Embracing AI: Why an Insight-First Approach Outperforms the Quest for Perfect Data

Embracing AI: Why an Insight-First Approach Outperforms the Quest for Perfect Data

Embrace the Insight-First Journey

Analytic Insights

Software and consulting companies alike are asking “Are you AI Ready? Take our Readiness Assessment”. Most questions focus on data maturity, but how mature is mature enough to get started?

By Candice Sutterfield

Manager | Analytic Insights

In the rapidly evolving world of Artificial Intelligence (AI), the quest for “mature” (i.e. perfectly curated and governed) data often feels like a pursuit for the Holy Grail: alluring but elusive. The truth, however, is that the path to impactful AI solutions does not require picture-perfect data with mature governance to be in place.  Instead, consider using an Insight-First approach where AI use cases are the catalyst to implement or enhance a fit-for-purpose data program. This slight shift in perspective is a strategic approach to make AI a reality in your organization quickly.

Insight-First vs Data-First Strategies

The traditional approach to a comprehensive data program is centered around the data itself.  It is logical, make a business case for mature data:  how best to access, consolidate, process, store, and protect the data.  Once in place, now create dashboards and predictive analysis to assess business value.  Here is the challenge: where is the business value actually realized – in the mature data itself or in the outputs of business intelligence and predictive analysis?  The [hopefully] obvious answer is that business value is realized when business problems are solved and actionable insights are given to business leaders.  It follows that the approach to building a high value-added data program should be AI lead, Insight-First not Data-First.

The New Data Strategy Roadmap: Build Data that is Fit-For-Purpose

What does this new roadmap look like?  A prediction is built from data, so how is this different than a data-first approach? 

The majority of work in most data science projects involves collecting and preparing data – even in situations where “mature data” already exists because the data may not be fit for AI purposes.  With an Insight-First approach, the focus is on the data needed to accomplish that use case.  While designing transforms, security, and operations for the current initiative, consider other AI use cases and any common data needs for future iterations.  Build and harden the data and data management iteratively instead of phasing disciplines: engineering then analytics.  This will greatly shorten the timeline to BI and predictive insights, providing faster time to value.  Now, assess the AI output’s contribution to business value then rinse and repeat.

Embrace the Insight-First Journey

The journey towards AI starts wherever you are right now. Embracing the Insight-First journey is not a compromise but a strategic approach to quickly build AI systems that are ready to make a difference in your business. As you navigate the complexities of your data, don’t wait for perfection. Don’t let perfect be the enemy of good (enough) to get started.