To Build a Data-Driven Organization, Executives Must Create a Data-Centric Culture
In March 2014, the Technology Association of Georgia (TAG) FinTech Society conducted a research study with financial services and financial technology organizations on their adoption of data science as a way to drive new revenues and lower operating costs revealed an industry in transition. The transition: Evolve from early adopters to fully embracing data analytics and big-data technologies as a core way of doing business by 2020.
This transition will recognize data as a true natural resource of a financial services organization. Data, analyzed properly, provides the knowledge to make informed and accurate business decisions. Ideally, these decisions will refine areas such as new product development, risk management, customer relations, competitive positioning and branding, marketing systems, and pricing strategies. In effect, data can change core elements of a financial services firm in dramatic ways.
Respondents to our survey were very forthcoming in sharing the current status of data science within their organizations. Today, data science serves as a specialized discipline and is generally conducted within the specific departments such as marketing or finance. In past years, data analytics specialists focused on risk management -- clearly a worthwhile function. The issue, though, is that executive management has not been thrilled with the ROI other types of data analytics or big-data projects have achieved.
The research results show a traditionally conservative industry that knows data science is key to its future, but is having difficulty in making the transition to becoming data-driven. Respondents shared in the research that they were concerned with the commitment management has shown to data science projects. Other corporate priorities have sidelined data analytics and big-data projects, and postponed necessary core investments required to become data centric.
Before we define a roadmap to 2020, here are some of the key findings from the TAG fintech research:
- While fintech organizations believe that data analytics/big-data should play an important role in their business, few organizations see it as a competitive advantage today.
- Today’s fintech organizations are not investing sufficiently to become data-driven.
- Data analytics/big-data projects are generally viewed as not delivering a strong ROI; however, areas are emerging where organizations are starting to see value.
- The adoption barriers organizations face are as much managerial and cultural as those related to data and technology.
- Data collaboration will increase in importance for fintech organizations.
- From a regulatory perspective, the future of financial transaction processing seems fraught with change, making the cost of compliance expensive and cumbersome. Data security and privacy regulations could slow adoption of data analytics as a business foundation.
- The vast majority of fintech organizations expect their competitors to invest in data analytics/big-data programs to strengthen their competiveness and market position.
- Core components of a successful organizational strategy such as the IT infrastructure, training, education, and professional talent are not in place.
While respondents were critical of current data science programs, they showed an overwhelming optimism that their organizations will be data-driven by 2020. That’s a tall task.
So, how do financial services organizations get from where they are today to being data-driven? Unfortunately, there isn’t a one-size-fits-all roadmap of how to become data-driven by 2020. Every part of the organization needs work to turn the corner. Since each fintech organization is different, each requires its own unique roadmap.
Becoming a data-driven fintech organization is a multi-dimensional effort. Each organization must realistically assess its strengths and weaknesses in data science. From this assessment, a tailored roadmap can be built.
Here are the core elements of the roadmap management must pay attention to and implement.
Business culture: Fintech organizations must build a culture in which data is central to the organization. Management must think data-analytically and support a culture where data science and data scientists thrive. Data-driven organizations are agile, able to respond to market opportunities, build new product/services faster, and meet customer needs more thoroughly. Fintech organizations are data rich, but the availability of data does not ensure successful data-driven decision-making. Management must understand how data science works, how data is analyzed, and how decisions are built from the results. Management should add to the senior management team a manager well versed in data science. Managers need to understand the fundamental principles of data science and be able to invest in and nurture a data science culture. The more “data” is an afterthought, or a sideline, the less competitive your organization will be in 2020.
Technology: There are many dimensions to technology. To build a data-driven organization, the technology assets must be in step with the business strategy. The more out-of-sync business and technology are, the more debilitating the impact on the organization’s agility. Investments in technology need to focus on the sources of data, how data is moved around the organization, and how it is handled, either through transactions, reporting, or analytics. The analysis of large data sets (big data) and unstructured data requires new and different technology than the organization probably has today. Even if the organization has already invested in new technologies, management has to figure out how it becomes central to the organization -- not just serving as an incubation environment.
Data framework: A fintech organization must know where all of its data is (structured, semi-structured, and unstructured). Data management specialist need to map the data to determine its structure/schema and its potential value. Structured data such as accounts payable or accounts receivable is pretty straightforward, but sifting through customer data from blogs, emails, and social media is a different challenge. An organization’s data framework maps your data assets such that a management team knows what it has to work with. Tacked onto the data framework can be external sources of data such as industry databases, analyst reports, newspapers, and more.
Data scientists: Interpreting the results of data analysis is not a job for the untrained. Just as a radiologist is trained to read MRIs and X-rays, a data scientist has a unique, trained skill set to interpret data. Market and product strategists must be able to shape new products and services from this data and constantly monitor performance results. Recruiting data scientists will be a challenge. They are in high demand as many organizations (not just fintech) realize the value and potential of data.
Management structure: Many of the management structures in fintech organizations today are not designed to support a data-driven organization. Executives need to be trained how to organize, manage and nurture a data-centric culture. Moreover, they need to understand how to interpret the output of the data analytics. Management needs to empower employees throughout all disciplines of the organization and monitor risk, failures, and successes. An executive, fluent in the management of data science programs and culture, needs to be part of the executive management team. Organizations are often naming this position the Enterprise Data Officer (EDO).
With extensive experience in the high technology industry, Don focuses on enterprise strategy development, operations execution, revenue generation, product definition, market/product positioning, and branding. RightCourse is a management consulting firm that helps FinTech ... View Full Bio
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