Business and production systems have become much more adept at collecting complex data. Equipment collects a variety of sensor and parametric data. All kinds of information on buying habits and consumer preferences is tracked for potential exploration. The level of detail in such data cannot be analyzed and understood with static, conventional reporting. Instead, business analysts, engineers and scientists can unlock insights by leveraging interactive and visual analytical software, such as JMP.
Characteristics of New Analytics
New analytical software has brought to the forefront a new world of analytics, which is characterized by these important attributes:
- Support for data that is “self-provisioned.” This means that users are able to get the data they need without assistance and without delay.
- Analytics are visual and interactive.
- Users can now conduct advanced analytics without a Ph.D. in statistics.
- Analysts conduct their work in the moment. Insights often illuminate questions that analysts can explore in the moment, thereby creating an active dynamic that leads to further discovery.
- Analytical thinking is completely attuned to business thinking.
- Analytics are inferential, more than descriptive.
Example of Analytics at Work in the Insurance Industry
JMP partition analysis initially shows overall conversion rate is about 12.5%
Consider the following example scenario for one of our clients in the insurance industry. Demographic information from many thousands of current and potential customers was collected and maintained in a database. The insurance company was able to export the data to a spreadsheet and summarize the data, but did its staff retrieve the best data possible to maximize potential insights? It took several days to retrieve, transform, and format the data to answer even the simplest questions. With the help of integrated, interactive and visual analytics, insights were revealed in seconds.
In considering prospective customers, the big questions were how many of them were converted to new business and what were the factors that drove the conversion? By measuring these factors, the strategic focus was placed on business practices that would lead to higher rates of success.
To conduct our analysis of the data, we used only a few clicks to load tens of thousands of prospective customer records, which included demographic information such as income, education, age, marital status, etc., into a JMP data table. You can see from the image above that overall about 12.5% of these prospects (represented by the blue segment of the graph) were converted into paying customers.
The Aha Moment
Next, the key question was which factors determined success in winning new business? One additional click on the Split button of the JMP partition platform (as shown in the lower left of the image below) and we experienced an aha moment.
After 1 click in JMP’s partition platform, there was a demographic that was an easy win. This insight was hidden in the data prior to this analysis.
The adjacent chart shows that a particular factor called “Xn” (thus named for confidentiality reasons), which led to an incredibly high conversion rate (about 90% as seen in the blue segment of the graph) for a considerable number of prospects. The remaining prospects without the factor “Xn” had little chance of succeeding. The analysts were amazed at this discovery. This insight had eluded our client because the overall conversion rate was masking a major distinction, identified by factor “Xn,” among prospective customers.
Keep in mind that these analysts spend day in and day out pouring over such data, but this important insight, and others that were later uncovered, required the deft use of the right analytics and in right amount.
Analytical Insights Triggered Essential Questions
This insight triggered a number of essential questions. First, were important changes in order to be made to instructions for sales representatives? Second, why was the conversion rate for other prospective customers so low?
These questions led to more questions about pricing, packaging and other factors in combination with demographics, which were to be investigated with designed experiments. Not only were the analysts impressed with the discovery, but also they were excited about how readily it was derived.
Looking back at the 6 attributes of new analytics above, we can see the importance of how they can help clients to accelerate their hands-on ability to use analytics effectively to boost productivity in their operational processes.
IT and the New Analytics
What does it take to bring the new world of analytics into your organization and support a culture of analytics? What kind of role does IT play? IT no longer needs to be concerned about actively conducting analytics. It’s best left to the analysts to maximize business value in this endeavor. Instead, IT now has a supporting role in promoting the culture of analytics. This is achieved by:
- Maintaining the hardware and software infrastructure that supports operational and analytical needs.
- Making data available and ready for analytics in an user-friendly way so that data may be self-provisioned. We do considerable work in this area to ensure that analytical data demands do not adversely affect operations. For example, in pharmaceutical, semiconductor, solar and other industries, unimpeded, real-time data must be collected for traceability. Analytical demand on IT infrastructure must not affect operational systems.
- Supporting companies such as Predictum in developing integrated analytical applications that further facilitate the analysis, storage, and transfer of knowledge and insights to make competitive gains in productivity and cost savings in areas of operations, research and compliance.
- Securing all systems and networks to safeguard data integrity.
Network security is a rapidly growing and increasingly demanding responsibility for IT across the enterprise. Without having to shift their focus away from data security, IT can leave it to the analysts to carry out the initiatives in analytics through self-provisioning.
Extending Analytical Opportunities with the Internet of Things
With the Internet of Things, new equipment with more sophisticated capabilities and the Internet’s expanding reach, we can expect an exponential increase in the amount of data that companies generate and maintain in the future. It’s best to prepare for future business opportunities and challenges by building a culture of analytics now. This is achieved through designing the right data architecture, promoting the use of JMP software, and enabling business analysts, scientists, and engineers to advance their subject matter expertise with analytics.
Are you interested in exploring the potential insights that your data may be masking? Get in touch with us to start a conversation on how to put your data to work for improved business performance.
Wayne J. Levin is President and CEO of Predictum Inc.