There has been and will continue to be a significant shift in how data is leveraged in this continually changing world. Until recently, the data science process of collecting, cleaning, exploring, model building, and model deployment ruled the data management mindset. In a world that is "steady as she goes," this makes a great deal of sense. The amount of data to be curated is growing impressively, but the data science mindset is still on the scene dealing though pressed to its limits with big data. Two things will break this sole reliance on the data science process.
Dynamic Business Scenarios
In a world with operational KPIs staying steady with minor adjustments over time, focusing on data makes sense. That world is virtually gone with the elephant in the room being a pandemics at the moment. Tomorrow it could be natural disasters and the down-stream effects of climate change impacting geopolitical behaviors. There are many business scenario possibilities and combinations headed our way. We can't afford just to explore data and have knee jerk responses.
Monster Data is Lurking
If you think big data is worthy of concern, just think about the monster data just around the corner, driven by higher volumes, more complexity, and even more inaccurate by nature. Organizations are bound and determined to take advantage of behavioral data that is further away from standard core operational data. Monster data includes all kinds of unstructured data that will contain digital footprints worthy of new types of decisions.
Either of these would require a major addition of new data processes, but combined data science processes alone just won't suffice. I am not saying that data science will dim, but it needs some new additional turbocharging and methods that are not just focused on exploring structured and clean data.
Dealing with Changing Scenarios
There are several ways of dealing with scenario planning and practicing responses, but here is what I would encourage organizations to do. Many decisions will drive the data that is leveraged during these efforts.
- · Plan probable scenarios by having executives brainstorm and list likely scenarios and their outcomes.
- · Simulate and practice these likely scenarios, so they become part of the muscle memory of an organization. It will involve leveraging key data sources cascading to tactics and operations. Build communications mechanisms ahead of time and communicate readiness.
- · Identify unlikely dangerous scenarios and simulate the effects and plan responses appropriately.
- · Identify critical decisions, events, and patterns to scour appropriate data resources (owned or not).
- · Identify key leverage points in processes, systems, applications, and the data that could be involved
Dealing with Changing Tactics
Middle management is always trying to optimize outcomes for their functional areas though savvy organizations try to link results to remove friction points for overall optimization. Optimization often leads to self-imposed changing goals that need to be operationalized or tweaked in operations. When executives want different outcomes based on a refined organizational charter, new governance rules, and critical trends delivered by business scenarios in place, a bigger picture is in play. Tactics are the essential glue to hold together operational outcomes guided by goals. As these goals shift in a dynamic set of business demands, managers would be wise to be ready for new guidance coming at faster speeds by following this list of practices.
- · Understand the impact of significant changes by modeling or simulating the effects of change.
- · Be aware of all executive expected sets of scenarios and search for critical events and patterns to detect new scenario emergence.
- · Implement various approaches to near real-time responses, including digital war rooms, dynamic process/application changes, and low-code methods.
Dealing with Operational Change:
Typically operational processes and systems are in place to deal with day to day operations. Changes in behaviors, markets, tactics, or scenarios will cascade down to operations. There may be additions and changes to procedures dictated by outside factors over the routine operational optimizations that occur on an ongoing basis. Processes tend to be more stable, but some changes could rock the house. To deal with functional change, I would encourage the following activities.
- · Model key decisions that affect KPIs and desired business outcomes.
- · Generate procedures from the models—manuals for human resources and code for processes and applications.
- · Perform a volatility analysis based on past changes to identify hot spots. Enable hot spots for change, particularly for code using late binding techniques or low-code.
We are entering an era of significant change linked to constant change. It means that just shining and studying data alone does cut it as a sole strategy. While data affects decisions as they are made, deciding what is going to change is emerging as a dominant new organizational competency area. We need to add some new disciples/practices to thrive going forward and call it Decision Science.