By The Business Analyst Team
Nearly every organisation in every vertical rely on an understanding of internal and external dynamics to make the hundreds and thousands of decisions that impact their P&L every day. To be successful, decision makers must do everything they can to understand customer preferences, competitor behaviour, internal business functions and supplier/partner dynamics. We rely on these decision makers to gather as much experience and information as they can, make assumptions, finalize decisions, evaluate results and iterate. Those that can do this better than their competitors - at scale - establish market dominance. The rest follow or die off.
However, due to the rapid digitization of our society, business environments and internal companies, we have created a new, invaluable resource for decision making. We have created a mind-boggling amount of data that describes, in unprecedented detail, the exact dynamics we have been trying to track using our own individual, limited views. Yet, to date, far too much of the decision making process has remained unchanged. We simply have not unlocked the ability to effectively leverage the data available to inform the decision making process… yet.
Anyone skeptical of the power of data-driven decision making need to look no further than Amazon.com. For many years, the narrative around the disruptive success of Jeff Bezos’ behemoth has been its’ ability to disintermediate physical store locations. Yet over the years, it’s become clear that this was only the beginning of the story. What Amazon has done so well - leaving others scrambling to catch up - is leveraging customer, competitor, internal operations and supplier data to make decisions at a scale, accuracy and speed unheard of previously.
Yet, the answer for Amazon’s omnichannel competition isn’t to try to simply copy their approach. Many have key advantages over Amazon. Legacy omnichannel retailers are often more focused and closer to their customers and suppliers. And they often have the benefit of years of human-centric decision making that can inuit and anticipate behaviours that simple data analysis can’t match. Yet to parlay these advantages into a sustainable competitive advantage, they must find ways to match their traditional strengths in human analysis with a keen understanding of what their data is telling them.
So it is with the majority of businesses across every sector. The need is not to replace your current decision making processes with data-driven automation. It’s to equip your decision makers with a deep understanding of what their data is telling therm in regards to optimizing performance. They may already be making the right decisions, but by not having the data’s perspective, they carry a significant (and avoidable) risk in the equation. Today’s leaders have a fiduciary responsibility to mitigate this decision risk by bringing data and data analysis into the decision making process - at scale - across the organization.
If this is so, and most would agree that it is, what is keeping us from executing on this vision right now? In fact, there are three major sources of friction slowing progress in this direction:
First is data availability. Most often, the data needed to support decision making exists, it just exists in ways that make it hard for decision makers to access in a timely fashion.
Second is the ability to analyse that data. Most data sets contain tens and hundreds - sometimes thousands - of variables. Sorting through this complexity to quickly understand how to optimize all of these variables is not an easy thing for the human mind. And far too often the tools available take years of special training. We simply can’t turn all of our decision makers into data scientists overnight - and we wouldn’t want to even if we could.
Finally, the biggest barrier is simply a resistance to change. Even overcoming the data and analysis challenges, it’s not easy for organizations to transform the way they work. It takes clear executive leadership to transform the way an organization works to optimize decision making - but the opportunity held by doing so is profound.
The story of MondoBrain is completely tied to our ability to help overcome the above barriers to optimize decision making at scale. The company provides an entire toolset and services focused on bringing together multiple sources of internal and external data to deliver the right data to the right decision makers. Furthermore the company’s AI-driven dashboards enable business users to easily monitor, explore and analyze their data in real time - without the need of data scientists. Finally MondoBrain is able to partner with strong executives to lead an enterprise rollout that touches nearly every function and business unit.
In many ways, bringing data into the decision making process is about validating your people’s decision to remove the risk associated with the current over-reliance on human assessments and inertia. Yet more often, the added data perspective will enable your teams to refine and optimize their current approaches to drive incremental successes that will accumulate into significant gains. However, in every organization there are numerous opportunities for “aha!” moments. Areas where teams are actively seeking answers. And other areas that challenge conventional ways of thinking to create fundamental leaps forward.
As you work to plan out a phased implementation plan for your organization, it’s helpful to clearly communicate the strategy of human + data-driven intelligence. And to set the expectations around the three levels of value: reducing risk, refining decisions for incremental improvements, and discovering solutions to big problems while opening new opportunities.
The point is to give your data a voice in the decision-making processes that drive value and determine the success of your organization. It may agree with you. It may point you in new and powerful directions. But you simply can’t afford to let it remain silent.