of opportunity for them to explore.
For Example, let’s say the seafood buff in our example is on a gluten-free diet, which means no wheat for that customer. What would they offer him for the main course? That’s a big window of opportunity to target those persons who are gluten intolerant and looking to order the main course online. Now go ahead and explore that!

● ROI
ROI is a safe metric – or so most people think. But without looking at the numbers and crunching them a bit, businesses would never know whether or not their investment is earning a return. By leveraging data visualization, they can use historical trends to predict risks and rewards and keep themselves safe.

Final Thoughts

Marketers and CXOs across industries, sectors, and segments use data visualization extensively to generate business intelligence. It’s tempting to think deriving visualization intelligence is simply using their imagination. Still, it does require a certain mindset to be able to visualize data in a certain way.
Moreover, visualization is easier when businesses have cleanly sorted and structured data and the full complement of data visualization tools and techniques at their disposal.

How do leading industries leverage big data analytics?

Introduction

On average, Google processes 8.5 billion searches daily. Ever wondered about the science behind this? Enter Big Data Analytics.   Big data is beneficial across industries to understand trends and patterns and derive other valuable insights. The technology has proven to be a game-changer in improving business-related outcomes, yielding higher profits, and scaling business models.

What is big data analytics?

Simply put, big data analytics refers to the use of advanced analytic techniques to examine big data and extract meaningful insights from – market trends, correlation, hidden patterns, and customer preferences to help companies make informed decisions. These insights help companies discover new revenue opportunities, marketing strategies, and tips to boost operational efficiency and offer a personalized customer experience.

What are the benefits of big data analytics?

Before jumping to the next section, let’s examine why companies use big data analytics. Here are a few reasons and benefits of big data analytics.

• Helps companies make better and more informed decisions
• Obtains real-time forecasting of the market and industry trends
• Improves internal operations and efficiency
• Helps identify potential risks and threats
• Helps offer an improved and better customer experience

Top 7 Industries that Use Big Data Analytics

Industries across sectors leverage the benefits of big data. Here’s a list of the top seven industries that use big data analytics.

1. Healthcare: The healthcare industry deals with massive data in terms of patient records. With big data analytics, healthcare organizations can get insights into clinical data and make informed decisions on the diagnosis, treatment, and prevention of diseases, and much more. It can help the healthcare sector to offer personalized treatment and medicines to patients.

2. Media and Entertainment: Big data has led to considerable improvements in the entertainment and media industry. The industry uses the data to recommend the right content and ad to viewers, target the audience with useful content, and measure the content’s performance. Major OTT service platforms, like Netflix, Spotify, Amazon Prime, and others, use big data analytics to suggest personalized content to customers.

3. Manufacturing and Natural Resources: Natural resources, including agricultural products, oil, metals, etc., are in huge demand and involve a massive volume of complex data. Similarly, the manufacturing industry also deals with high data volume. Analyzing this big data helps manufacturers trace, track and streamline the complete supply chain process. Big data analytics in natural resource management can also help organizations predict the future and make informed decisions for the coming generations.

4. Banking : The finance and banking sector deals with several challenges, including card fraud detection, credit risk reporting, IT policy compliance analytics, trade visibility, and much more. With big data analytics, financial institutions can monitor customer spending patterns and identify unusual behavior to prevent fraudulent activities. Banks can also leverage big data analytics to oversee KYC verification, prevent money laundering and identify other potential risks.

5. Retail : With the e-commerce boom, the retail industry has collected tremendous data over the past few years. Big data analytics lets businesses offer personalized recommendations based on customers’ purchase history in the retail sector. Leading retail giants like IKEA, Costco and ASOS use big data analytics to offer personalized shopping and improve customer experience.

6. Education : The education industry deals with academic data. Through big data analytics, teachers and professors can track students’ performance. The data gives insights into teachers’ performances and how well they act on students’ feedback. They can also analyze why the students struggle and where they require personalized learning.

7. Transportation : The transportation sector can use big data analytics in multiple ways. For instance, the government can use it for traffic control, congestion management, and implementing an intelligent transport system. Transport providers can use it for logistical planning, revenue management, and more. Individuals can utilize it to plan routes for better fuel and time savings and arrange travel plans.

Final thoughts

Big data is no longer a trend that will pass or a buzzword that will fade away. It helps regulate how to collect and manage vast chunks of unprocessed raw data and offers valuable insights for enhanced customer experience and business growth strategies.
Everything boils down to understanding customers and offering what they expect. There are endless possibilities for industries to leverage big data. It can be utilized promptly by understanding the industry-specific challenges. And big data analytics helps companies innovate, provide solutions, and provide them right!

