By Daria Chadwick | Part I of III
Over recent years, we’ve come to learn how quickly change can happen and how drastically it can impact our organizations. Today, the notion of “returning to normal” stands as a foregone conclusion: we face no old or new normal, but rather a world of continuous uncertainty.
Business and technology leaders across all industries are acknowledging that change is everywhere and is inevitable. They understand the need to architect for resilience and know they still have to deliver superior business performance, especially during times of volatility.
In light of these learnings, and in keeping with the overarching goal of digital transformation, leading organizations are responding accordingly by:
Applying composable thinking across architectures, technologies, services, and processes;
Increasing the adoption of value streams in building cross-functional alignment and collaboration to optimize costs, reduce risk, and expand business value; and,
Focusing on agility and flexibility to better adapt to the accelerated pace of business change.
The Driving Force
At the heart of these efforts is data & analytics, and with it, attempts to leverage quality, data-driven decision-making at every possible opportunity. However, these opportunities aren’t nearly as prevalent today as leaders or their teams need them to be.
In fact, progress in this area is moving far slower than initially anticipated, despite the significant investment made in data over the past decade. It seems that becoming truly data-driven is proving to be a very difficult process.
Why? Some attribute the difficulty to a disproportionate investment on the technical side of data over the business side of data. Others point to the defensive approach to data that focuses on governance and privacy. In the same breath, executives have named cultural challenges as the #1 blocker to becoming data-driven for the fourth consecutive year.
It’s no surprise we’re seeing multiple, complex reasons why the business side of data and analytics isn’t taking hold as much as we’d like or as much as it has the potential to. After all, we’re still very much in the early stages of applying data for better decision-making. Given our rudimentary status contrary to where we'll even be in a few years, we should accept now that we'll have to adjust our trusted “knowns'' and our “normals” around data as the capabilities and our best practices around data progress.
In this way, we can build and implement what technologies and standards serve us best as our organizations grow and evolve.
But as we venture deeper into yet another year of digital transformation efforts, we do so equipped with a better understanding of the blockers that persist around data today. At this time, we should briefly pause to assess these blockers. More importantly, we should listen to what these blockers are saying about our current efforts.
What do blockers to being data-driven tell us?
In becoming more informed on the value of data, executives began issuing directives through the organization that stressed the importance of employees understanding and using data in their daily decisions. As we’ve come to learn, this approach isn’t generating the returns executives hoped for.
Maybe it’s because the bulk of decision-making occurs at the furthest possible point from executive direction:
Maybe this is also a contributing reason to why culture is sitting pretty as the #1 blocker. As Peter Drucker famously stated, "Culture eats strategy for breakfast."
It’s important to have a top-down approach so that the organization at large is moving in the same direction. But if we’re not supporting a top-down approach to data with a bottom-up one, we’re going to be spinning our wheels in the mud.
We have to work on reducing frustrations and problems where data-driven decision-making is actually happening. Otherwise, all the data literacy training and value-of-data education is going to fall on deaf ears. It's a tried and true business model to build products and services that fit into how people work and function, and not the other way around. Our data-driven decision-making capabilities should act the same way.
"It's a tried and true business model to build products and services that fit into how people work and function, and not the other way around. Our data-driven decision-making capabilities should act the same way."
So, what have we learned so far in assessing these blockers? We’ve learned that becoming data-driven is ultimately about the ability of people and organizations to adapt to change. We’ve learned that we need to complement top-down approaches with bottom-up ones that target where the bulk of data-driven decision-making is actually occurring. And, we’ve learned that these bottom-up approaches have to fit into how our people work and how our organizations function.
There is one last lesson to be learned. While it’s fairly simple, it’s critically important:
We cannot expect our existing data & analytics capabilities to support these types of efforts and levels of change when our data & analytics capabilities don't even possess these characteristics themselves.
We cannot expect to blaze valuable, revenue-generating trails in this modern age and have our current data & analytics capabilities support them effectively as these capabilities exist in their current state.
It's not that we need our data & analytics capabilities to align as closely as needed to our businesses processes. It's that we need our data & analytics capabilities to actually be infused into our business processes. In this way, if those processes suddenly change, our data & analytics can change just as quickly.
So, given that our data & analytics capabilities today have to be more effective, widespread, flexible, and infused into our processes, what exactly does or should that look like?
The experts have some suggestions. We identify and evaluate those in The Future of Data & Analytics: Reengineering the Decision & Adapting for Change PT. II >
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