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Understanding the 4 Types of Data Analytics

Companies increasingly grapple with how to handle the sheer amount of information they can collect from customers and business partners. Data analytics helps them sort, parse, and convert raw data into key performance indicators (KPIs) and actionable business intelligence. The methods by which we analyze data can be sorted into four core categories. These four cornerstones of data analysis will ultimately aid your leadership team in making more informed, confident decisions about the direction of your business.

In this article, we will teach you the fundamental features of business analytics in a way that removes the mystery and helps you implement it in your operations.

What are the Different Types of Data Analytics?

There are four levels of data analysis:

  • Descriptive
  • Diagnostic
  • Predictive
  • Prescriptive

Descriptive and diagnostic analytics both focus on the past. With descriptive analytics, you look at the raw numbers to figure out what happened. Diagnostic analytics takes you a step further by showing why you achieved those numbers.

For example, a business may track sales numbers, examine the details of, and determine the success or failure of those numbers. That’s the sort of activity businesses have been doing for centuries. Analytics really isn’t that scary — or new!

Predictive analytics focuses on the present in relation to the future. This method is used to set goals today based upon past activity, giving your company an effective benchmark. With prescriptive analytics, your attention is on the future. Companies use all of the information gleaned from the first three branches of business analysis to determine how you can make your predictions come to life.

Some experts think there is now a 5th branch: Cognitive analytics. This field leverages machine learning and artificial intelligence to comb through large quantities of data, aiding computers as they learn from situations and humans over time. Many people simply think it’s smart to have computers perform prescriptive analytics because they can work through various theoretical models and outcomes with greater efficiency and speed.

How Do the Four Levels of Analytics Work Together?

EAG 1Source uses the Gartner Maturity Model to explain the relationship between the four different types of business analytics to our clients.

Gartner Maturity Model

The graph above reflects the logical evolution of data analytics over time. As your organization’s data and collection methods mature, you can begin to predict, prescribe, and apply cognitive applications to your business strategy.

The mutual support between each style of analysis is baked into how companies use their data.

  • Most companies start with descriptive analytics, as you must first look into the past to learn what happened and when.
  • Armed with that context, you use diagnostic analytics to determine why it happened and whether your efforts were successes or failures.
  • It’s then that predictive analytics steps in to help you calculate what to do about it as you prepare goals for the future that either replicate, exceed, or avoid earlier events.
  • Eventually, prescriptive analytics enters the game to deliver specific steps you can take to create the future you want.

Which Types of Data Analysis Matter Most to IT Outsourcing? How are They Applied?

Most day-to-day data visualizations and traditional financial reporting fit into the descriptive and diagnostics categories. For example, balance sheets and income statements look at what happened financially for the company in the past.

Common questions that descriptive data visualizations answer include:

  • What did we make in profit in the last quarter?
  • How many units did we sell?
  • When, where, and how did we make those sales?

From there, KPI dashboards are geared toward diagnostic analytics. Your company must dig into the numbers to see why something happened. For example:

  • Oil revenues had a steep drop in the first quarter of 2020.
  • You look behind that number to see that the oil price dropped and when in relation to the reduced revenues.
  • Combined with socioeconomic events, you can determine that’s why your oil revenues dropped.

Once this initial analysis describes and diagnoses the past, we can then apply models to forecast and help predict what may happen in the future if current trends continue. This is predictive analytics at play, and it’s becoming more essential to both IT outsourcing companies and the businesses receiving those services. With the advent of machine learning and other advanced computational tools, we can set the stage for prescriptive analytics. This helps clients make more informed decisions about the direction of their business and industry.

How Can Oil and Gas Companies Use the 4 Types of Data Analytics Effectively?

For any company wanting to pursue data analytics and data science, the Gartner maturity model is a great start. Analytics is maturity-based, so developing your company’s analytical capabilities requires time, effort, and intent if you want to take logical steps along that curve. It is also helpful to note that some departments and businesses may not need prescriptive analytics because it doesn’t align with their business needs.

For example:

  • The accounting team is most concerned with descriptive analytics – understanding what happened in the past so they can update the balance sheets.
  • Reservoir engineers implement predictive and prescriptive analytics to best determine where and which wells will be the most profitable.
  • C-Suite level individuals often prefer diagnostic and predictive analytics to understand what is causing losses or gains in profit and to have more accurate budgets.

Again, this sort of work really isn’t new. The difference is that contemporary data analytics has greater technology on its side. Instead of paying people to manually run the numbers and make educated guesses, business leaders can now use machine learning and artificial intelligence to process data and make predictions faster than humans.

Ultimately, EAG 1Source believes that we should demystify the term “data analytics” so that we talk about these concepts with more clarity and less dependence upon buzzwords. Boiled down to its essence, business data analytics means looking at all the information from your past, present, and future transactions so you can confidently make decisions that give you the best competitive advantage.