In the last piece, we spoke about compliance and governance of data, but everything can go in vain if you are collecting nothing of value. The situation becomes similar to someone on the quest for gold, who ends up collecting pebbles. For most companies, the gold/pebble analogy syncs with their approach because though they have a treasure trove of data out of them, most of the stuff is invaluable until you have a data quality control framework to keep a watch on everything.
What is a Data Quality Framework?
It is the process where you can set up specific definitions for the data that you are collecting. If the collecting of the data does not match with the specific definition as per the organization, it should be flushed away. However, the process would require setting up principles, processes, and tools that could aid the monitoring of the data as per the organizational definition since data will be coming from different verticals. That’s where building an effective data quality framework will come into play.
How Will An Effective Data Quality Control Framework Help?
- Bettering the decision-making process
- Meeting regulatory compliances & risk mitigation
- Enhancing operational efficiency
- Increasing trust & reliability
- Bettering data integration & consistency
- Improving ROIs on data investments.
In 2009, Optum, a subsidiary of UnitedHealth Group, developed a Data Quality Assessment Framework(DQAF), and it was seen that Optum’s decision-making capability increased significantly. For example, when Alberta Health Services used Optum’s DQAF, Alberta Health Services was able to improve the skills and knowledge necessary for its related patients to improve their diabetes monitoring and treatment. In the span of just 365 days, it was seen that Alberta Health Services could resolve some of the key problems in their patients, like back pain, high blood pressure, and high cholesterol. Allegiance to the DQAF provided by Optum that Alberta Health Services could improve its reputation and patient intake.
How To Build An Effective Data Quality Control Framework?
After this success story that you learned above, as an organization, you now understand that DQAF can take you leaps ahead in the competition, but building an effective one is challenging; that’s where this process will help you;
1. Assessment
Your first line of approach will be to build an effective DQAF, and for that, you need to define the terms of the sources, metadata, and data quality indicators. Once you have completed the same, you need to choose sources of incoming data like CRM, third-party providers, and others. After you have done the same, you will have to define the size, patterns and formats. These will be the filters used to refine the data, extract only the ones that add value, and flush out the rest.
2. Pipeline Design
Once you have collected all the data as per the specific needs of your organization, you need a design that can make the data readily available to the department in the manner they want. For that need, you need a pipeline design to clean, match and protect the data. For that, you will have to make a series of updates to your DQAF, such as cleaning and standardizing, deduplication, data merging, and more. With a proper pipeline design in place, this can be easily achieved.
3. Monitoring
The final stage will be monitoring because you have done the assessment and created the pipeline design, but the data must reach the respective verticals in a predefined manner for the best impact. For that, you need a monitoring tool that will ensure that you can assess the data quality and make necessary changes for better outcomes.
In order to make that happen, you have to put a checking system that can see the configured data. Furthermore, you will also need additional software support to eliminate last-minute errors so that the best data will reach the desired location. Moreover, you will also have to set up a warning system so that all the corrupted data is flagged before they enter the quality pool. All of these would require technical adeptness and proficiency for best outcomes.
Build Your DQAF with Ve3
This is where the role of Ve3 starts because Ve3 will be helping in your DQAF through MatchX. MatchX is a revolutionary software system that allows you to match the data using its core competencies like (i) Advanced Data Ingestion, (ii) Intelligent Data Profiling, (iii) Automated Quality Improvement, (iv) Precision Data Matching, (v) User-Friendly Dashboard, (vi) Comprehensive Reporting. If you want to stay ahead in the game where data is the new oil, stay tuned with MatchX and let us do the business matching for you like never before. Contact us or Visit us for a closer look at how VE3’s solutions can drive your organization’s success. Let’s shape the future together.