A central data management strategy is becoming essential in modern organization and it's essential to have a solid foundation to build from. Data management is not only about having procedures and technicalities. Communication and leadership are key to succeed with efficient data management. That said, we all know that cleansing and transforming data is one of the biggest challenges and task in most organizations. We will help you understand how to ease the process and work with data quality and effective data management in this blog post.
Read this list of do's and don't and succeed with your data management project.If you are not sure how to measure data quality, read our brief about what data quality is, what to measure and why it is important.
Why data quality? What is data quality in your company? Find your data quality answers among business people and business goals. Make sure you and your team know your finish line. Make sure you set goals with a high business impact.
Recruit data architects, business people, data scientists, and data protection experts as a core data quality team. It should be managed by a deployment leader who should be both a team coach and a promoter of data quality projects.
With defined ownership from a business responsible, master data lead and business accountable.
Use your data quality core team to confront short-term compliance initiatives such as GDPR to gain immediate short-term value and strategic visibility.
When establishing your data quality plan, set bold business-driven objectives. Your plan will retain the attention of the top officials and stretch people's capabilities.
Remember, goals should be SMART (Specific, Measureable, Achievable, Realistic, Time-bound) - learn more in this article from Harvard Business Review on how to set goals and objectives.
Quick wins start by engaging the business in data management and key to that to have the ability to generate insights so you can establish awareness around Master Data. Examples include onboarding data, migrating data faster to the cloud and cleansing product master data.
Define and use measurable KPIs accepted and understood by everyone. Data quality is tied to the business, so drive your projects using business-driven indicators such as ROI or cost-saving improvement rate.
When finishing a project with measurable results, make it visible to key stakeholders. Know-how is good. It's better
with good communication skills.
A data quality project is very much as much communication effort as it is technical from a management perspective.
If you go with a project that is too broad it will end being too heavy or difficult. Attack one piece at a time. Do you know the saying about how to eat an elephant? One bite at a time, do not try to swallow it whole.
More knowledge isn't always better. Your team must learn from team experience first.
Take a step back and keep a company-wide vision. It's important to keep the business objectives in mind at all time when evaluating "are we on track?".
Set and meet deadlines to bolster your credibility. If time is running fast and your organization shifts to short-term
business priorities, track your route and stay focused on your goals.
Your projects need to empower everyone to curate data.
Use cloud-enabled applications that can scale across the company with a simple, intuitive interface.
Make sure that you set up real-time tracking on your progress with data quality improvements.
Finding the right tool to support empowerment, communication and with effective monitoring and management have been difficult. Some tools on the market demands comprehensive training before use and adds a lot of weight to your data setup. A tool like exMon could be the right choice for your company - find out by getting a free trial on exMon Data Management or book a free, instructed demo with one of our Data Management and Data Governance specialists.
Don't just take our word for it. Product Master Data Manager in Marel, Elsa Gudbergsdottir says ‘Within a “second” you have this super user-friendly tool that users and people can relate to and exMon has a lot of features that are not common if you compare it with other data quality tools’