The Rise Of Exploratory Analytics

Richard Mooney

This blog is the first in a three-part series about exploratory analytics.

Not long ago, analytics was viewed as a technology for data experts – analysts and data scientists. Data analysts used data discovery tools to help measure and analyze historical business performance. Analysts combed through data, discovering insight about processes and results and sharing it with select company stakeholders.

 

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Data scientists used advanced analytics tools, which utilized complex mathematical techniques to mine data for insights that could be used to improve business operations.

Today, the analytics landscape has changed. Business executives increasingly think of data analysis as a crucial business tool to be employed by users throughout the organization. Several convergent trends have created this perception:exploratory Analytics.2

  • Familiarity with advanced analytics is growing. The widespread reliance on consumer information technology – such as mobile platforms, new media, and gaming systems – has brought digital know-how to the masses. People are increasingly tracking personal metrics such as fitness or food consumption using self-quantifying devices and wearable technologies. And the maturing Internet of Things (IoT) is demonstrating new ways to generate data that can deliver insight.
  • Executives are witnessing a massive increase in data volumes. They want to find ways to use this information to bring a competitive edge to their businesses.
  • Simple, well-designed, BI tools – often delivered through the cloud – have made it easier than ever for executives to retrieve their own insight. With these self-service tools, leaders expect to perform their own analytics.

Digging deeper for insight

In parallel, advanced analytics tools have become easier and more intuitive. For common use cases, data analysts and IT specialists can use advanced analytic techniques to achieve the same high-level results that were once limited to data scientists – with much less investment, time, and effort than ever before. These tools cover the entire end-to-end data mining process, and they allow enterprises to run predictive analytic models in a stable, repeatable, and manageable way. This change represents an “industrialization” of data mining – a shift that has turned traditional analytics from a craft practiced only by data scientists into a defined business process.

But that’s just the beginning. The tools and techniques enabling this democratization of predictive analytics are the foundation for a new generation ofexploratory analytics.3 analytics tools: exploratory analytics. Exploratory analytics takes automated analytics techniques and packages them in a self-service environment, which can be used by executives and business users to see what is driving their business. These tools help business users find key insights faster.

Building an analytics ecosystem

Exploratory analytics will not eliminate the need for traditional analytics, however. To gain maximum insight from data, most businesses will use all three analytics techniques in parallel. How do these tools work together?

Traditional analytics enables the business to measure and understand key trends. It summarizes and reports data in a way that mirrors the structure of the enterprise – by company regions, product lines, or business units. Business users can drill down into the data, slicing and dicing it to reveal new insights. Traditional analytics uses a top-down approach to analysis and helps the business understand what has happened.

Exploratory analytics allows business users to discover patterns in the data in a self-service environment. It uses a bottom-up approach to analysis. The insight is driven from the underlying transactional data and helps the business understand why something happened.

Finally, advanced analytics allows the business to put this information to work to improve operational efficiency and generate ROI. It allows expert data scientists to examine the data in more detail and create more sophisticated models. Advanced analytics manages the deployment of these models into production systems. It helps the business identify what will happen in the future and use that to improve outcomes.

For example, a data discovery tool shows how many units are built in production line. Exploratory analytics might discover that the key factor influencing the production line’s throughput is component availability. Advanced analytics monitors the production system and automatically warns that a shipment is going to be delayed due to insufficient components.

Building on traditional self-service and advanced analytics, exploratory analytics can help companies discover new trends and relationships that lead to significant business value. Business executives are just beginning to realize value from exploratory analytics, but the potential is exciting. What type of new insight would help your business make better decisions?

For more analytics strategies that can enhance your business, see Are You Ready To Take The Leap From Descriptive Analytics To Predictive?


Richard Mooney

About Richard Mooney

Richard is the lead product manager for the Predictive Analytics Product Portfolio including Predictive Analytics, Predictive Analytics Integrator & SAP Cloud Platform predictive services. He has 18 years experience in the software industry starting off in development and transitioning to customer facing roles including Product Management, Sales & Marketing. Richard also spent 2 years working as an innovation expert using techniques like Design Thinking, ROI Analysis and Ideation to drive customer innovation and value.