1. Adopt a holistic approach to data management.
Businesses approach data in two different fashions: project and discipline. If you are currently taking on quick-win, clean-up data projects, you will likely run into common data roadblocks that prevent organizations from becoming data-driven. Seeing real-time, consistent progress that drives your data initiatives and business outcomes starts with an ongoing data management plan. Start by investing in a self-service data quality management tool that can be easily accessible across your entire team. Then you can you standardize processes across the business around how data is governed and leveraged so you can start using reliable data to inform your decisions.
When you frame your approach to data management as a discipline, you will better understand who your customers are on individual level, and you’ll have reliable historical data to predict trends and drive deeper customer insights.
2. Evaluate your current state and where you need to improve.
If you’ve been patient, implemented processes to manage your data, and you still haven’t seen actionable insights, it’s time to go under the hood. For data management to work, it takes a cultural shift. Schedule a data deep dive with your team to understand where they see gaps in your current data operations.
Ask for their input on the value they see in the data, whether they’re confident they have quality data, why or why not, and whether current practices are supporting access to clean, actionable data. We often find the culprit is a lack of cross-departmental communication around data management. In fact, more than a quarter of businesses see this as a challenge when enabling data throughout their organization.
If this is the case, consider standardizing communication touchpoints such as regular meetings to share what you’ve learned from your data or providing access to cross-departmental dashboards to better align strategies. By understanding what is and isn’t working you can determine your next steps, like investing in data quality management tools and processes to better fit your team’s needs.
3. Tackle your data debt.
Like all types of debt—financial and technical—data debt is the baggage that will prevent you from seeing the benefits of new tech and your investment in data management. Inaccurate and incomplete data can be your biggest roadblock to becoming data driven.
Our research shows 65 percent of businesses say inaccurate data is undermining key initiatives. Tackling data debt starts with prioritizing data quality. To clean up your records, you need the right tools to validate your customer data so you have correct postal addresses, email addresses, and phone numbers within your database. By investing in these tools, you can confidently fuel your initiatives with quality data, ultimately relieving your pile of data debt to making room for ROI.
