Data quality projects are becoming collaborative and team-driven. As organizations strive to accomplish their digital transformation initiatives, data professionals are realizing they need to work as a team with business operations so everyone has the quality data they need to succeed. Chief Data Officers need to master some simple but useful Dos and Don’ts about running their data quality projects.
Start by connecting the data quality issues with business outcomes. For example, when a marketing team realizes that 20% of their activities will never reach their target due to data quality issues, they’ll be more likely to get on board with the data quality project. Keep in mind, however, that this is an ongoing process and that perfect data might never exist. Set intermediate goals, realistic expectations and make sure you measure each success.
Data has become a serious business in digital transformation, and as a result, a growing number of people within different lines of business have become data-savvy. All of these people individually complain that they spend 80% of their time crunching the data before they can turn it into something useful. So, what if everyone combined their talents and worked as a team? This is your opportunity to make data a team sport. Establish a shared service with a data platform and bring onboard the digital marketing experts who struggle to reconcile the data coming from external channels. Additionally, Data Protection Officers need to make sure that the data in your brand-new cloud data warehouse is properly anonymized.
While it’s key to stretch capabilities and set ambitious goals, it’s also necessary to prove that your data quality project will provide business value quickly. Don’t spend too much time on heavy planning. Instead, prove business impacts with immediate results. For example, what about organizing a "data clean-up day" with the sales and marketing team to apply quick fixes in your Salesforce or Marketo data? Once you have demonstrated how easy it is to get benefits, you gain credibility, and people will support your project, allowing you to move onto the bigger tasks.
More often than not, data quality is an afterthought.
