Two Words That Will Change How You Think About Data
They say there are a few moments in your career that stay with you, that change the way you think, and permanently alter how you approach your work. I was fortunate early on in my financial services career to have such an experience that made me think quite differently about the value I was able to deliver.
Coming from a quantitative background, I was always focused on getting the numbers right and understanding what they mean. Truth be told, my view was narrow. I had never really given too much thought or credence to the term “governance.” What did that mean anyway?
But, then came a pivotal interaction with a regulator that rocked the way I saw the world. It started with a statement, then a question, and then a challenge:
- The statement: “Numbers look reasonable and make sense.” (Phew.)
- The question: “But how do you know they’re right? Do you trust the process that produced them?" (Do I?)
- The challenge: "Prove it."
Those two words made me realize it isn't just about the final numbers. Demonstrating the integrity of the process by which we produce the numbers is just as important and valuable as the numbers themselves.
The value of a firm, but flexible process
Being able to vouch for your numbers and the process that produced them delivers immeasurable returns. If you can't demonstrate the integrity of the process by which you report data, how much weight and value does your data actually have? What credibility can you lend to your leadership, regulators, and the marketplace?
What if you could unequivocally rely on the integrity of your data? What would that trust be worth to you?
In the years to come, improved data integrity will help the financial services industry build trust against a backdrop of tough challenges:
Challenge 1: We work in a complex and fast-changing regulatory environment.
Operations for compiling business data for regulators depend on people, processes, and technology. Maintaining business continuity, control, and confidence is critical. A breakdown doesn't just impact a point in time. It can have far-reaching consequences.
Challenge 2: We face heightened regulatory and senior leadership expectations.
Organizations collect and gather data for a purpose, from giving leadership the information needed to make strong decisions to helping regulators better monitor business activities.
The whole reason we capture information, move it, and store it is so we can use it. Once we agree on that, we can begin to understand how to maximize data ownership and information stewardship for the purpose of delivering enhanced business insights across an organization more frequently, more rapidly, and with higher quality.
Challenge 3: We're under pressure to do more with less.
In today's economic environment, operational costs are under a microscope. The need to make sense of a growing volume of both quantitative and qualitative data faster, with fewer resources, is not going to go away anytime soon.
Challenge 4: Data is almost always very complex and interconnected.
Nearly every organization—no matter how big or small or where they do business—struggles with the operational effectiveness of their data. A seemingly endless network of connections creates processes that are redundant, yet somehow unreliable as well. This daily barrage of data and information handoffs increases operational risk, leads to duplicative work, and requires layers upon layers of data cleansing and manipulation just to have useable data.
Challenge 5: Emerging technologies are changing the landscape. (Hi, fintech!)
On top of worrying about regulations and internal challenges, new technology is constantly pushing the envelope and transforming 'nimble' from an aspirational buzzword to a potential reality. Adopting new technology in our highly regulated environment, however, is really hard. It's essential to choose the right partners that will help you establish new ways of working without compromising the trust that has taken years to build.
Transforming processes so you can trust the data
With all of these unique hurdles, how can we continue to move quickly while improving our ability to "prove it"? The key is having an efficient, operationally resilient process of managing critical business data. Improving this process can ultimately give your organization a strategic advantage by making data accessible, traceable, and usable.
The Holy Grail is finding the balance between maintaining an appropriate level of governance without becoming too cumbersome.
When established, a system of work that connects data, systems, and collaborators provides key operational advantages for an organization by:
- Eliminating redundant, non-value-add work
- Reducing operational risk
- Increasing operational resiliency
- Increasing efficiencies, speed, frequency
- Enhancing not just data transparency but the understanding of the data
It's done by enabling high-quality and controlled information through a flexible data environment that’s governed by end-to-end audit capabilities and operationally sound data practices.
If you don't know where to begin, start by simply creating a map or flowchart of your process, so at the very least, the steps are documented for newcomers and veterans alike. From there you can identify parts of the process you'd like to improve over time. It's a journey, but it starts with a single step.
Workiva provides a system of work designed to transform how teams operate, with a linked, flexible, and controlled platform connecting data, systems, and collaborators in a single ecosystem. To see more, request a demo.
About the Author
Heather Markheim is the Senior Director of Product Marketing for financial services at Workiva. She drives the go-to-market strategy and execution for Workiva solutions supporting banking and insurance. Before joining Workiva in 2020, Heather spent 17 years in the financial services industry in a wide range of roles, including enterprise data management, CCAR, model risk management, and enterprise strategic transformation. Her research and areas of interest include big data and analytics, explainable AI, and machine learning.