4 Big Data Analytics Use Cases, Examples, and Applications

Introduction:

The most pressing problem of organizations today is the staggering amount of structured, unstructured, and semi-structured data they generate. Organizations struggle to manage massive data storage and cannot use it constructively. Big data analytics has revolutionized the technological era by resolving the global enterprise big data problem. Corporations can have an adequate data outcome by identifying new market trends, customer preferences, and hidden patterns.

 

Big data uses advanced analytics tools, such as algorithms, predictive models, data mining, and machine learning techniques, to ensure effective marketing strategies for enterprises and boost their operational efficiency and revenue generation.

 

According to projections, over 180 zettabytes of data will get generated globally by 2025. In this article, we will discuss cutting-edge solutions with industry examples of big data applications and big data use cases.

How Does Big Data Analytics Work?

Businesses use big data analytics to integrate, process, capsulize, and analyze large datasets to leverage business operations, as described below.

● Integrate data : Organizations gather both structured and unstructured data from a variety of networks, including cloud storage, mobile networks, technological networks, social media, etc. Once collected from diverse sources and applications, data are combined with big data to form insightful outcomes.

● Process data : Unstructured data must be properly organized after it has been integrated and stored to yield accurate results. More specifically, data must be organized, cleaned, and configured for the smooth execution of analytical queries.

● Capsulize data : Data capsulizing is the process of encoding, reorganizing, or altering data to reduce its size. The process involves re-encoding data using fewer bits than the original representation.

● Analyze data : It takes time to analyze and transform enormous data into a usable form. Advanced analytics techniques can transform big data into significant insights once they are ready. Many big data analytics examples use data mining, machine learning, and deep learning to get insightful results.

4 Big Data Analytics Use Cases

Understanding how enterprises use data analytics is crucial. Let’s look at some of the big data use cases:

● Cost reduction : Big data analytics helps businesses better understand their customers’ needs and make better strategic decisions with its data-driven insights. Additionally, operational procedures are simplified, lowering operating costs and improving the business model’s effectiveness.

● Fraud Prevention : Big data can detect fraud or suspicious activities before they occur. It gathers data in real time and aids in the search for unusual or suspicious interruptions. A reliable and robust big data analytics platform ensures dedicated and continuous data streams as the organizational data grows./p>

● Boosts efficiency : Reliability, performance, and overall decision-making in business organizations are optimized by big data analytics. Big data improves service delivery techniques, accurate customer segmentation, insights into consumer behavior patterns, and service customization for greater efficiency./p>

● Social media analysis : The posts and stories on social media platforms are significant and contain valuable data points. Businesses that do not pay close attention to what their clients say about their products on social media fail to address complaints or offer personalized service. Big data can track social media sites in real-time and probe customer sentiment to oblige businesses to take fair actions./p>

Big Data Applications Examples

Big data has evolved to the point where it now impacts almost every facet of our lifestyle, buying patterns, and consumer choices. Let’s check out some of the big data in business examples.

Healthcare : Healthcare organizations can produce data-driven healthcare solutions to improve patient outcomes in various ways. Patients’ health records are constantly updated with sensors with real-time implementation. This helps mitigate severe health conditions, accelerates research, predicts the onset of epidemics, and upgrades the analysis of medical images.

Education : In the education sector, officials and other faculties use big data to improve curricula, identify top talent, and facilitate student experience. Customized curricula, lower dropout rates, better student outcomes, and targeted international recruiting are all made possible with big data analytics.

Transportation : Businesses in different areas of transportation and travel, including airlines, airports, logistics, railways, and others, are benefiting from big data’s ability to handle the ample quantity of data they currently possess. Big data facilitates and simplifies transportation via route planning, traffic safety, and congestion management.

Stock Market : Every day, a vast amount of data is produced in the stock market. These datasets are very dynamic in addition to being significant in volume. Both buyers and sellers may benefit from the real-time analysis of this data. Big data also detects fraud and other illegal activities that help to boost the stock market performance.

What is the Future Scope of Big Data Analytics?

Big Data Analytics has wholly transformed the IT industry. The vast data generated by sensor-enabled devices, mobile platforms, cloud services, media platforms, and satellites are improved with big data. It allows users to run virtual machines, store massive amounts of data online, and do various other things.

 

For example, the Indian government recently announced the use of big data analytics to understand the sentiments and ideas of Indian citizens via crowd-sourcing platforms like websites and social media to gain a picture of common people’s thoughts and opinions on government actions.

 

In the future, it will be a one-stop shop for cloud-based applications, online gaming, mobile applications, and so on. All of this necessitates massive amounts of data processing, which is where big data comes into the picture.

By leveraging big data, companies can have a more targeted and relevant customer experience worldwide. Big data analytics makes it possible to collect and process massive amounts of data while catering to customers’ specific needs.

