In data we trust…or do we? That’s the question—and it’s one that the vast majority of companies continue to agonize over. Our research shows over the past several years, the level of customer and prospect data collected has stayed stagnant at nearly 30 percent, and nearly half of companies do not trust data in their CRM or ERP. And while data quality issues have commonly been swept under the rug, the impact is starting to bubble.
“Poor data is often entangling entire organizations with crippling effects, preventing timely decision-making and negatively impacting customer experience,” says Steve Philpotts, General Manager, Data Quality & Targeting, Experian AUS.
The quality of data underpins every facet of a business. To compete in today’s market, investing in trusted data for advanced analytics and insights is mission-critical to operational efficiency, customer engagement, and loyalty (and bigger profits). Just how much? Research shows by increasing the usability of data by just 10 percent, the average Fortune 100 company could expect an increase of $2 billion dollars in revenue.
We’re here to help set you on the path toward trusted data with four actionable steps you can take today.
Get buy-in for addressing data quality issues
With organizations’ sights set on becoming data-driven, getting data quality on senior leadership’s agenda is an absolute must. You’ll first want to make sure you can quantify your business case for data quality. Consider looking at ways to link data quality issues with wasted time and resource allocation, or better yet, tell the story around how data quality issues hurt efficiency, increase risk, or act as blockers to strategic business initiatives. That’s sure to get some attention.
Tie data projects to tangible results
Once you’ve gotten signoff, you’ll still need to earn trust from the business with measurable and meaningful data outcomes. “Data projects that maintain a laser focus on driving tangible and practical business outcomes are often the ones that will succeed and be lauded across the organization,” says Philpotts. He suggests considering metrics that can be delivered in shorter and more agile timeframes such as impact decision-making speed, call center efficiency, operational success, and customer satisfaction.
Avoid focusing solely on traditional data quality metrics such as completeness when telling your data story. Instead, focus on what matters most to your organization, whether it’s, for example, call center efficiency, cash flow, e-commerce experience, operational efficiency, and more.
Identify quick wins
The heftier your project scope and goals are, the more likely you are to have unforeseen obstacles, moving targets, and longer timelines. Meanwhile, you’ll start to lose management confidence, support, and funding. Instead, Philpotts suggests focusing on practical, outcome-based quick wins that demonstrate the value of data improvements. “These will be different for every organization,” he says. “However, consider a quick profile of your data to get an assessment of your biggest challenges, and find ways to make small changes that can have a material impact.”
Focus on sprint initiatives with a smaller scope—such as improving data quality at the initial point of capture through your website—that enable you to easily quantify, measure, and communicate results back to the business so you can build trust and secure funding for phase two.
Drive forward a data culture
Now that you’ve built momentum for an investment in trusted data, maintaining the quality of your data requires a scalable, enterprise-wide approach. Our research shows 70 percent of organizations report a lack of data literacy skills—the ability to read, work with, analyze, and argue with data—in the business is impacting the value they get from investments in data and technology. Without a data literate workforce, how can you fully leverage trusted data to become data-driven?
“A data-informed culture is a determining factor that fundamentally differentiates companies that are digital native from their digital-delinquent peers,” says Paul Malyon, Head of Data Literacy, Experian UK&I.
Adopting an enterprise-wide data-driven mentality—regardless of whether employees carry a data job title—so your organization adopts and maintains data hygiene requires a groundswell effort. We find organizations are investing in the office of the CDO and hiring data practitioners to spearhead this effort, and another third plan to implement a data literacy program within the next 12 months. Consider rallying existing data practitioners to aid the creation of a wide-ranging training curriculum for your data literacy program to include topics such as privacy, ethics, governance, security, quality, storytelling, decision-making, and more.