This is the third piece of a 5-part series on plugging the obvious but overlooked gaps in achieving digital success through a refined data strategy.
Data is essential, yes. But the whole idea of being a data-driven organization can be quite misleading. It incongruously tilts the scale of focus towards sourcing more and more data than monetizing it, to an extent that collectively, there is expected to be 40 zettabytes of data by the end of this year! Which is great, of course, but let’s cut right to the chase here: the real deal with data does not lie in its vastness, it lies in an organization’s ability to churn out insights that yield performance improvements and give them a competitive advantage. (a mismatch in data collection vs its monetization is discussed in part 2 of this series). Which is the reason why I insist that we change the narrative from data-driven to insight-driven organizations.
Because ultimately, it is the insights that drive value. It is the information that you derive from the data that will:
But there’s got to be a villain in every story…
If deriving value from insights was as easy and powerful as this, why aren’t all companies already doing it? For numerous reasons but topping the list is the cultural gap that has caused managers to not optimally utilize data-driven insights to make decisions.
Sometimes, the tools are too complicated for a business-facing, less-technically equipped frontline employee to use. Other times, the organization has a deep-rooted culture of intuitive decision-making or hasn’t modeled a way to adopt data-driven decision making.
Whatever be the cause, an organizational shift is necessary to become an insight-driven organization. And if you find yourself stuck in this spot, here are a few things you can consider to resolve the challenges.
Build accountability through ownership. Let your data strategy have KPIs identified for each team that will then collect, assimilate, and derive insights from corresponding data. Ensure that the KPIs are in keeping with your day-to-day processes and decision-making norms.
For managers and frontline employees to use new algorithms and models on a daily basis, it has to be easily consumable. Not everyone is a data scientist. Identify intuitive tools and interfaces that simplify the work for the less technical folks, not complicate it.
Improve the data and analytics literacy of your workforce. To weave it into the fabric of the organization, leaders will need to model the behavior, introduce incentives and metrics, and have training to reinforce the behavior.
Don’t read data in a way that it supports your assumptions. Look at data objectively. Let the data guide instead of the other way around.
A successful digital journey does not start with data but with a well-charted data strategy. (Read post one here). And it does not end with data collection and management but with deriving insights through analytical models that can improve business performance. Finally, in the last post of this series, let’s touch upon the significance of breaking down complex data into simple, understandable nuggets of information that is ingestible by one and all.
By Anita Raj
