Data analytics is the computational analysis of data, statistics, or other forms of information to extract knowledge, patterns of behavior or other forms of actionable insight.

Through data analytics, a number of insights can be gained. Some examples include, but are not limited to:

  • Noticing particular times and days where sales spike or crater.
  • Uncovering unusual network activity, which could be an indication of hackers.
  • Identifying applications that use an inordinate amount of system resources.
  •  Noticing changes in customer/consumer traffic due to external influences.

Data analytics is all about better decision making. This complex process is performed primarily by data analysts and data scientists, but in some cases and with the right tools it can be done by non-tech staff as well. The process often starts with raw data, which is data mined, seeking valuable insight – indeed, competitive advantage is the goal of business data analytics.

The Importance of Data Analytics

As digital transformation has gained adoption, the practice of data analytics has skyrocketed. No matter what industry you work in, data analytics likely plays a key role in crafting your strategy. Many companies now have data analysts using data mining techniques on raw data – seeking the many actionable insights gained from this process.

In response, the market for data analytics software has climbed rapidly. Companies are hiring data scientists and data analysts at a steady clip. The mantra now is “data-driven decision making.”

And it’s worth noting that investment in data analytics did not drop off even in the darkest days of the global coronavirus pandemic. “Unlike many other areas of the IT services market, big data and analytics services continued to grow in 2020 as organizations relied on data insights and intelligent automation solutions to survive the COVID-19 pandemic,” said Jennifer Hamel, a research manager at IDC. “The next phase of digital resiliency will spur increased investment in services to address both lingering and new challenges related to enterprise intelligence initiatives.”

These growing importance of data analytics encompasses a wide range of activities that are common in modern enterprises. For example, data analytics can include many of the following:

  • Data mining
  • Text analytics
  • Data visualization
  • Business intelligence
  • Data Catalogs
  • Data warehouses
  • Data lakes
  • Data fabric
  • Data modeling
  • Artificial intelligence (AI)
  • Machine learning (ML)
  • Deep learning

In addition, a wide range of disciplines make use of data analytics and assorted big data trends, from finance to accounting to product management to manufacturing. And an array of related actions and technologies play a role, including data visualization and relational databases, often using large datasets.

Data analytics is integral to research and development, engineering, and strategic planning. And of course it is the very heart of logistics and supply chain management. With every year, analytics plays a larger role in information technology and cybersecurity. In sum, there is hardly an industry that isn’t driven by data analytics.

Today, many organizations have a chief data officer whose job it is to oversee all aspects of data management within the organization, including data analytics and data science.

Types of Data Analytics

Not all data analytics are the same. Most experts divide data analytics into four key types, including descriptive, diagnostic, predictive and prescriptive.

Descriptive analytics describes what happened in the past or what is currently happening. This type of analytics answers questions like who, what, where, when and how. For example, a sales report that shows your monthly sales over the past four quarters is an example of descriptive analytics. This is the easiest type of analysis to perform, but it has only limited value to the organization. You can’t leave it out, however, because descriptive analytics is a necessary foundation for the more advanced types of analytics.

Diagnostic analytics tells you why something happened. For example, if your descriptive analytics informed you that sales dropped last quarter, diagnostic analytics would help you figure out what went wrong. This type of analytics usually involves combining multiple data sets to create a more full and accurate assessment of your situation. Maybe your sales drop happened because of supply chain problems or bad weather or because you lost a key account after hiring a new salesperson. Diagnostic analytics can help you figure that out.

Predictive analytics helps you understand what is likely to happen next. It takes a look at historical trends, looking for patterns that will offer insights into the future. Often predictive analytics tools rely on advanced data models and machine learning technology that can distill the important factors that impacted past performance and apply those to the current situation. This is a much more advanced and speculative form of analytics with a high potential value. It is becoming a very common tool, particularly for large enterprises.

Prescriptive analytics attempts to tell you what you should do about what is likely to happen in the future. For example, if your predictive analytics forecasts lower sales for next quarter, prescriptive analytics can help you see how that might change if you lower your prices or change your marketing strategy or source product from a different supplier. Obviously, the potential benefit with prescriptive analytics is extremely high, but it is also very difficult to do prescriptive analytics well. Currently few organizations have the resources and capabilities to do prescriptive analytics at scale.

Most organizations start their data analytics journey with descriptive analytics. Over time, they expand into diagnostic analytics, then predictive analytics. Many aspire to eventually have a successful prescriptive analytics program to better inform their business decision-making.

Most experts agree that data analytics is tremendously important for modern organizations because it helps them become more competitive. Organizations undertake data analytics – using using data analysts – for a large number of reasons. Some of the most common things you can do with data analytics include the following:

Better Understand your Customers

Most organizations have access to a wide variety of data about their customers, including demographics, order history, customer service interactions, social media, browsing history, survey responses and more. Employing data analysts to analyzing this data can help companies create a fuller picture of each individual customer as well as an aggregate picture of their customers as a whole. In addition, it might highlight opportunities to better meet customer needs or reach new groups of buyers.

