I have a had a lot of conversations about data strategy this year.
With both the rise in organizations looking to move their data to the cloud and the increasing awareness of the power of BI and generative AI, data strategy has become a top priority.
If the data is not correct then its garbage in and garbage out, or as I have always said :
BIG DATA + BAD DATA = BIG BAD DATA
However, when it comes to a good data strategy, it’s not just about the quality of the data, it’s also about the design and functionality of the structure where it is housed.
As organizations look to design and create new data warehouses, lakes and even lake houses in the cloud and populate them with data from a combination of legacy on-premise data warehouses , new cloud applications and existing on-premise applications, they quickly realize this is a huge undertaking.
When they try to include implementing a new cloud ERP or other application and adding in a Data Governance initiative just for good measure, it becomes a gigantic undertaking. If they also decide to include converting to new BI tools and adding new generative AI tools, it becomes monster-size!
Yet, all these items are within the realm of a Data Strategy so where do you start?
This is where the infamous “How do you eat an elephant?” conundrum comes in. The equally infamous answer is “A bite at a time” but I realize it is not that simple.
What is simple is the advice not to try to eat the elephant all at once but even that sometimes falls on deaf ears. I understand some people’s bite is bigger than others and depending on the existing data, applications, warehouses, BI tools, resources and expertise already in place, a larger initial phase 1 may be feasible.
The bigger question though is where to start.
The best starting point is a Discovery exercise which will look at the current state of your data strategy, the future state of what is desired and the analysis of the gap between them. This can be done internally or you can bring in outside Data Strategy specialists but the best approach, I have found, is to use a combination of the two. While the current state subject matter expertise is likely to be internal, the future state and gap analysis is often better performed by Data Architects and consultants who have done this exercise before and can bring both experience and current technology/methodology knowledge to the table.
A Data Strategy Discovery exercise includes auditing the existing data and applications and interviews with the business function folks who know their data and their reporting/analysis needs. It will also help to identify a potential pilot project (or Proof of Concept) which would be the next logical step after the discovery.
The end result you are looking for is to truly understand both the size of your Data Strategy elephant and the realistic bites you can consume.
Of course, both product and consulting vendors will often tell you they can do the whole thing if you use their methodologies and tools. Be wary of these super elephant eaters because you may end up with only a partially eaten elephant!