Big data analytics begins as a process. One that involves examining large data sets, containing a variety of data types, to uncover hidden patterns, unknown correlations, market trends, customer preferences and other useful business information.
Why do companies pursue Big Data analytics?
- 91% of marketing leaders believe successful brands use customer data to drive business decisions (source: BRITE/NYAMA)
- By increasing the usability of data by just 10%, the average Fortune 100 company could expect an increase of $2 billion dollars (source: InsightSquared)
- $300 billion could be saved if Big Data was used effectively in the US healthcare sector; thereby reducing expenditure by 8% (source: McKinsey)
But there are different types of Big Data analytics. And picking the right one for your organization is critical to delivering the right results. Because what good is the data if you don’t know what to do with it?
Here’s how 4 types of Big Data analytics deliver data-driven results.
- PRESCRIPTIVE ANALYTICS – Seek to determine the best solution or outcome among various choices, given known parameters. For example, in the health care industry, you can better manage the patient population by using prescriptive analytics to measure the number of patients who are clinically obese, then add filters for factors like diabetes and LDL cholesterol levels to determine where to focus treatment. The same prescriptive model can be applied to almost any industry target group or problem. Prescriptive Analytics gives laser focus to answer specific questions. It usually results in recommendations based on rules. This is one of the most valuable types of analytics. Unfortunately, it is largely not used. According to Gartner, only 3% of organizations are using Prescriptive Analytics.
- PREDICTIVE ANALYTICS – Show likely scenarios of what might happen with the goal of delivering multiple options. The deliverables are usually forecasts. For example, some companies use Predictive Analytics for sales lead scoring. They examine the data for the entire sales process, analyzing lead source, number of communications, types of communications, social media, documents, CRM data and, of course, conversions and closed sales. Then, they apply Predictive Analytics to different “what if” scenarios based on different marketing plans that deliver different forecasts. Like Prescriptive Analytics, Predictive Analytics is focused on delivering a data-driven solution. But not just one solution; rather multiple futures options on the decision-maker’s actions.
- DIAGNOSTIC ANALYTICS – Examine past performance to determine what happened and why. The result of the analysis is often an analytic dashboard. Diagnostic Analytics are used for discovery or to determine why something happened. For example, for a social media marketing campaign, you can use Descriptive Analytics to assess the number of posts, mentions, followers, fans, page views, reviews, pins, etc. There can be thousands of online mentions that can be distilled into a single view to see what worked in your past campaigns and what didn’t.
- DESCRIPTIVE ANALYTICS – Highlight what is happening now based on incoming data. To mine the analytics, you typically use a real-time dashboard. An example of Descriptive Analytics would be assessing credit risk; using past financial performance to predict a customer’s likely financial performance. Descriptive Analytics can be useful in the sales cycle. For example, to categorize customers by their likely product preferences and sales cycle. Descriptive Analytics or data mining are at the bottom of the Big Data value chain, but they can be valuable for uncovering patterns that offer insight.
Analytics is all about making the best decisions form the data that we have. Do these types of Big Data analytics help you see how your organizations can make better decisions from data. Is not the time for your company to learn how to use Big Data analytics for data-driven results?
