9 Steps To Achieving Data Quality Success In Your Organisation - Downloadable Infographic
9 Steps To Achieving Data Quality Success In Your Organisation - Downloadable Infographic
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Anyone that has tried appreciates that achieving good data across an organisation is not easy. For many organisations, getting to the point where data quality is seen as a “non-negotiable” will take commitment, effort, and investment.
For regulated organisations in financial services sectors such as superannuation, banking, insurance and wealth management, poor data can also result in breaches, potential fines, and reputation loss.
From Strategic decision making to surviving constant change; organisations are beginning to realise that the consequences and risks of making incorrect decisions is now far greater.
For organisations to embark on a journey of successful data quality implementation, the following is a high-level overview plan worth considering:
1. Understand how good data adds value
It is important for the organisation to realise the value of data quality, then identify and develop use cases that illustrate quantitative and qualitative benefits. For example, identify a business process that requires a heavy use of data and identify any tangible benefits of error free data. This can serve as a business case to communicate the value across the organisation.
2. Conduct a health check of the current state
Understand the current state of any data capabilities and identify if the organisation currently has a framework already in place. Then determine what is working versus not working and what needs to be changed. This will help create a baseline and starting point for your data initiative project. It will also identify hotspots, gaps and key opportunities which should then be used to structure data projects with a targeted scope and measurable tangible benefits. Documenting this will get the project off the ground and enable business funding and executive sponsorship.
3. Find champions to endorse data quality
Finding data champions in your organisation that can articulate the value of data quality to both business and IT will help drive data projects forward. It is important for senior management to sponsor and influence data quality objectives in team scorecards and individuals' development plans.
These data champions also need to identify steering committee members so that measures of data quality and reporting are conducted frequently to ensure wins are celebrated and a momentum is created to build data quality improvements across the organisation.
4. Start with a pilot, test it, then replicate success in other areas of the organisation
Do not start with a big bang all in approach; this will never work for data projects.
Ensure you select an area that has relevant data problems but is not too complex to fix and remediate. Implement and fix that area, ensure it is successful then move on to tackle the wider data issues. Learn from mistakes and update any skill sets that are missing. Celebrate small wins!
5. Attract and retain talent
Ensure skills developed during the data quality project are retained, especially individuals that process data management and business experience. Build your data quality team with key resources that communicate between business and IT teams.
Ensure that knowledge transfer within the team occurs frequently to ensure all information learnt is shared. Define career progression plans beyond the data quality project such as leadership opportunities to help retain talent. If help is required, seek assistance from external consulting organisations that are focused on delivery of data quality and data remediation projects.
6. Select the right tools to uphold data quality
Cleansing data is a labour-intensive task, identifying the errors is much harder. Ensure you select and scope out software that can help with data errors identification (at a minimum). Most organisations do not like data remediation to be done automatically as usually their preference is to ensure it is fixed and controlled from their front-end applications.
If the front-end applications are allowing bad data, fix this! Put in a process to use the automated scheduling capabilities of the data quality software to check for ongoing data quality issues. If it occurs frequently, identify why it is recurring.
7. Conduct an ongoing audit of the data quality process
Once you start the data quality initiative, it does not mean you stop there! There are many reasons why data projects can go off-track. It is important to revisit the original project charter and re-validate if the original scope and objectives are still on track for delivery?
Do you still have the budget to meet project objectives? Does your project still solve the identified issues? Are the timelines still reasonable compared to the original project plan?
8. Refresh perspective and ongoing change management
Sometimes it is difficult to understand why a project is not going well. You might need to get advice from others on how they successfully approached similar data quality problems. It might also be beneficial to get external perspectives to understand how other organisations have solved their data quality problems.
9. Regular data quality checks
Finally, it is critical to maintain a continued focus on data quality as an ongoing function within the organisation. Data quality processes and protocols must be routinely monitored and enhanced. The organisation must remain ready to kick- start further data projects when required.
If you are interested in taking control of your Data Quality, book a demo to see how Investigate DQ can transform your data quality.
