Just as every company has customers with unique needs and interests, there are also different classes of analytics users, each with its own set of needs and objectives.
For instance, a simple dashboard approach may appeal to senior executives who are interested in receiving timely updates on key performance indicators (KPIs). But line of business leaders and data scientists who are in the trenches typically require more industrial-strength capabilities from analytics.
These include the ability to understand the cause and effect behind a performance issue in order to identify the source of a particular business challenge (e.g., a sudden drop in customer satisfaction scores for a specific customer segment) and then be able to act quickly to address that issue. Often, the most effective way to manage is by exception.
It’s important to recognize that there are different types of users of predictive analytics, including line of business experts who frequently make extensive use of analytic capabilities such as “what-if” scenarios, as well as C-level executives who are often interested in obtaining quick snapshots of current business conditions and KPIs.
Simple analytics tools may work for a limited class of users. But let’s face it, simplicity doesn’t always work in every corner of a global enterprise that has different classes of analytics users.
Consider: a new analytics user values simplicity. But as these “new” users evolve into more seasoned practitioners, they demand more from the tools they use.
With this in mind, today we examine the distinctive analytics requirements of “business champions.”
Brian is the business champion for a fictitious company named Zodiak Industries, a discrete manufacturer with multiple operating facilities distributed across North America and Asia Pacific. Brian, an MBA graduate, has worked at Zodiak for the past seven years and is responsible for a department that’s in charge of performing use-case activities.
An extremely busy manager whose own performance is measured by KPIs such as the mean time between failures on the plant floor, Brian tends to manage by exception, relying on his eight-person team to focus on the details.
Brian’s need for his team to focus on details is also one of his top challenges. Brain’s team members have historically relied on Excel spreadsheets and canned business intelligence reports for updates and insights on key operational measurements such as inventory turnover and gross profit per part.
However, these tools don’t offer real-time insights into emerging operational and business trends. Moreover, the team often calls upon Zodiak’s data analysts to create special reports for them, which results in additional lag time.
Brian and his team need access to up-to-date performance data as well as the use of analytics tools that enable them to quickly spot manufacturing performance issues and analyze the root causes to mitigate risk to the enterprise. He also needs his team to have greater confidence in their decisions – decisions that are based on fact and not on gut instinct.
In essence, Brian is looking for analytics tools that can take his team’s performance to the next level.
What he’d truly like is for the team members to become less reactive to performance issues and more proactive so they can forecast operational performance over a six- to 24-month period and make decisions based on the high probabilities of events (e.g., anticipating overnight plant floor maintenance to prevent costly breakdowns).
The right predictive analytics tools can help Brian and his team accomplish this and more. The right tools can help Brian’s team better understand the cause and effect behind different operational performance issues so they can drive positive change.
Clearly, Brian’s needs as a predictive analytics user are far different than the needs of someone who is looking for a simple dashboard view of KPIs. Brian needs robust analytics that enable him to discover critical insights on his own that he can act on quickly.
What we can see from this example is that Brian and his team require a single analytics platform that’s capable of addressing both his needs and theirs. Of course, there are other types of analytics users in the enterprise with their own unique needs.
In our next installment, we’ll focus on the analytical needs of Michelle, a management budget owner for a big box retailer, who’s tasked by the CEO to take back market share within 18 months.
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