As business intelligence has evolved over time, we’ve watched more people come to understand the connection of data to their day-to-day roles and responsibilities; in parallel, we’ve seen everyone from warehouse workers to marketers benefit from a greater understanding — if not mastery — of data analytics as it relates to their business goals and growth. Some may know just enough to be dangerous, while others build careers in both technical expertise and insights synthesis so that data paints a vivid picture of scenarios and actions to be taken. This is the art of the possible.
How does your company facilitate that process? The clients I work with every day use data like air - often unconsciously - and in order to survive and thrive. A recent Industry Week article I read reinforced the shift of data analytics from a differentiator to a core necessity for business. In a world that demands data centricity, the pressure mounts to make the process clear and the resources readily available for rich data analytics. When building or evolving a data analytics team, whether relying on third party support or internal resources, there are ample things to consider. To make the most of your data analytics capabilities, the following steps are a good starting point as you navigate change management.
Consider a hybrid approach to governance.
A Center of Excellence is often the best approach to create and document best practices, disseminate information and build consistency for data quality. It creates a go-to resource to best understand the data sets and data types available across your organization while enabling experimentation with new technology. Whether testing a new platform or adopting machine learning and AI initiatives — that require much hands-on attention during initial trial and data ingestion — having a centralized location and process for collecting insights and formalizing recommendations for distributed use makes technology adoption more systematized and intentional. When it comes to taking this intel and expanding its impact across the organization, however, dedicated teams within different business units allow for adaptation to meet specialized needs. A hybrid model, then, offers centralized oversight while making possible more widespread use and ownership of the innovations tested and passed on by CoEs.
Create a standalone team or department.
A recent survey done by APQC (American Productivity and Quality Center) showed that nearly one third of companies in the manufacturing sector now have a standalone department for data and analytics teams when it comes to reporting structure. Because data is used across more business functions than ever — and for more than financial forecasting and general business performance evaluation — companies across sectors need a dedicated department to keep data at the helm. In order to better understand everything from audience insights to employee engagement to equipment maintenance, a standalone team offers similar advantages to a CoE by allowing analytics projects to span business-wide priorities, seeking cross-department insights and multifaceted inquiries rather than simply one-off requests. For analytics teams within each business unit, having a direct line reporting structure to a standalone analytics department lead offers deeper contextual knowledge of challenges, time constraints, and subject matter expertise, allowing for improved employee retention and smart use of in-house talent. Companies that only have analytics teams reporting into IT may benefit from evaluating the need and business case to expand their practice, stemming from top-down changes like appointing a Chief Data Officer.
Prioritize behavioral change metrics.
While the first measurement that leadership weighs to demonstrate the success of data analytics teams may be revenue generation or cost savings tied to findings, it is easy to forget the importance of looking at the long-term advantages of data analytics to discover insights and alternative ways of working that require significant creativity and collaboration. The need to balance efficiency and human ingenuity for data analytics to flourish means that basic functions can more easily be automated, but analysis is as valuable as ever — as long as the right people are tapped and the right information is sourced. A data-driven mindset must be carefully cultivated through people within and outside of an analytics team so that information freely flows between data analytics teams and their functional counterparts. Teams like marketing, sales, product and HR — anyone, truly — should feel comfortable and empowered to reach out to data analytics teams with curiosity: whether a hunch, a nagging question or a hyper-focused request. This expectation and business process takes time to develop, and, like a muscle, requires repetition to strengthen. By setting metrics such as utilization and quantity of project or service inquiries to analytics teams, the value-driver becomes the act of teams learning to reach out for data more often, rewarding the essential and early stages of data-driven culture formation as well as the needed ingredients for its continuation.
Feel as if you’re not getting the most out of your data? Let’s talk about the power of data analytics, and what more can be done to get your house of data in order.