Streamline Business Operations

Many of the processes within your organization, from order taking, to fulfillment, to supply chain management, to customer service, to IT operations and more are measurable. And anything you can measure, you can improve. Data analytics can help you track progress towards key performance indicators (KPIs) and help you identify bottlenecks that might be slowing your organization today.

Identify New Opportunities

One of the more interesting areas of data analytics is the discipline of whitespace analytics. This practice helps organizations identify business that they aren’t doing today that they could be doing. It can help you find new customers, new products and new partnerships to pursue that could increase revenue and margins.

Capitalize on Existing Trends

Even the most basic data visualizations make it easy to see which direction KPIs are moving and at what rate. By identifying these trends – often sifting through raw data – you can do more of the things that are working well and attempt to correct things that are heading the wrong way.

Market More Effectively

Marketing is one of the business disciplines that has been most transformed by data analytics. Because so much marketing takes place digitally, marketing teams have a wealth of data available that can help them identify which targets are most likely to become customers, which customers are likely to buy again, which customers are in danger of defecting to a competitor and much more. They often use data visualization to help data mine for business insight.

Improve your Pricing Strategy

What if improving your prices by just 1 percent can increase your organization’s overall margins by as much as 10 percent? Analytics can help you analyze the variables. Data analytics can help pricing teams identify where they should increase prices (and where they should decrease them) in order to maximize profitability.

Make Better Decisions

Humans are always tempted to make decisions for emotional reasons, often based on preconceived notions that may or may not be true. Data analytics provides a strong check to this instinct so that business leaders can see whether their gut reactions are likely to result in success or not. In a very broad sense, data analytics can help businesses improve their decision-making across the entire organization.

Challenges of Data Analytics

Like every technology solution, data analytics has its challenges for anyone who chooses to embrace it. They include lack of trained staff, and difficulties with data visualization.

Lack of Trained Professionals

Analytics, big data, and AI are all emerging technologies that have only begun to really take hold in the last few years. That means that the training and education behind them is also lack even further. Many universities are putting together undergraduate and graduate programs and technical education institutions like CompTIA and Coursera have also made an effort to get people trained. But there is still a shortage of talent, for a variety of reasons, such as cost for education. The shortage is not seen being alleviated anytime soon.

Getting the Decision-Makers Onboard

Business units within an organizations may all be in the same boat but all too often don’t think that way. According to research from Forbes Insights and Cisco, 70% of leaders say a successful analytics strategy hinges on close collaboration between IT and business units, but only 15% of global executives, overall, rate analytics interactions between these two groups as “excellent,” while 39% rate business-IT collaboration as “fair.”

The consequences of this schism is that investments in analytics won’t give people the information they need, while more than a third surveyed consider this shortfall a deterrent to capitalizing on technology innovation. It’s important, then, to get IT and the business units on the same page with a shared vision.

Messy Data

The way data is received/collected today is very different from before. With manual database entry, everything went into a neat row and column. Now you have the advent of unstructured data, the flood of data from the edge, and new sources like social media.

The result is often data that is siloed and not readily available. The data collected has to be processed, which can take considerable time. Putting data together from disparate sources means multiple challenges, from getting it out of the dirt data stores to putting it all together into a single format. Much work has to be done by data scientists to make data just usable, and that’s time spent away from analyzing it.


If you are trying to glean information from a database, your eyes would quickly glaze over staring at rows and columns of numbers and text information. To truly gain knowledge, you have to engage in visualization of the data and display it in forms that reveal information, such as trends, anomalies, and exceptions.

While data visualization tools are incredibly helpful, they are not easy to use. There is however plenty to choose from, from the beginners tool that is Microsoft Excel to a more advanced tool like Tableau. But they all have steep learning curves and will require time and training.

Data Analytics and AI

Data analytics and artificial intelligence have a synergistic relationship. To some degree, they need each other. AI requires a massive amount of data for training and machine learning, but not every bit of data is useful or helpful. You want some method to sift through the data to find relevant, useful data and get rid of data you do not need.

By deploying AI for better data analysis, you can better use the data that you have left and more easily leverage it for advanced analytical capabilities, like predictive analysis. The combination of the two can improve your business in multiple ways:

  • Through the anticipation of emerging market trends, you can get ahead of the curve.
  • By analyzing consumer behavior, you can spot consumer trends as they are happening rather than after.
  • By recognizing customer patterns, you can personalize and optimize digital marketing campaigns for the customer rather than one-size-fits-all.
  • Use big data, AI, and predictive analytics to build intelligent decision support systems.

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