Most companies will approach analyzing large volumes of data as a technology issue, it is actually a much broader and complex problem to address.
No more drowning in the data lake: Framing the journey to support your business needs
Seeking a competitive advantage, many financial services providers have developed innovative products and services for their customers based on analyzing large volumes of data. Chances are, your company is no exception. You may have invested heavily in creating the infrastructure to pull together and analyze disparate data – only to discover that the expected return on that investment has not materialized. Now, you may be continuing to invest precious resources in the hope of delivering on this promise.
Are you drowning in your data lake? If so, you are not alone.
While most companies approach this as a technology issue, it is actually a much broader and complex problem to address. At Liberty Advisor Group, we have found that clients struggling with how to optimize their current and future investments in data and analytics must overcome several hurdles that cross the boundaries of technology, skill sets, and organizational readiness.
As shown in Figure 1, we can break the data and analytics journey into four phases. In each phase, there are hurdles that must be anticipated and overcome. Failure to address these challenges at any stage of the journey will lead to outcomes that do not fully support your business strategy and fall short of your economic targets. In addition, the entire journey must be underpinned by an organization that is aligned around the intended use of the data and operationalizing innovations, guided by vocal, committed executive leadership.
Investment: A targeted approach is needed
Data lake development is often initiated as an enterprise-wide endeavor rather than a targeted approach designed to support the needs of a line of business. In many cases, this leads to ingestion of data for the sake of ‘filling the lake’ instead of selectively drawing from specific sources that align with a particular business strategy or need. In turn, this can lead to significant data governance challenges. Technology organizations tend to have one of two responses: a heavy-handed application of data governance as they attempt to deal with the influx of disparate data, or an overly light touch designed to speed access to the data. Heavy application of data governance creates friction that can slow data analysts’ access to the information. Too light a touch can lead to missed requirements for masking and managing personally identifiable information (PII). Both approaches create problems that impact the return on investment for the organization.
True insight requires that your company has high-quality data, delivered in a timely manner, and utilized by data analysts/data scientists with the right skill sets. Few organizations can consistently deliver against all three of these requirements.
In many cases, the interval required to ingest internal and external data creates enough lag that time-sensitive opportunities are missed. In other instances, poor data quality leads to the inability to create consistent outcomes from models.
Regardless of the quality or timeliness of the data, if your company does not have the data science skills required for effective analysis, then the needle in the data haystack will never be found. The companies that are consistently developing insights from their data have invested heavily in building teams of data analysts/data scientists and have formal programs to push that skillset into all areas of the organization.
Innovation: Effective action is essential
Once an insight has been gained, a company’s inability to act on this effectively can greatly reduce or even eliminate the potential benefits. Leaders in this space have well-defined processes in place to promote insights into operational reality. Effective and early alignment of cross-functional teams – including experts in compliance, legal, marketing and HR – is required to facilitate rapid time-to-market after an idea is pushed out of the incubator.
Fully understanding the cost and benefit of operationalizing the insight is also critical. The concept may seem like a tantalizing opportunity, but is it addressable? How does your company effectively reach target customers? What is the associated cost? And is the target base large enough to justify the expense of the rollout?
Income: Rapid testing, learning and refining innovations are key
After successfully navigating the potential pitfalls of the prior steps of this journey, there is still potential to stumble in the last mile. In some cases, the sales, marketing and customer service areas of an organization have had bad experiences with past campaigns that were based on stale or incorrect insights. If this has occurred multiple times, an institutional ‘muscle memory’ develops that requires a substantial organizational change management effort to overcome.
Useful tactics for overcoming this final barrier include piloting concepts to use as success proof points for the broader organization and alignment of incentives to drive adoption. As momentum is gained your company can then move into a cycle of rapid testing, learning, and refining innovations to optimize benefits.
In conclusion, navigating the path from investing in data and analytics to generating income from new ideas is challenging for every organization. By effectively addressing the issues outlined above and ensuring that the critical supporting elements are in place, your company can optimize existing and future investments to become a more insightful and innovative competitor.