When talking about data analytics, defining terms is the best place to begin. Any strategy that adopts an analytics capability without understanding distinctions will not end well.
The first question is: What kind of analytics does your company need? There are four established types, with others emerging as organizations integrate more sophisticated technologies, such as machine learning and artificial intelligence, in their analytics.
According to Harvard Business School, business analytics comprises any technology-enabled process that collects, organizes and analyzes data to drive decision-making, strategy development and implementation, innovation and performance optimization. most straightforward types are:
- Descriptive analytics discovers trends and patterns in historical data to describe what happened or is presently occurring.
- Diagnostic analytics answers the question: “Why did it happen” by integrating datasets — such as sales and demographics — to find connections between variables.
CIO rolls up descriptive and diagnostic analytics into business intelligence (BI), which it describes as “information about the data itself. It’s not trying to do anything beyond telling a story about what the data is saying.”
Moving forward in technical complexity and analytical capability, Harvard continues:
- Predictive analytics can mine massive amounts of historical data and industry trends to deliver forward-looking intelligence that enables decision-makers to accurately forecast short- and long-term planning and resource allocation.
- Prescriptive analytics helps decision-makers answer the question: “What should we do next,” by leveraging machine learning and AI to model possible scenarios, potential responses and likely outcomes.
What BI Platforms Support More Advanced Analytics?
BI tools and software for reporting remain useful, according to CIO, noting that several platforms integrate data analytics to expand BI capability, among them:
- Board integrates BI, predictive analytics and performance metrics to provide finance-focused insights on human resources, supply chain delivery optimization, supplier management and cross- and up-sell analysis.
- MicroStrategy for enterprise BI integration enables cross-platform analytics to produce reports and enhance visualizations.
- Oracle Analytics Cloud leverages machine learning to support human resources leadership, analysts and front-line management with data preparation and predictive analytics.
CIO says BI solutions simplify data management and visualization, which can help leaders understand it. “But how simple that process gets, and how you can visualize the data depends on the tool,” it notes.
What Predictive Analytics Tools Provide Forward-Looking Insights?
CRM describes predictive analytics as helping business leaders “see into the future” using algorithms to deliver a range of possible actions with probabilities of success.
E-commerce platforms use predictive analytics to suggest new purchases to customers based on their buying patterns; financial institutions use it in credit scoring; manufacturers use it for everything from product innovation to supply chain management.
CRM’s top-rated predictive analytics platforms include:
- SAP Analytics Cloud powered by artificial intelligence automates workflows to reveal trends and relationships and supports Natural Language Processing, making it ideal for businesses of all sizes.
- RapidMiner is a license-free predictive analytics tool that provides more than 1,000 pre-developed algorithms for machine learning applications and templates to monitor key metrics.
- Alteryx, a Gartner Magic Quadrant leader in data science and machine learning, advances analytics through a collaborative, intuitive interface that can mine unstructured data for consumer sentiment.
“The insights here go beyond data scientists. They help in all sorts of use cases for business managers and other professionals,” CRM says of its top-ranked predictive analytics tools, noting they integrate modeling with understanding what the models mean.