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.

Data Quality Do’s

Set Your Expectations From the Start

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.

Build Your Interdisciplinary Team

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.

Deliver Quick Wins

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.

Don’ts

Don’t Leave the Clock Running

More often than not, data quality is an afterthought. “Garbage in, garbage out” has become one of the most common mistakes that hampers the benefit of any IT or digital transformation initiative. By the time you realize you need to fix it, it’s too late – your data lake has become a data swamp. Taking control of your data has turned into a very tricky and expensive initiative. There’s no compromise in taking control of your data before you can share and process it.

Don’t Overengineer Your Projects, Making Them Too Complex and Sophisticated

It is tempting to go straight to the holy grail – often referred to as the single version of the truth. But is it what you really need and is your company ready to deliver? Some data management disciplines, such as Master Data Management, can bring your data quality standards to the highest level, but this requires a lot of effort, strong sponsorship, and authoritative approaches to governance. There are other initiatives, such as self-service data quality or data cataloging, that allow you to instill trust in data. Consider them as alternatives or as natural milestones that will help you step up in your maturity curve.

Don’t Sell a Data Quality Project

Data quality is a discipline. It requires methodology, tools and processes. But this doesn’t entice the lines of business to join in. Few people in a company care about a 360° view of customers, for example, unless they understand how it will boost marketing campaign efficiency, customer conversion rate, or time to support a customer claim. To succeed, your project must be widely known within your organization and linked to the concrete benefits it brings to the different activities. As you make it more specific to some activities, some might understand that their own activities were not in the scope. Guess what? They may well ask you to extend your projects and solve their data quality pains. This is a good reason to ask for more budget.

Conclusion

Data can drive insights, and it can drive the business, so we better trust it. That’s why data quality has become so important. The cost of doing nothing is rising. But it has become much easier to fix issues by informing people in the company of the value of data and training them to become data savvy. At the same time, technologies have become easier to use by anyone via self-service, smarter in their ability to capture data quality issues and automate their remediation, and more pervasive in their ability to bring data quality controls wherever they need to be. Are you ready for the challenge? 

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