TechTrends
Unlocking Value From Your Data
Businesses today have no shortage of data at their disposal, so the challenge has now become how to manage and safeguard their information effectively.
Whether you’re a startup, a midsized company or a large organization operating at a global scale, data is flowing through your business every second, connecting you to the patterns and trends of your clients and competitors. With so many possible data points to track and analyze, the opportunity for your business to learn and adapt is vast—but making the most of this data and keeping it safe can also pose real challenges.
At JPMorgan Chase & Co., we count nearly 50 percent of American households and 80 percent of Fortune 500 companies as banking clients, so the volume of data we manage is sizeable—over 65,000 filing cabinets’ worth of paper.
Like any other company, we face the important task of managing data securely, strategically and in a way that best serves our clients and their interests. When thinking about your business and data, governance and, more importantly, a data-driven culture are key to unlocking value.
Creating a Data-Driven Culture
We live in a digital-first and tech-centric world where there’s great demand for immediacy and streamlined transactions. Take your typical online shopping experience: When you search for a striped shirt, you expect all of your search results to correspond to that description. Once you’ve found the perfect blue, pinstriped shirt and you’re ready to purchase, you meticulously type in all of your credit card and shipping information—and did you notice what else you did? You triple-checked to make sure the information you entered was correct.
That same care and due diligence needs to be applied to the data flowing through your business. To rally your business around good data practices, you need to instill a sense of care and urgency and find a mobilizing North Star. It will become the heart of your data governance strategy.
3 Essential Elements of Any Good Data Governance Strategy
Any successful data governance strategy should focus on the data landscape, reference data and data quality.
Every good data governance strategy should require a deep understanding of your business’s data landscape. To understand it, you have to know what data you have, where it comes from and where it goes. With that knowledge, you’ll be able to identify which data sources are authoritative as to a given set of data, and which are conflicting or unnecessary.
Authoritative Data Sources
Strategic sources of truth for given sets of data on which your company should rely.
Non-authoritative Data Sources
Conflicting and/or unnecessary sources of data.
Once authoritative data sources are identified, you can concentrate your allocated data quality spend on strengthening those sources as opposed to unnecessarily diluting your budget and efforts. Over time, you’ll likely notice your data landscape shrinking as you define your strategic data sources and eliminate redundancies—a good sign that your efforts are paying off.
Think of reference data as the golden record or single source of truth about your core sets of data. Whether it’s information about your customers or clients, products and services, or the countries or currencies in which your business transacts, you must have a company-wide consistency as to these core data sets.
Imagine if the same customer transacts with different divisions of your company, and each division created its own record for that customer. Each division may spell that customer’s name differently, or one division may have a rich set of data about that customer while another considers it a prospect with very little information. Now imagine you’re the CEO going to visit that customer and you ask for a report of all the business it does with you. Pulling that report together will be much harder to do, if it’s possible at all.
Your data quality efforts can be thought of as being along a continuum from reactive (least mature) to proactive to preventative (most mature).
Reactive data quality acknowledges that data quality issues have occurred and had a negative business impact, so your focus is on implementing controls that effectively capture and remediate issues so they don’t recur.
Proactive data quality acknowledges that data quality issues have occurred, but you put controls in place to catch them before they could negatively impact your business.
At the most mature end of the continuum—the golden ticket of data quality—is preventative data quality. Here, you’ve built data quality into the very design of your systems so as to prevent issues from occurring at all.
If you have a good data governance strategy, you should strive to execute it so that it delivers business in three- or six-month increments. Those benefits can include increased efficiencies (technological and operational) and reductions in risk, but the greatest benefit of all may be to your clients. Accurate data can help you glean more meaningful insights to improve their overall experience.
3 Tips to Help Grow Your Business With Smart Data and Talent
- Invest in data first and technology second: Your technologies are only as powerful as the data that runs through them. Before overinvesting time, energy and money into sophisticated technologies like artificial intelligence, machine learning or natural language processing, ensure your data is of the requisite quality. Otherwise, prepare to risk relying on insights that may be false or misleading.
- Less is more: More data doesn’t necessarily equal more value for your business. It’s best to start from the ground up—start modestly by identifying 10 data points about a client, then ensure their data quality and add from there.
- Communication is key: When you’re implementing and executing a data governance strategy, communication is one of the most valuable skills you or an employee can have. The ability to put complex ideas into simple yet meaningful terms can drive adoption and help rally your business around a set of common data governance goals.
Privacy and Data in the Digital Age
With discussions about privacy and cybersecurity often swirling around in the press, it’s more important than ever for a business to be transparent with its clients about how it’s using their data.
A good data governance strategy protects data that may be sensitive or confidential—after all, a key component of any data governance program is classifying your data so as to understand its sensitivity. By being transparent with clients and respecting their privacy concerns, you’ll build loyalty and trust, deepen existing relationships and pave the way for new ones.
Key Takeaways
Don’t pursue a data governance strategy simply to satisfy internal audit or external regulators. Rather, recognize it as essential to driving your business, and use it to solve real business problems.
It’s about data quality over data quantity. Businesses use data to generate actionable insights, but those insights can mislead if the data informing them is of poor quality. Build your data stores deliberately from the ground up, and with a data governance strategy in place from the beginning.
Good data doesn’t just happen. Before feeding your data into powerful technologies like machine learning, ensure that it’s of the requisite quality.
It takes consistency, attention to detail and, most importantly, perseverance. These can be multiyear journeys. Use failures as growth opportunities and rally your employees around a common set of goals and best practices.
