What is Analytics?

Analytics is the systematic computational analysis of data or statistics. It is used to discover, interpret, and communicate meaningful patterns in the data.

It also entails applying data patterns toward effective decision-making.

What Is Data Analytics?

Data analytics is the science of analyzing raw data to make conclusions about that information.

Data analytics help a business optimize its performance, perform more efficiently, maximize profit, or make strategically-guided decisions.

The techniques and processes of data analytics have been automated into mechanical methods and algorithms that work over raw data.

Data analytics relies on a variety of software tools ranging from spreadsheets, data visualization, reporting tools, data mining programs, or open-source languages for the greatest data manipulation.

Types of Analytics?

Analytics are traditionally broken down into the following categories:

Descriptive Analytics — Descriptive analytics answers the question, “What happened?”. This type of analytics is by far the most commonly used by customers, providing reporting and analysis centered on past events. It helps companies understand things such as:

• How much did we sell as a company?
• What was our overall productivity?
• How many customers churned in the last quarter?

Descriptive analytics is used to understand the overall performance at an aggregate level and is by far the easiest place for a company to start as data tends to be readily available to build reports and applications.

Diagnostic Analytics — Diagnostic analytics, just like descriptive analytics, uses historical data to answer a question. But instead of focusing on “the what”, diagnostic analytics addresses the critical question of why an occurrence or anomaly occurred within your data. Diagnostic analytics also happens to be the most overlooked and skipped step within the analytics maturity model. Anecdotally, I see most customers attempting to go from “what happened” to “what will happen” without ever taking the time to address the “why did it happen” step. This type of analytics helps companies answer questions such as:

• Why did our company sales decrease in the previous quarter?
• Why are we seeing an increase in customer churn?
• Why are a specific basket of products vastly outperforming the prior year's sales figures?

Diagnostic analytics tends to be more accessible and fit a wider range of use cases than machine learning/predictive analytics. You might even find that it solves some business problems you earmarked for predictive analytics use cases.

Predictive Analytics — Predictive Analytics is a form of advanced analytics that determines what is likely to happen based on historical data using machine learning. Historical data that comprises the bulk of descriptive and diagnostic analytics is used as the basis for building predictive analytics models. Predictive analytics helps companies address use cases such as:

• Predicting maintenance issues and part breakdown in machines.
• Determining credit risk and identifying potential fraud.
• Predict and avoid customer churn by identifying signs of customer dissatisfaction.

Prescriptive Analytics — Prescriptive analytics is the fourth, and final pillar of modern analytics. Prescriptive analytics pertains to truly guided analytics where your analytics is prescribing or guiding you toward a specific action to take. It is effectively the merging of descriptive and predictive analytics to drive decision-making. Existing scenarios or conditions (think your current fleet of freight trains) and the ramifications of a decision or occurrence (parts breakdown on the freight trains) are applied to create a guided decision or action for the user to take (proactively buying more parts for preventative maintenance).

• Automatic adjustment of product pricing based on anticipated customer demand and external factors.
• Flagging select employees for additional training based on incident reports in the field.

It should be noted that most companies today are still spending most of their time in the descriptive analytics world. That is not necessarily a bad thing. Being able to get the right information in front of a decision-maker, in an easily digestible format, is a talent all within itself.

Components -

Analytics is not a 1 step process. It is a series of steps, often performed in an iterative manner. And just as each business problem is unique, so are the steps to the analytics process used to find the solution.

For me, perhaps the best way to understand analytics is to look at some of the more common tasks performed.

• Data Management: While designing, building, and maintaining databases and data warehouses may not typically fall under the responsibility of an analytics professional, having a general understanding of how they work is nonetheless important. Databases and data warehouses are where most businesses keep their data. If you want to be taken seriously as a data professional, you need to have a fundamental understanding of how data is stored and how to query the stored data.
• Data Modeling: Data modeling is organizing data into logical structures so that it can be understood and manipulated by a machine. As a simple exercise, make a quick spreadsheet for sales amounts for 5 salespeople across 4 quarters. When you are done, look at the table you created. You have just modeled data.
• Data Cleaning: While this may not be the sexiest part of the job, it is the part you will spend the most time on. 60–80% of your time will be spent in this phase of the job.
• Data Mining (Machine Learning): Now this is the cool stuff everyone is talking about. Data mining or machine learning, whichever you prefer to call it, is the Artificial Intelligence (AI) portion of analytics. Data mining is difficult to provide a simple explanation for, but I will try anyway: In traditional programming, the programmer provides explicit instructions to the computer as to how to perform a task. With data mining, data sets are fed through an algorithm. The computer then determines the best way to solve the problem based on the data provided.
• Data Visualization: Data visualization is fun. It is the real show-stopper in the data world. Visualizations make the patterns pop off the page. There are a lot of great programs out there for data visualization. Now Data visualization should rightfully be broken into two separate categories. The first is Exploratory. These are visualizations used by the data professional to help analyze and understand the data. The second is Production. This is the finished product that ends up on reports and dashboards for the business users to see.