To learn more about bridging the data and analytics divide to drive meaningful adoption, join Kathleen and other speakers at Beyond 2020, December 9-10. Register for the free virtual event today.
To hear some industry soothsayers tell it, we are heading for a collision, or a fusion, of data and analytics. I can certainly understand why they would come to that conclusion. The two need each other to be effective, and collaboration between them has grown over the years. Vendors who offer both have blurred some of the distinction, muddying the waters for both practitioners and businesses alike.
Bringing Out the Best of Data and Analytics
But while data and analytics are intertwined when it comes to creating business value, the specific roles of each are significantly different. Data is like crude oil – potentially valuable but only in the right hands – and analytics is the process of refining that crude oil and turning it into a valuable commodity. Miners and refiners cooperate, but each does what they do best. Similarly, treating data and analytics as one risks eroding the value of each if roles aren't clearly defined and generalists or hybrid employees are hired instead of specialists who know their roles better and are more capable of playing them.
Data and analytics specialists need to work well together without losing what makes each vital, but more importantly they need to collaborate effectively with the business partners who depend on good data and analytics to support effective decision making. We all know that the best data and/or analytical solution in the world produces no value if it is never adopted by frontline decision makers. Their problems are the ones we must solve. Helping them generate more business value and produce better outcomes should be our only priority.
I have a saying I use to remind analysts of that: There is no such thing as an analytics initiative, only a business initiative. The same is true of data.
Businesses should embrace the differences between data and analytics, then create a shared objective that is larger than either individual function. In the most advanced organizations, that shared objective will be an overarching business problem that requires both data and analytics, not data or analytics. Neither expert can individually meet the needs of the business, but together they can.
Building the Right Business Case
The data-and-analytics work is usually a small part of a much larger project that requires a significant business investment in staff time and energy, strategic leadership, project management and funding. In my experience, there is at least a 3-to-1 ratio of business to technical time and resources.
Usually, an initiative starts with some variation of the question, “Can you help me understand x?” After an exploratory analysis, I typically debunk some myths, present a more holistic understanding of the scenario, and offer a potential analytical solution.
This is the point at which the business then takes the lead in developing a business case. In other words, understanding the potential return on investment of that analytical solution while incorporating their time, effort and energy to implement. If the return is large enough and they have the ability and desire to establish an implementation team, then I can build my solution. Data managers make sure data is available, that it's good quality, and organized in a way that supports initial analysis and ongoing implementation of the analytical solution.
Simultaneously, the business is laying the groundwork for adoption: Who needs to be involved? Who will be affected by the change? Will training be required? If so, who will build it - and who will fund it? Will any risk controls be impacted? Are there regulatory or legal considerations? How will this impact upstream and downstream processes?
The business is always at the center of deployment, and their data and analytics partners are only two of many stakeholders business leaders need to coordinate toward a single business objective. Until we data and analytics professionals recognize that reality and work within it, we’ll continue to set up our work for failure.
The New Team Structure for Success
An even more productive version of this engagement model can be leveraged once credibility and trust are established. Business units are running initiatives all the time, and the best collaborations result in putting data and analytics partners on the core project team even before any specific analytical need has been identified. All three – business, data and analytics – have direct communication with each other, as well as group interactions. Leaders very quickly learn that both data and analytics will always be value-add when incorporated well, and data and analytics partners become key players with permanent seats at the table.
Think of data and analytics as individual wires inside a cable sheathed by the business unit. Because they are bundled, they are more flexible while increasing the strength of the cable as a whole. Melting them down and combining them into a single, thicker wire would decrease their effectiveness.
It's interesting that, in my role as an analytics expert, data professionals will often place me on the “business side” while people in the business will usually place me on the “data side”. It's a subtle indication of the us-vs.-them dynamic that we need to evolve beyond if the partnership is to produce the best results.
Culture change and transformation take time. Data and analytics functions continue to evolve, and the right balance of specialization and collaboration may vary from company to company. But with effort and the right support and direction, companies can create a culture that values data, encourages innovation, empowers individuals, holds them accountable and fosters collaboration that benefits everyone.
I’ll be sharing more about how data, analytics, and business teams can work together to drive valuable outcomes for their organizations at Beyond 2020. Register today to join me!
