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.
Net; Net:
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.
This comment has been removed by a blog administrator.
ReplyDeleteThis comment has been removed by the author.
ReplyDeleteThis comment has been removed by a blog administrator.
ReplyDeleteThis comment has been removed by a blog administrator.
ReplyDelete