Data is everywhere.
Global data creation is projected to grow to more than 180 zettabytes by 2025, according to a report by Statista. To understand the scale, imagine if each gigabyte was a brick. You could build 46,440 Great Walls of China with that much data.
As data proliferates, data skills have become critical for almost every role in an organization. Data-driven decision-making improves accuracy and efficiency at every level, and it is the opposite of gut instinct, which is predicated on heuristics and emotions.
“Data never lies,” said Prizeout Chief Technology Officer Brendan Grove. “It is easy to convince yourself that a project, team or individual is doing better or worse than they actually are. However, looking at, measuring and tracking the correct data gives you a more accurate sense of performance and outcomes.”
Unfortunately, data is often full of noise and it’s easy to misinterpret — which is where data literacy comes in. Understanding the meaning of data vastly improves organizational outcomes. Data skills improve judgment and increase confidence in decisions. And, according to Gartner, data literacy will be a focus of more than 80 percent of data and analytics and change management strategies by 2023. Companies need to invest in improving literacy now to keep up with the staggering volume of data they have access to.
“Data literacy ensures we are using the right data, measuring actual signals and using data to iterate effectively within the organization,” added Grove.
To give other tech leaders insight into local data literacy initiatives, Built In NYC talked to Grove and Kurt Boehringer, chief financial officer of Derivative Path, to discover how they are fostering a culture of data literacy at their New York City-based organizations.
Prizeout is a performance-based adtech company that offers a platform for payments, rewards and loyalty solutions.
What programs, initiatives or trainings did you use to promote data literacy across the organization?
We’ve found exposure to data and a culture of data proficiency to be the best promotions of data literacy. This can be as simple as the data team sharing insights — small and large — with the rest of the organization, but extends all the way to teams tracking and measuring their performance. In order for this to be successful, we made sure to invest our time and energy in building out a world-class data platform; making our data across our systems available, in real time, for all to analyze and use with self-service tools and pre-built reporting.
A mindset of “measure everything” can be detrimental if it takes up too much bandwidth or creates analysis paralysis. When done well, it gives the ownership of tracking performance to the team and allows them to test and experiment more quickly without any of the downside risks. We want our teams to be empowered to try anything to reach their goals, and this measurement can quickly and easily tell them if their efforts have been well targeted, if there have been any unintended consequences, and if they are truly making the progress they want towards their primary objective.
What new capabilities has data literacy unlocked for your team?
Better decision-making, improved planning, and the ability to be honest with ourselves. We have a saying: Decisions start with data, they don’t end with it. I think this mindset has allowed us to move faster, fail more often and ultimately find what works.
Decisions start with data, they don’t end with it.”
For example, on the technical side, I have always found that goal setting and measurement are what allow us to actually evaluate ourselves. If we thought we could get a certain amount of work done in two weeks and we didn’t, then why was that? Did we not plan well enough before we started? Should we have asked for help more quickly during the work? Did we pigeonhole? Were there too many injections and fire drills? Or were we just bad at estimation? These questions are all solvable when you are honest with yourself and effectively measure with analytics. Data creates the forum to have the introspective conversations needed for true personal and team growth.
Derivative Path is a fintech platform that provides advisory and technological solutions to financial institutions.
Why is broad-based data literacy important for your company?
Derivative Path has a firm-wide culture of making data-driven decisions. We lean on data to allocate resources, understand customer behavior and identify bottlenecks. Our sales and marketing team aggregates conversion data from inbound and outbound lead-generation efforts to closed deals. This data then drives where we invest additional sales dollars such as in sales hires, SEO, content marketing and strategic partnerships.
We invest in research and development based on data regarding where clients have their most significant pain points and identify trends of how our clients’ needs are evolving. We numerically track the bandwidth of operations and support teams to meet ongoing client demands and include capacity for unpredictable requests.
What programs, initiatives or trainings did you use to promote data literacy across the organization?
During our new-hire onboarding, we emphasize the value of data-driven decision making. Being a fintech platform, our employees have exposure to data during their day-to-day job. As a result, the application of data-driven decisions happens organically. Our CEO insists that all critical business decisions must have an ROI component to justify the decision. Leadership will offer internal and external support for employees seeking to improve their data literacy.
Leadership will offer internal and external support for employees seeking to improve their data literacy.”
What new capabilities has data literacy unlocked for your team?
Data literacy has been vital to direct our investment as we build our go-to-market strategy. We have become more targeted in our outreach and marketing spend. It also enables us to run experiments and test hypotheses against live data. Our product and engineering teams rely on these metrics to plan and reflect on the effectiveness of our investments. We’ve focused on two key areas: engineering and business line distributions.
Engineering distribution data helps us maintain balance with our engineering teams. It shows a composition of how we’re investing our time across several categories. For example, are we spending too much time on roadmaps — and seeing a rise in production support or client requests? It gives our leadership team more insight during our planning process to better understand our products’ overall health.
With business line distributions, data effectively conveys to leadership the percentage of time we invest into each business line. Data insights have allowed us to see how investments have been allocated and are critical for determining future investments.