 

The demand for big data is skyrocketing, and numerous tools are available in the market today, so all you need is the right approach. Big data will revolutionize business, and business will revolutionize society, bringing positive outcomes in every possible situation.

Big Data Analytics: A Complete Overview and its Role in the Business Organization

Introduction

The amount of complex and massive data generated from various sources is mind-blowing. According to a recent study by Gartner, by 2025, 70% of organizations will shift their focus from big data to small and comprehensive data. As per forbes.com, around 2.5 quintillion bytes of data are created daily via the internet.

 

So the demand to examine bulk data is rising, and here is where big data analytics comes into play. It is an analytic technology that plays a crucial role in providing helpful insight from large data sets.

 

Even small-sized businesses can benefit from it to improve their decision-making capabilities and operations. So let us deep dive to know more about big data analytics.

What is Big Data Analytics, and What are its Types?

Big data analytics examines large, complex, unstructured data sets to determine trends and patterns. Enterprises use it to improve business decisions, identify opportunities and potential risks, and track and monitor the performance of an organization.

 

This analytic technology is applied to any data set but is mainly used with data that is too large or too complex to be analyzed using traditional methods. The data can come from various sources, such as social media, sensors, transactions, and clickstreams.

 

Big data analytics uses unique tools and business intelligence software to analyze and transform the data. These include – distributed computing, MapReduce, and data mining.

 

There are four types of data analytics under the umbrella of business analytics strategy:

1. Descriptive analysis: This method tracks key performance indicators to understand the current state of a business

2. Predictive analysis: It analyzes trend data to identify the probability of future results

3. Prescriptive analysis: It uses past performance results to generate recommendations on how to deal with similar circumstances in the future

4. Diagnostic analysis: This method identifies the root cause of the problem

The Potential Benefits of Big Data Analytics in Business Organizations

Business organizations can gain a competitive edge and improve their bottom line by harnessing the power of big data. Here are some of the key benefits of big data analytics for businesses:

1. Customer Acquisition and Retention:From an eCommerce perspective, business analytics helps companies identify precisely what customers want. For example, companies like Amazon have leveraged business analytics to identify consumer patterns, and today they have mastered personalized shopping experiences on the internet.

2. Identification of Potential Risks:Companies thrive in high-risk environments with the help of big data analytics for risk management. Businesses can develop risk management solutions using business intelligence software; moreover, it helps them create new strategies for their organizations.

3. Boosts Sales and Increase Revenue : Big data analytics plays a pivotal role in increasing company revenue. Leading industries are now using predictive analysis to create a growth strategy for their business.
For example, Harley Davidson uses predictive analysis to attract leads and get insights into a customer’s product choice. Moreover, they can identify customers who are ready to buy the vehicle. A sales representative contacts these customers and helps them find the most suitable offer. Thus, a customer’s data offers the scope to create personalized products and services to cater to the specific needs of customer segments, that further helps to boost sales and revenue.

4. A Significant Contribution to Digital Marketing Strategy: Big data analysis helps to optimize digital marketing strategy in the following ways:

● Having Accurate and Correct Data :Too much data is problematic, so having the correct data will create an effective campaign. Furthermore, KPIs help companies to build an effective campaign. A data analyst uses tools like Tableau KPI Dashboards to check the campaign’s or a product’s performance.
Some KPIs for marketing initiatives include landing page visitors, registrations, click-to-calls, and email collections.

● Targeting Right Keywords:Using the right keywords in your websites or posts will drive traffic to your channels and improve SEO. Top analytics tools like google analytics, google ad keyword planner, and more optimize your websites by targeting exact keywords.

5.Innovative Products: Big data analytics tools dig deep into the vast dataset to extract valuable insights. The analysis transforms actionable business strategies and decisions. The insights help to find radical ways to tweak business strategies, develop new products/services, optimize customer service, and improve employee productivity. Moreover, they are the key to product innovation.

The Challenges of Big Data Analytics

● Expensive – For one, big data analytics can be costly. Companies must invest in the hardware and software to store and analyze large data sets. They also need to hire data scientists and other experts to help them make sense of all the data.

● Time-consuming – It can take a while to collect and process all the data and even longer to analyze it and draw conclusions. This process, in effect, can delay decision-making and make it difficult to respond quickly to changes or opportunities.

Final Thoughts

Despite a few challenges, big data analytics can be a powerful tool for businesses with the right approach. When used correctly, it can give businesses a competitive edge and help them make better and futuristic decisions. All you need is the right people on your team who know how to use those tools and techniques. And if you do not have in-house big data talents, you can always reach out to a proven expert data analytics services provider.

How is Big Data Analytics Used In Healthcare to Improve Patient Outcomes?

Introduction

With the rapid use of big data in the medical field, the global healthcare big data analytics market is expected to grow at a CAGR of 9.12 percent by 2028 to reach $ 59.10 billion.

