Today’s financial services providers seek a competitive advantage as they develop and deliver innovative products and services to customers. And to accomplish this, they analyze large volumes of data as the foundation for success. Your company is no exception. You’ve invested heavily to create the infrastructure that unites disparate data and analyzes it. Yet often, that expected return on that investment doesn’t materialize. In fact, you continue to invest precious resources trying to make the promise real.
You are drowning in your data lake.
You are not alone.
While most companies approach this as a technology issue, it’s actually a much broader and complex problem to address. We have found that financial services organizations who struggle with how to optimize their current and future data and analytics investments must overcome a number of hurdles—ones that cross the boundaries of technology, skill sets and organizational readiness.
To that end, we can break the data and analytics journey into four phases. In each phase certain hurdles must be anticipated and cleared. When you fail to address these challenges in any part of the journey, your outcomes will not fully support your business strategy and will fall short of economic targets. In addition, the entire journey must be underpinned by an organization aligned around the intended use of the data and operationalizing innovations: guided by vocal, consistent executive leadership.
Often data lake development is initiated as an enterprise-wide endeavor instead of a targeted approach to support line of business needs. In many cases companies ingest data for the sake of “filling the lake” instead of selectively taking it in from specific sources that align with a stated business strategy or need. In turn, this can create major data governance challenges.
Technology organizations tend to respond one of two ways: with a heavy-handed application of data governance as they try to deal with the influx of disparate data, or an overly light touch designed to speed data access. Heavy application of governance creates friction that can slow the data analyst’s access to the information. Too light a touch leads to missed requirements for masking and managing personally identifiable information (PII). Both approaches create problems that impact the organization’s return on investment.
True insight requires that your company possesses high quality data—delivered in a timely manner and utilized by data analysts/scientists with the right skill sets. Few organizations consistently deliver against all three of these requirements.
In many cases the time required to ingest internal and external data creates enough lag that time-sensitive opportunities are missed. In other instances poor data quality stifles the ability to create consistent outcomes from models.
Regardless of the quality or timeliness of the data, your company will never find the needle in the haystack if it lacks the data science skills to effectively analyze what’s available. The companies that consistently develop insights invest heavily to build analyst and scientist teams and have formal programs to push those skillsets throughout the organization.
Once an insight is gained, companies often fail to effectively act on it—which greatly reduces or negates the potential benefits. Leaders in this space have well-defined processes in place to propel insights into operational reality. Effective and early alignment of cross-functional teams—e.g. compliance, legal, marketing and HR—is a must to facilitate rapid time to market after an idea emerges from the incubator.
When the time comes to take the insight into action, fully understanding the cost and benefit is critical. A concept may tantalize, but is the opportunity one you can truly address? How does your company effectively reach its target customers? What cost is associated with it? Is the target base large enough to justify the expense of the rollout?
Even those who successfully navigate the potential pitfalls in these prior steps could still stumble in the last mile. In some cases an organization’s sales, marketing and customer service areas have survived bad experiences with past campaigns based on stale or incorrect insights. If this has happened multiple times, an institutional “muscle memory” develops and demands substantial organizational change effort to overcome.
Useful tactics to clear this final barrier include piloting concepts that act as success proof points for the broader organization and aligning incentives to drive adoption. As you gain momentum your company can then move into a rapid cycle to test, learn and refine innovations to optimize benefits.
Putting it all together: From drowning to addressing
Navigating the path from data and analytics investment to income from new ideas challenges every organization. Effectively address the issues outlined above to ensure that the critical supporting elements are in place. That way, your company can optimize existing and future investments and become a more insightful, innovative competitor. No longer will you drown in the data lake or dog paddle in it: Smart strategy and execution means smooth sailing ahead.
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Scott Gregory is financial services partner, Liberty Advisor Group.