Data analytics is fundamental for modern businesses, and there are various tools for gathering and analyzing different types of data. From workforce intelligence software to predictive and prescriptive tools, companies can leverage analytics tools for decision-making insights. Here's an overview of the five major kinds of data analytics tools, how they are used, and their merits:
If you want to know the basics of what's happening in the business, you need to invest in descriptive analysis. A descriptive data analytics tool is the backbone of business reporting. It answers the questions of what, when, where, and how much/many. Gaining business intelligence without descriptive analytics tools is virtually impossible. These tools offer valuable information you can use to determine whether or not everything is going well.
Descriptive analytics describes the current situation using raw data from multiple sources. You can quickly determine if something is right or wrong without necessarily identifying the reasons why. Descriptive analytics is essentially the foundation on which other analyses can be made to determine the root cause of specific results. In most situations, you’ll need other analytics tools to use information from descriptive analytics.
Descriptive analytics tools can fall into two categories: canned reporting and ad hoc reporting. A canned reporting tool provides information about a specific subject, such as the performance metrics of your latest ad campaign. An ad hoc reporting tool offers information when there’s a need to answer specific business questions. These reporting tools aren't scheduled. An example is a report about the types of people following your social media pages.
A diagnostic data analytics tool is precisely what the name suggests. It seeks to answer the question of why and can help identify the root cause of specific results. Diagnostic data analysis depends on past data and allows businesses to measure data against historical outcomes. The process yields in-depth insights into specific problems. An example is drill-down analytics indicating fewer workdays from a sales rep resulting in low sales.
Diagnostic data analytics can fall into smaller categories like discover and alerts, query and drill-down, and more. A discover and alert tool identifies and reports a problem and its possible causes. The tool can determine when low staff hours are likely to result in reduced closed deals. You can also leverage discover analytics tools to identify the most qualified talents for a vacant job position.
Query and drill-down analytics seek a more detailed report about a specific situation. The business can use such tools to determine the cause of low staff hours, such as a vacation or sick leave. Diagnostic tools use information gathered from descriptive analytics to explain outcomes. They are fundamental in providing metrics for things like predictive analytics. Businesses also need detailed information to avoid collecting individual data for each issue.
Most data analytics tools are used to predict future outcomes. Predictive data analytics tools are popular as they examine trends, correlations, and causation. These tools seek to answer the question of what's likely to happen and are vital in decision making and strategy formulation. Predictive tools use descriptive and diagnostic analytics findings to detect clusters and exceptions. They can forecast future trends and belong to a category of advanced analytics.
Predictive tools are proactive and can fall into predictive modeling or statistical modeling. An example is a business using predictive analysis to discern what happened to another company and correlating the causes and outcomes. As the name implies, these tools seek to predict the outcomes if specific steps are taken and form the basis for prescriptive analysis. The business can use insights from predictive analysis to determine the definitive cause of action.
Predictive data analytics utilize statistical algorithms and machine learning to provide recommendations and answers. Most businesses see predictive analysis as the most crucial form of data analysis pertaining to what might happen in the future. HR teams can leverage such tools to refine recruitment and training strategies and ensure seamless business continuity. There are various other departments and companies that can benefit from predictive analysis.
Prescriptive data analytics is all about recommending the best course of action based on metrics from predictive analytics. The goal is to eliminate future problems and take full advantage of promising trends. A business can also identify opportunities for repeat customer engagement based on sales history.
Prescriptive tools rely on past events and use sophisticated algorithms, artificial intelligence, and machine learning.
Businesses can break down predictive analytics into optimization and random testing. The company can ponder the results of a specific course of action before deployment. Random testing can also test or recommend new variables to gauge the impact on outcomes. A good example is HR teams using workforce intelligence software to predict talent requirements and necessary training before there's a need for such skills.
All data analytics seek to find the best solution for specific business issues. Descriptive, diagnostic, and predictive tools aim to determine what's working, what's wrong, the causes, and what to do for better outcomes. Ultimately, analytics should refine business processes and strategies for better results and profitability. Analysis can also identify the best approaches for achieving business goals and how to elude or minimize risks and unwanted outcomes.
There are fewer cognitive data analytics tools because of the sophistication involved in developing such applications. Cognitive analytics combines intelligent technologies like machine learning algorithms, artificial intelligence, and deep learning. The goal is to apply human brain-like intelligence for specific tasks. Cognitive analytics tools can provide structure for unstructured data and work more like the human brain.
Many industries can benefit from cognitive analytics to tap into unstructured data like emails, images, and social posts. Cognitive approaches are the new way to analyze data and are more effective than traditional approaches. Hospitals can use cognitive analytics to match patients with the best treatment. Businesses can also analyze and structure various data with limited human intervention, streamlining the reporting process.
Data analytics is all about business intelligence. All companies need reliable information at their disposal to make an analysis. At Revelio Labs, we absorb and standardize millions of public employment records to provide the world’s first universal HR database. You can get insights into any company’s workforce composition to make informed decisions based on actual data.