 

One of the primary reasons for the rise of this market is the increased adoption of big data solutions in clinical research. The traditional methods used for clinical research are time-consuming and often do not deliver accurate results. Big data provides more accurate and faster results.

 

Let’s see how big data analytics is used in healthcare to improve patient outcomes.

How big are data analytics used in healthcare?

The healthcare industry leverages big data for various purposes, such as identifying trends, optimizing processes, and making predictions.

 

Big data analytics in healthcare helps achieve predictive modeling, improve the accuracy of diagnosis, develop more effective treatments and predict which patients are at risk of certain conditions. Each of these applications of big data analytics in healthcare has the potential to improve patient outcomes. For example:

● Predictive modeling: Predictive modeling examines patients who are at risk for certain conditions and diseases. The healthcare industry can use this information to develop targeted interventions and therapies to prevent or delay the onset of the predicted conditions.
For Example, a study published in the journal Nature found that predictive modeling could be used to identify patients at risk for developing sepsis (a life-threatening condition caused by infection).
The study found that patients identified as high-risk by the predictive model were more likely to receive early intervention and treatment, which led to improved outcomes.

● Improving the accuracy of diagnosis: One of the critical benefits of big data in healthcare is how it improves the accuracy of diagnosis. This is because big data analytics can help identify patterns in data that may be indicative of a particular condition. For example, big data analytics uses a machine learning algorithm that can accurately identify skin cancer from images with an accuracy of 95%. This is a significant improvement over the accuracy of traditional methods, which is around 85%.

● Developing more effective treatments: By analyzing large data sets, healthcare organizations can identify patterns and trends that may be otherwise hidden. They can then use this information to develop more effective treatments and interventions.
McKinsey estimates that big data science may save over $300 billion annually in healthcare in the United States by removing waste, decreasing redundancy, and enhancing results.
For example, by analyzing patient records, researchers could identify a previously unknown link between certain types of cancer and obesity. This discovery could lead to new and more effective treatments for these types of cancer.

● Improving the efficiency of healthcare delivery: Big data analytics can help reduce healthcare costs, improve the efficiency of your operations and administrative tasks, and reduce the time spent on clinical activities.
For example, healthcare providers can use big data to streamline processes at every stage of care – from registration through discharge planning – to improve patient outcomes while reducing costs associated with each step along the path, such as unnecessary tests.

● Reducing readmission rates: Readmission is a patient’s return after being discharged from an inpatient hospital or nursing home due to their illness or condition worsening or not improving as expected post-treatment. It may also include patients returning within 30 days of discharge due to complications related to their original illness, such as pneumonia or heart failure, which could have been prevented had they known about earlier treatment options available during their time at home (such as telemedicine).
One way big data can help reduce readmissions is by providing insights into patients at risk of being readmitted. By analyzing patient data, such as demographics, comorbidities, and previous hospitalizations, hospitals can develop risk scores predicting patients more likely to be readmitted. These risk scores can target high-risk patients with interventions designed to prevent them from being readmitted.

● Predicting and preventing disease outbreaks: By analyzing data sets that include information about patients, their symptoms, and the circumstances under which they became ill, it is possible to develop models that can predict when and where disease outbreaks are likely to occur. This information can then facilitate the development of interventions to prevent or mitigate disease outbreaks.
Several initiatives are currently using big data to predict and prevent disease outbreaks. One such initiative is the Global Health Security Agenda, a partnership of countries, international organizations, and private sector entities working to strengthen global health security.

● Mobilizing hospital care through wearables: Wearable devices are already being developed to track blood pressure, heart rate, and movement to ensure patients get better and have improved recovery times. This means doctors, nurses, and other medical staff can identify any deterioration earlier and take steps to prevent health problems from worsening.

Conclusion

Big data analytics fulfills all the promises of the healthcare industry. By providing insights that lead to improved patient care, reduced costs, and increased efficiency, big data analytics is poised to impact the future of healthcare significantly.

How does Big Data revolutionize the Banking Sector and Finance?

Introduction

Big Data is no longer exclusive to the world of technology. It is becoming more prevalent in various corporate sectors. It uses cutting-edge statistical and mathematical models like data mining, artificial intelligence, and predictive analysis to develop new insights that lead to better and faster business decisions.

 

As the name suggests, it is a term used to describe a sizable collection of data whose size and volume are beyond those that can be collected, managed, and analyzed by conventional databases with little trouble.

In light of this, one can ask – Do banks and financial organizations generate a sizable amount of data? The answer is yes.

Microsoft, Amazon, eBay, Baidu, Apple, Facebook, and Tencent, have massive databases that undoubtedly provide them an advantage over rivals. Some of these businesses already offer their clients financial services, including asset management, payments, and lending activities, in addition to their primary business operations.

