Six Do’s and Don’ts of Collaborative Data Management

Data Quality Projects are not technical projects anymore. They are becoming collaborative and team driven.

As organizations strive to succeed their digital transformation, data professionals realize they need to work as teams with business operations as they are the ones who need better data to succeed their operations. Being in the cockpit, Chief Data Officers need to master some simple but useful Do’s and Don’t’s about running their Data Quality Projects.

Let’s list a few of these.


 Set your expectations from the start.

Why Data Quality? What do you target? How deep will you impact your organization’s business performance? Find your Data Quality answers among business people. Make sure you know your finish line, so you can set intermediate goals and milestones on a project calendar.  

Build your interdisciplinary team.

Of course, it’s about having the right technical people on board: people who master Data Management Platforms. But It’s all also about finding the right people who will understand how Data Quality impacts the business and make them your local champions in their respective department. For example, Digital Marketing Experts often struggle with bad leads and low performing tactics due to the lack of good contact information. Moreover, new regulations such as GDPR made marketing professionals aware about how important personal data are. By putting such tools as Data Preparation in their hands, you will give them a way to act on their Data without losing control. They will be your allies in your Data Quality Journey.

Deliver quick wins.

While it’s key to stretch people capabilities and set ambitious objectives, it’s also necessary to prove your data quality project will have positive business value very quickly. Don’t spend too much time on heavy planning. You need to prove business impacts with immediate results. Some Talend customers achieved business results very quickly by enabling business people with apps such as Data Prep or Data Stewardship.  If you deliver better and faster time to insight, you will gain instant credibility and people will support your project. After gaining credibility and confidence, it will be easier to ask for additional means when presenting your projects to the board. At the end remember many small ones make a big one.


Don’t underestimate the power of bad communication

We often think technical projects need technical answers. But Data Quality is a strategic topic. It would be misleading to treat it as a technical challenge. To succeed, your project must be widely known within your organization. You will take control of your own project story instead of leaving bad communication spreading across departments. For that, you must master the perfect mix of know-how and communication skills so that your results will be known and properly communicated within your organization. Marketing suffering from bad leads, operations suffering from missing infos, strategists suffering from biased insights. People may ask you to extend your projects and solve their data quality issues, which is a good reason to ask for more budget.

Don’t overengineer your projects then making it too complex and sophisticated.

Talend provides simple and powerful platform to produce fast results so you can start small and deliver big. One example of having implemented Data Management from the start, is Carhartt who managed to clean 50,000 records in one day. You don’t necessarily need to wait a long time to see results.

Don’t Leave the clock running and leave your team without clear directions

Set and meet deadlines as often as possible. It will bolster your credibility. As time is running fast and

your organization may shift to short term business priorities, track your route and stay focused on your end goals. Make sure you deliver project on time. Then celebrate success. When finishing a project milestone, make sure you take time to celebrate with your team and within the organization.


To learn more about Data Quality, please download our Definitive Guide to Data Quality.


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