Data analytics is no longer the preserve of innovative tech companies or large multinationals; it is becoming a tool that could play an essential part in every organization. However, this powerful medium, which can offer great insights and guide business decisions, requires some thought and attention.
In order to successfully implement a data analytics function, you need to combine the benefits of machines with human ingenuity and skill. Mind-only analytics, where humans do all of the work, is recognized as being too slow and costly. On the other hand, machine-only analytics rarely delivers the insights needed. Goals are achieved only when mind+machine come together with structure and purpose to solve a problem. And therein lies the challenge!
While everyone is talking about artificial intelligence, implementing data analytics in your business is slightly more complex than installing a new coffee machine; it requires a harmonious interaction of humans and machines. As a leader, it is your responsibility to create the necessary culture and environment before you can expect your data scientists to churn out value-adding use cases on a regular basis. For those who are not familiar with the term, here is a quick definition:
A use case is the end-to-end analytics support solution applied once or repeatedly to a single business issue faced by an end user or homogeneous group of end users who need to make decisions, take actions, or deliver a product or service on time based on the insights delivered.
Use cases can be very simple or incredibly complex. I have seen a use case that was based on a mere 800 bits and others, in the realm of Big Data that need hundreds of gigabytes worth of data. An example of a use case could be developing a solution that analyzes customer data in order to improve the understanding of customer churn and thereby inform the development of a new customer retention strategy.
Allow me to point out two key factors that will directly influence the success of your data analytics use cases.
The Psychology of Analytics
It is a common misconception that data analytics is a rational discipline. Similar to sciences such as mathematics or physics, people often have strong emotions about data analytics. Some instantly see the benefits, while others shy away from it on principle. It is a challenge to bring both groups together and have them overcome their differing mindsets and personality profiles. Collaborating on data analytics use cases is incredibly important, considering that the data you want to analyze will come from different business functions (HR, marketing, operations, etc.) and the use cases will feed insights back into different business functions. Your data analytics team cannot exist in isolation.
If you already foresee these problems or want to make sure you avoid them, I recommend the Myers-Briggs Type Indicator test. It is a robust HR framework that will help you and your employees understand personality types. Being aware of your own personality profile and that of those around you will raise awareness of communication requirements. Everybody needs to understand and be understood. Using this framework can help foster better communication. While it is crucial for data analytics, it will most probably also have a positive effect on other parts of your business.
The Use Case Methodology
We believe there are around one billion analytics use cases out there – one billion different mind+machine applications that can help businesses optimize their work and generate value-adding insights! Of course, not all of them will be applicable to every organization, but every organization should have a portfolio of use cases that work for them and provide positive ROI. Often this portfolio will be made up of use cases using a variety of different types of mind+machine applications. Over the past few years, I have seen that systematic application of a standard Use Case Methodology throughout any organization simplifies matters dramatically, increases transparency, and improves ROI. The Use Case Methodology provides a common language that is understood by all parties.
Crucially, the Use Case Methodology is not the only tool for data scientists. As mentioned above, for data analytics to be successful, it needs inputs from around the business. Ensuring that the Use Case Methodology is generally understood by all involved will enable conversations about potential data analytics use cases without resistance or misunderstanding.
Data analytics can provide a tremendous amount of value to any business, but as a leader you must remember that it needs mind+machine to be successful. While machines will do what you tell them to (provided you have the right technology personnel), humans might need a little more attention. Make sure you create the right culture and enable everyone to have conversations about potential data analytics use cases in their business function. Like all transformation exercises, this might take a while to achieve.
– Marc Vollenweider is co-founder and Chief Strategist of Evalueserve, an industry-influencing global research, analytics, and data management solutions provider. He is the author of MIND+MACHINE: A Decision Model for Optimizing and Implementing Analytics (CLICK HERE to get your copy).
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