 

The banking sector has made headway thanks to big data analytics. The technology offers perception into the numerous intricate aspects of a person’s life, including their way of life, desires, and preferences.

 

The process makes it simple for banks to adapt their services to the needs of each customer. Using big data in banking, one can monitor their clients’ transactional behavior continuously and in real time.

 

With the aid of data developers, banks and other companies can streamline their internal processes for greater effectiveness. They can provide better services and recommend ways to optimize their customers’ usage.

Impact of Big Data in the Banking Sector

With the modernization of the banking sector, Big Data has played a revolutionary role in the following ways that were impossible with conventional methods.

● Trends in customer spending patterns: Big data analytics in the banking industry is used to gather the data they need to enhance services and satisfy customer needs. A thorough analysis of transactional data enables banks to predict customers’ purchase patterns, making understanding the suitable financial product for each customer easier.

● Segment customers based on their profiles: Big data technology allows banks to profile their customers and categorize them into link-minded groups. It serves as the best way for banks to focus on their marketing efforts by understanding the most profitable and least profitable customers, intending or not intending to purchase the bank’s financial products.
The technology enables banks to promote ancillary goods and entice customers with unique offers. Additionally, the banks can estimate clients’ monthly income and expenses and provide tailored solutions to guarantee a profit.

● Sentiment And Feedback Assessment: System improvement, the discovery of flaws, and the equitable distribution of work are essential to improve bank operations. Analysis results will be erroneous if sentiment or feedback analysis is performed inaccurately. As a result, the system as a whole will be incorrect because the pattern recognized will also be incorrect.
Banks can offer a robust strategy for analyzing customer behavior and responses by using big data analytics in banking industry. Therefore, feedback or sentiment analysis is essential to identify and understand prospective growth opportunities. This type of analysis can help further reveal shortcomings in service delivery.

● Fraud Risk Mapping and Data Management: The banking and finance sector’s primary aim is to reduce fraud and cybercrime. Big data technology identifies irregular and illegal activities to detect and stop fraud. This specific pattern of understanding the reasons for cybercrimes and tracing the perpetrators demonstrates the value of technology, thus ensuring safety and security in the banking sector.
Banks can identify problems more efficiently by becoming aware of customers’ frequent financial behavior patterns. For example, the bank will get intimated immediately if a third party or con artist uses any specific customer’s card to withdraw money from the bank account. In that case, the bank will inform the customer about such activity.

Case Studies

● Singapore UOB: The third-largest bank in Southeast Asia, The United Overseas Bank (UOB) Limited, has used big data to manage risk, which is the main area of concern for any financial business. Keeping this in mind, UOB decided to take a chance using a Big Data-based risk management solution.
Risk value estimation is a laborious process that might take up to 20 hours to complete. UOB was now able to complete the same operation quickly by using a big Data risk management system in real-time.

● Danske Bank: One of the biggest bank in Denmark is Danske Bank, which has a customer base of more than 5 million. The bank was having trouble managing up to 1200 false positives each day. This was due to its fraud detection methods having a relatively low percentage, or barely a 40% fraud detection rate. They found this to be an alarming rate, necessitating prompt action.
They then made the decision to collaborate with a well-known database and analytics service provider to use cutting-edge Big Data analytics to enhance their fraud detection methods. They immediately noticed some notable outcomes.

Conclusion

https://www.pwc.com/gx/en/industries/financial-services/assets/pwc-global-fintech-report-2019.pdf According to PwC’s Global Fintech Report (2019), Technology leaders have given insight that AI and Big Data will be one of the major drivers of financial services in the upcoming years. Internet of things, 5G, Blockchain, and other technology drivers are also coming up as major changes.

 

Every day, financial organizations uncover new methods to use Big Data analytics in banking, a path of discovery fueled by technological advancement. Machine learning and artificial intelligence (AI) models help to integrate big data and automation to improve data quality management and customer segmentation, lower errors, and make it simpler for banks to create groups and assess product data and customer preferences.

 

Huge volumes of data present countless chances for organizations of all types across a wide range of industries to capitalize on that data, and the banking industry is among those that stand to gain the most. The financial companies gathering information is just as important and lucrative to them as anything else. Banking companies are using their data now that they have realized its value. For them, data acts as a second form of currency.

 

The transformation in internet banking in the sector has been driven by big data analytics. Today, it is a crucial component of the major banks in the world. They can now save millions, which previously appeared unachievable, thanks to big data analytics.

Big Data Analytics in Accounting: Benefits and Use Cases

Introduction

No longer are accountants simply number crunchers. Now they are expected to be able to interpret and make sense of data. This is where big data analytics comes into play.

