The goal of becoming a data-driven enterprise is a business-savvy one, but it has generally ended in disappointment as projects and investments fail to shift the culture of the enterprise toward making a majority of decisions backed by data. Do not give up! As analytics and artificial intelligence capabilities are baked into an ever-growing range of enterprise applications and the need to squeeze value from data investments grows, enterprises will be forced to be data driven.
Making a majority of decisions by using data to understand the background and likely outcome is obviously a good thing from a business perspective. At Ovum we are consistently asked for good practice on how to make an enterprise more data driven. Our traditional answer invariably combines elements of people, process, and technology changes that add up to more than just investments in new technology – they sum to a change in culture. Problem is, changing business culture is hard. Just because it is hard does not mean we should not try, but add other challenges like a lack of data and analytical skills in the workforce, and tackling the problem with traditional approaches starts to look intractable.
There is good news. Two trends are combining to create a critical mass that will make the data-driven enterprise a more achievable objective. First, analytics and AI are no longer "nice to have" additions to enterprise applications – they are the "must have" functionality of just about every enterprise technology vendor's current releases and roadmaps. Second, while data hype may have lessened, the investments it yielded in capturing and storing reams of new information are now desperately trying to make a measurable return on investment – something analytics and AI have the potential to unlock. Put these two trends together and barriers to a change in culture are substantially reduced.
This does not mean that effort is no longer required to help the process along, nor does it mean that existing investments are wasted. Acceptance, followed by trust, takes time to achieve, and while the analytic or AI capability may be baked into the application, whether the user will act upon it is a different matter. Tracking, documenting, and sharing the positive outcomes associated with using available analysis and automated insight is key. Along with improved business outcomes and a sense of achievement among workers, this approach enables another interesting benefit: bringing survival of the fittest to data and analytics. Where benefits are greatest, most attention will go – focusing effort on the data and techniques used to interrogate it that offer the steepest upside.
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