During March 2014, the FinTech Society of the Technology Association of Georgia (TAG) conducted a market research study with financial services and payment processing organizations in Georgia to determine whether data analytics and big-data technologies (DA/BD) are playing a significant role in generating new revenues and helping reduce operating costs. To the best of my knowledge, this is the first primary research that has been conducted on the use of data science as a key driver of the organization.
Georgia is a tidy place to conduct such a research study since over 85 billion payment transactions flow through the computer systems of Georgia-based financial technology organizations. This volume represents about two-thirds of all consumer and commercial financial transactions. With over 100 FinTech firms in Georgia, there is a good cross section of companies to tap for our research.
Prior to beginning the research, we knew that data analytics and big-data technology usage was in the early adoption phase. But we wanted a baseline to define where these organizations are today and where they perceived they need to be by 2020 in their data science initiatives. Our plan is to update the research every two years through 2020 to determine whether FinTech organizations are changing their business models and becoming more data-driven.
In Part 1 of this three-part series, you’ll get an overview of the research findings. As we review the information, please build your own baseline of how data science is changing your business model. Our research respondents indicated that there would be considerable changes to their business models between 2014 and 2020. Data science will be a key driver.
The 2014 baseline would ideally be a measurement -- a specific rating that provides more specifics than “early adoption stage.” But, measuring the use of a new technology is like measuring a cloud -- every time you try to take dimensions, it changes. Today, our research indicates that data science is not giving FinTech organizations a competitive advantage within their market segments. In general, our respondents said: “Our organization needs to do a better job of embracing the use of data analytics.”
Said another way, the analysis of data to improve decision-making and guiding the strategy of the organization is less than it could or should be. Why? Here are some reasons our respondents shared.
· 75% of the respondents believed their organization was either moderately investing or underinvesting in its data science capabilities -- defined as a combination of technology (software/hardware) and education/training.
· 65% of the respondents believed that management commitment was low to medium in making the needed investments. What is inhibiting organizations from adopting a data-driven model? Here are some verbatim responses:
· Competing priorities
· Lack of skills/talent
· Lack of understanding of data value to improve the business
· Lack of executive sponsorship
· Ability to get the data -- data ownership
· Lack of ROI of prior programs
· Lack of strategy and funding
· Concerns/restrictions related to privacy and other regulatory issues
· Existing culture does not encourage sharing information/collaboration
· 69% of the respondents believed that their organization’s executive team does not use data analytics to manage key performance indicators (KPI) in the organization.
Like many industries, financial services and FinTech organizations scan the competitive landscape to see who’s investing in what technologies and whether they are having a positive impact on corporate results. When we asked about competitors, our respondents grouped themselves as follows:
· 61% indicated that they will measure the strength of a competitor’s analytics capabilities when doing competitive analysis. They will look at partner relationships, core strengths of their analytics programs, revenue and volume from data analytics/big-data programs, and new products based on data science.
· 70% of responders indicated that competitors with strong data analytics capabilities are either a “modest” or “significant” threat in their respective markets.
In the category of success-breeds-success, a key issue we found is that executive management has not seen what they consider positive results (ROI) from prior investments in data analytics projects. In terms of measureable results, past attempts at generating new revenue streams have not yielded the results needed to drive additional investments into data analytics programs. This could be the result of not having the right technology in place. Or, could it be that management does not know how to work with data derived from the data science?
According to McKinsey and Company, by 2018 there will be a 190,000 person shortage of data scientists to meet the data analytics needs of enterprises. In addition, and more significantly, there will be a 1.5 million person shortage of managers capable of interpreting the findings of data scientists and of knowing how to take action on that data.
Data science is, and should be viewed as, a new management discipline. Just like a new enterprise software system such as ERP or CRM technology, it requires significant investments in core technology infrastructure, in software tools, and in lots and lots of training. Most importantly, to become data-driven executive management must instill a corporate culture that supports data-centric practices. If data scientists are cordoned off from the day-to-day operations of the company, they will always be viewed as outliers.
When we asked our executives if they had big-data programs underway, here’s what they had to say:
· 50% of the organizations have big-data projects ongoing. Here are examples of the projects that respondents classified as big data (analysis of very large data sets):
o Enterprise search
o Knowledge management
o Financial analytics
o Fraud detection
o Risk trended data
o Real-time sales offers
o 360-degree view of client and fraud analytics
o Product development
o Fraud customer-experience improvement
It’s interesting to note that 57% of the companies are embracing the analysis of social media in their current analytics programs. The volume of data to analyze in social media certainly qualifies as big data. But, the question remains: What to do with analysis once completed? What percentage of the analyzed data is used to drive new revenue, reduce operational costs, or improve customer service?
In Part 2 of this three part series, we’ll examine the technology challenges in becoming data driven and hear what our financial services respondents had to say about their technology challenges and their drive to 2020. You will see how their plans come together to transform the way they do business today and become data-driven.
To view the results of this research study, please visit the Technology Association of Georgia website.
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