 

Professionals use big data analytics in accounting to improve financial reporting, detect fraud, and optimize tax compliance. The use of big data analytics in accounting is expected to grow in the coming years as more organizations generate and store large amounts of data.

 

This is why the global big data analytics industry is expected to generate over $68 billion by 2025, up from an estimated $15 billion in 2019. This growing trend shows how important big data has become.

What is big data analytics in accounting?

It is said that data is the new oil and big data analytics is the refinery. The growing volume of data and the need for real-time insights is fuelling the demand for big data analytics across all industries. Businesses are under pressure to make decisions faster and more accurately than ever. Almost all the areas of accounting are affected by big data.

• Auditing can result in more data-driven audits, which will benefit both the client and the auditor. It may also result in more beneficial insights.
• In advisory services, it is used to develop analytical models that support a range of product or operational enhancements, identify problems, and monitor and improve business performance.
• The field of tax offers the chance to evaluate efficiencies more easily, pinpoint possibilities to improve the tax system, and support the evaluation of global expansion.
• In managerial accounting, it helps in risk management and identification.

Thus, the accounting profession has started harnessing the power of big data analytics. Accountants use it to improve financial reporting, identify trends, and benchmark performance.

Benefits of Big Data Analytics in Accounting

In the modern world, accounting has become more complicated than ever before. Big Data analytics and accounting have allowed accounting professionals to provide accurate recording and analysis. Here are five prominent ways data analytics help accounting professionals:

● Help Identify Errors and Discrepancies: Big data analytics help identify errors and discrepancies in financial statements.
For Example, if a company’s financial statements show a decrease in revenue, but its customer data shows an increase in sales, a discrepancy needs to be investigated.

● Identify Trends: Big data analytics can also help identify trends that may not be apparent in the financial statements.
For Example, if a company’s customer data shows a decrease in sales for a particular product, but the financial views do not reflect this trend, it may be an early warning sign of a problem.

● Comprehensive View Of the Company’s Financial Health: Accounting data analytics provides a comprehensive view of a company’s financial health, allowing accountants to make firm decisions about where to cut costs and improve efficiency.
Accountants can use big-data-derived insights for activity-based costing (ABC), which is a cost allocation method that assigns indirect costs, such as – rent, utilities, or administrative expenses – to specific business activities.

● Detect fraud and anomalies early: By analyzing large data sets, businesses can identify fraud and other anomalies much earlier than they could be using traditional methods. Organizations can use big data analytics to supplement forensic accounting investigations by providing additional data sources and helping to identify behavior patterns that may indicate fraud.
For Example, big data analytics could be used to analyze transaction data to identify unusual patterns that might suggest fraudulent activity.
While big data analytics cannot completely replace traditional forensic accounting techniques, it can provide valuable insights that can help uncover hidden fraudulent activities.

● Understand accounting information usability: With accounting data analytics, professionals can understand how accounting information fits into other business areas. This implies using big data analytics software, such as scorecards or dashboards, that show trends over time (like whether or not a client is satisfied with the service).
This can help them identify opportunities for improvement so that they can increase efficiency at a lower cost than traditional methods would require without sacrificing quality control!

● Detect People Inefficiencie:s Last but not least, through analyzing internal operational datasets, the accounting department may detect issues related to processing or people inefficiencies/human errors that were difficult or even impossible using traditional methods like surveys.

As big data analytics evolves, its influence will likely spread beyond imaginable domains. By providing organizations with comprehensive insights into their operations, big data analytics could prove essential in helping them hit their strategic objectives more effectively.

Future Prospect of Big Data and its Emerging Trends

Introduction

The exponential expansion of the world’s data started ten years ago and hasn’t stopped since. The data is gathered online through social networks, web search queries, text messages, and media files.

 

Big data presents an abundance of new growth opportunities in business, including in-depth insights, data-driven decision-making, and automation. The trend of IoT devices and sensors also produces a massive amount of data. They are the main forces behind expanding the worldwide big data market.

 

Big Data connects to trendy terms like machine learning, data science, artificial intelligence, deep learning, etc. These technological trends will continue contributing significantly to big data analytics for enhancing the business models we now have and enabling improvements in research because these sectors demand data.

 

Cloud storage companies like AWS, Microsoft Azure, and Google Cloud will dominate in storing big data as it continues to rise and grow. Cloud platforms make room for big data scaling and effectiveness. Cloud computing distributes computer services over the Internet (“the cloud”), including servers, storage, databases, networking, software, analytics, and intelligence. It provides quicker innovation, adaptable resources, and scale economies.

 

Instead of maintaining their computing infrastructure or data centers, businesses can utilize cloud computing. This enables companies to pay only for their services, avoiding the upfront expenses and complexity of building and maintaining their own IT infrastructure.

 

Cloud computing service companies can gain enormous economies of scale by offering the same services to a wide range of consumers.

