Market and customer insights are critical for business success. However, obtaining those insights has always been difficult. In today's digital age, you require a data analytics solution that combines the best of analytics and data management capabilities to quickly and easily access and analyze the information you require—when and where you require it.
What are the best types of data analytics?
The best type of data analytics for a company depends on its stage of development. Most companies are likely already using some sort of analytics, but it typically only affords insights to make reactive, not proactive, business decisions.
More and more, businesses are adopting sophisticated data analytics solutions with machine learning capabilities to make better business decisions and help determine market trends and opportunities. Organizations that do not start to use data analytics with proactive, future-casting capabilities may find business performance lacking because they lack the ability to uncover hidden patterns and gain other insights.
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Four main types of data analytics
Predictive data analytics
Predictive analytics may be the most commonly used category of data analytics. Businesses use predictive analytics to identify trends, correlations, and causation. The category can be further broken down into predictive modeling and statistical modeling; however, it’s important to know that the two go hand in hand.
For example, an advertising campaign for t-shirts on Facebook could apply predictive analytics to determine how closely the conversion rate correlates with a target audience’s geographic area, income bracket, and interests. From there, predictive modeling could be used to analyze the statistics for two (or more) target audiences and provide possible revenue values for each demographic.
Prescriptive data analytics
Prescriptive analytics is where AI and big data combine to help predict outcomes and identify what actions to take. This category of analytics can be further broken down into optimization and random testing. Using advancements in ML, prescriptive analytics can help answer questions such as “What if we try this?” and “What is the best action?” You can test the correct variables and even suggest new variables that offer a higher chance of generating a positive outcome.
Descriptive data analytics
Descriptive analytics is the backbone of reporting—it’s impossible to have business intelligence (BI) tools and dashboards without it. It addresses basic questions of “how many, when, where, and what.”
Once again, descriptive analytics can be further separated into two categories: ad hoc reporting and canned reports. A canned report is one that has been designed previously and contains information around a given subject. An example of this is a monthly report sent by your ad agency or ad team that details performance metrics on your latest ad efforts.
Ad hoc reports, on the other hand, are designed by you and usually aren’t scheduled. They are generated when there is a need to answer a specific business question. These reports are useful for obtaining more in-depth information about a specific query. An ad hoc report could focus on your corporate social media profile, examining the types of people who’ve liked your page and other industry pages, as well as other engagement and demographic information. Its hyper specificity helps give a more complete picture of your social media audience. Chances are you won’t need to view this type of report a second time (unless there’s a major change to your audience).
Diagnostic data analytics
While not as exciting as predicting the future, analyzing data from the past can serve an important purpose in guiding your business. Diagnostic data analytics is the process of examining data to understand the cause and event or why something happened. Techniques such as drill-down, data discovery, data mining, and correlations are often employed.
Diagnostic data analytics help answer why something occurred. Like the other categories, it too is broken down into two more specific categories: discover and alerts and query and drill-downs. Query and drill-downs are used to get more detail from a report. For example, a sales rep that closed significantly fewer deals one month. A drill-down could show fewer workdays, due to a two-week vacation.
Discover and alerts notify of a potential issue before it occurs, for example, an alert about a lower amount of staff hours, which could result in a decrease in closed deals. You could also use diagnostic data analytics to “discover” information such as the most qualified candidate for a new position at your company.
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