Where is the big data trend going?

The significant data trends are changing and taking shape slowly. Industry experts notice the future in big data trends to give a precise idea of where it can grow significantly.

● More and more diverse data drive the growth of edge computing and cloud computing: It makes sense that the industry is discussing edge computing. Edge computing is one of the “breakthrough technologies” that can transform businesses seeking to overcome the past constraints of conventional cloud-based networks. With a CAGR of 12.5%, the IDC expects the global edge computing market to reach $250 billion by 2024.
The natural inflection point for edge computing will occur within the next 12 to 18 months. Businesses will continue to benefit from cloud computing in terms of cost reduction, agility, reliability, and innovation catalyst. Finally, real-world applications where this design can be advantageous are starting to emerge. Today, 91% of our data is created and processed in centralized data centers.
Edge computing is the secret to completing digital transformation and more effective company operations. Edge computing is crucial today because it is a modernization for multinational corporations to increase their operational effectiveness, performance, and data security. Additionally, it will make it easier to automate all fundamental business procedures and provide the “always-on” feature.

● Businesses are advancing the data lake concept: The latest trends in big data show organizations are investing more of their resources in storing this data in various cloud-based and hybrid cloud systems. These systems are designed for all the V’s (volume, value, variety, velocity, and veracity) of big data to deal with the inevitable rise in data generation.
Earlier, businesses were responsible for managing their storage infrastructure, which produced enormous data centers that companies had to oversee, secure, and run. Cloud computing has altered that dynamic.
Organizations can now deal with unlimited amounts of new data and pay for storage and computing power on demand by delegating responsibility to cloud infrastructure providers like AWS, Google, Microsoft, and IBM. This eliminates their need to maintain their substantial and complex infrastructure.

● Demand for machine learning and AI will increase: At this point, big data is unquestionably here to stay. Big data and artificial intelligence (AI) will continue to be in high demand. AI is meaningless without data, yet mastering data is impossible without AI. Therefore, data and AI are melding into a synergistic connection.
A decade ago, it was impossible to get as detailed information about consumer habits, likes and dislikes, activities, and personal preferences as it is now, thanks to the internet.
Insightful data may be added to the big data pool through social media accounts and online profiles, social activity, product reviews, tagged interests, “liked” and shared material, loyalty/rewards apps and programs, and CRM (customer relationship management) systems.
For Example, fulfillment and supply chain operations are heavily dependent on data. Therefore, they are turning to the advancements in Big Data and AI to give them real-time insights into client feedback. Businesses can do this by basing their marketing, financial, and strategic decisions on the flow of new information.

● Data operations and data governance take center stage: Big data trends show organizations are also grappling with data governance, privacy, and security challenges more and more. Enterprises used to be somewhat relaxed about data privacy and governance issues in the past.
The new legislation makes them far more accountable for what happens to personal data stored in their systems. Organizations are becoming more involved in data stewardship and working harder to secure and manage data correctly.

● Data fabric: Across hybrid multi-cloud systems, a data fabric is a powerful architectural framework and collection of data services. They standardize data management procedures, data trends, and uniform capabilities.
Since this solution may reuse and mix various integration techniques, data hub skills, and technologies, more businesses will rely on it as the present business trend toward accelerated data complexity increases.
Additionally, it lessens system complexity by reducing design, deployment, and maintenance times by 30%, 30%, and 70%, respectively. It will be widely used as an IaaS (Infrastructure as a Service) platform for re-architecting by 2026.

Conclusion

The Internet of Things, big data analytics, and cloud computing revolutionize how companies generate value. Companies can obtain the most value out of their data and the most insightful information by using data analytics, which is supported by the growth in computing power.

 

Over the last two years, significant market and sector disruption has altered how firms operate. Employees and customers use data indefinitely. Although the current state of the world has increased uncertainty, it has also given firms more chances to improve their operations to react quickly to changing customer needs, competition movements, and new threats by utilizing cutting-edge data solutions.

Machines Vs. Human Mind: The Power of Intuition in Big Data Era!

Introduction

Artificial intelligence and machine learning have fueled the big data era. Many organizations are increasingly pivoting to big data analytics to improve business-related outcomes and operational efficiency.

 

Big data analytics and artificial intelligence help enterprises make more precise, data-driven conclusions to transform them into actionable insights. Even though the insights are precise, there has been no subjective evaluation of the data. This is where data intuition comes into play.

 

Human creativity, imagination, intuition, implicit knowledge, experience, and skills, which can only be expressed through behavior, play a crucial role in the organization’s overall development. This suggests that businesses that have successfully established a strong position in the market for many years understand the importance of both data-driven analysis and intuition.

Role of Intuition in the Big Data Era

Regardless of how advanced AI, big data, and machine learning have become, organizations do not entirely rely on them. The important thing is to recognize the situations in which big data shouldn’t be considered a priority. More data doesn’t always lead to more profitable decisions. It requires business data intuition to know what questions to ask first.

• Data can only give you an impression of the question. The right question will yield excellent results, while the wrong one can completely undermine the plan
• Data is used to identify market trends, and your gut intuition can ascertain what factors are triggering those trends
• Data enables you to optimize. Organizations can easily transition into a test-and-learn routine in the big data era through big data and machine learning.

However, focusing solely on optimizing current data won’t advance the company. There is a chance that entrepreneurs will pass up significant opportunities. Big data in business examples have certainly demonstrated that neither data nor intuition alone can support a venture’s expansion.

 

When making decisions, data and intuition should be used carefully alongside other assets like market research, past knowledge, and proof-of-concept trials. A wise combination of data and intuition using conventional methods (qualitative research, business sense, etc.) can be an effective tool for alerting decisions and offering a valuable feedback loop. Organizations presently use data to validate and measure the behaviors they have found, in addition to qualitative discovery.

 

So, in this big data era, one has to accept that everything can’t be reduced to a set of data and that business sense, intuition, and gut feeling matter. Data is only one component of the decision-making criteria. Adopting a “data-informed” rather than a “data-driven” mindset motivates successful data products that achieve a long-term goal.

What is Intuition in Business?

Many enterprise decisions are made not just on data and analysis but also using the data intuition or hunch of the decision maker. Speaking about critical business decisions, intuition refers to the use of “gut feelings” supported by logical, rational decision-making models rather than having a complete scientific approach.

Data intuitive decision-making has the following advantages:

 
• A manager can make quick and effective decisions in complex situations based on data intuition or gut feeling.
• Intuition helps identify a long-term objective for the company. Finding dreams that align with an organization’s values and objectives is credible since data intuition is a subconscious component.
• Intuition leads to new possibilities, innovation, and diversity in the enterprise
• Businesses can make more informed decisions when intuition and hunches are used strategically in conjunction with scientific methods.

How to Balance Data and Intuition in Decision-Making?

Human body’s intuition is essentially its big data processing system. Data can amplify your intuition with well-supported and logical data, benefiting both shareholders and employees. Backing it up with factual data and real numbers makes absolute sense.

 

An organization’s data is humanized by intuition. Data is identified and contextualized in ways that are still beyond the abilities of machines. It enables you to think analytically without reaching decision paralysis.

 

For Example, think of a race car driver accelerating to the car’s absolute limit with their foot jammed on the gas. The reading indicates that the engine has overheated. This means the data is advising them to pull over. Being in the final loop, the driver knows that pulling over would result in a loss. Having gone through a similar experience, he is confident that nothing will affect the final few seconds of the race. As a result, the driver won’t stake his victory on the warning indication alone.

 

Data supports intuition, but it’s not the only factor in decision-making. An entrepreneur in the big data era must consider data relating to the company’s objectives, values, strategies, and mission. Market trends can be discovered with data, and your gut analysis or data intuition can find the factors influencing them. Using both factors wisely would certainly produce better results for the company and its employees.

Can Machines Replace the Human Mind?

Applications that use machine learning and artificial intelligence operate more effectively than humans in terms of speed, operational efficiency, and work productivity. Although algorithms have robust advantages in terms of processing data faster, they have the drawback of processing only pre-fed data. This restricts innovation in an organization.

 

On the other hand, human intelligence derives from personal experience and knowledge acquisition; it is independent of pre-fed data. It is, thus, essential to have a multi-criteria and hybrid approach for decision making delivering more reliable results.

 

Here are a few reasons why artificial intelligence (AI) and machines cannot entirely replace the human mind but complement our intelligence. Machines may lack:

● Emotional intelligence: Systems with artificial intelligence are quick, logical, and precise. They lack intuition, empathy, and cultural awareness. And it is because of these skills that the human mind is just as valuable as technology.

● Creativity: Machines and artificial intelligence (AI) built with specific types of codes are not entirely capable of devising innovative and creative solutions to emerging problems.

● Empathy: People still prefer communicating with humans over data science & big data analytics to win their trust and loyalty. The human mind can understand and register various customer behavioral actions, such as pleasure, sorrow, happiness, hope, gratitude, optimism, kindness, and others.

Final Thoughts

In this big data era, while the use of artificial intelligence will make life easier for humans and may even motivate humans to evolve their skill sets, it may never completely replace human resources.

 

The use of this technology will always be directed, run, and controlled by the human mind. Despite the existence of machines, human interaction and input will never be obsolete.

 

Combining data intuition and data-driven solutions will always produce the best organizational results. Therefore, the value humans provide is hard to be replaced by machines, computers, or other technological devices.