Adoption: Point 4 from the previous part of this series is about starting the pace of technological adoption slowly with some quick, easy-to-measure wins that can get the buy-in of change agents in your organization. Switching out an entire suite of software all at once can be daunting, especially for non-technical staff. Smaller wins help drive adoption in a more digestible way and create appetite for bigger changes.
Data quality is a prime example. If data is not scrubbed and validated, issues will pop up throughout the enterprise, from regulatory reporting to customer support. The cost of data issues can be quantified by marking the time spent catching, analyzing, and remediating data issues. But other, less quantifiable costs should be considered as well, such as diminished employee morale due to repeatedly handling data issues rather than their core functions.
If an automated data quality solution were in place, tangible results could be generated without straining staff. The resources required for a hierarchal model are released to add value to the organization.
By addressing data quality first, multiple benefits are realized:
-
A key foundational layer of the data governance program is created.
-
Additionally, if validating data quality is achieved collaboratively, teams across the organization can have both ownership and confidence in a “single source of truth.”
-
Teams implementing the technology directly see its democratization along with the flexibility of the new data governance model.
Collaboration: Point 5 from the section above involves a focus on features of technology that support a decentralized data governance and data democratization. Two key features are:
-
Intuitive user interface (UI): This enables non-IT team members like BAs to utilize the technology without deep technological expertise (such as developing code). When BAs and domain experts are the data owners,
-
No user limit: IT does not have be the gatekeeper of all data requests. User limit creates inefficient processes in data requests – In some cases, even members of the IT team may not have access to certain database portals. Working with legacy systems, this was a common scenario, which contributed to additional layers of requests and additional delay in generating analysis and insights.
BaseCap Analytics
BaseCap Analytics has worked with clients with varying organizational structures, cultures, and legacy technology systems, helping them achieve successes in their data initiatives. While each of these models carries values to their constituents, BaseCap Analytics has found that a decentralized model provides for the greatest flexibility, knowledge base, employee empowerment and ultimate growth of an organization. The new paradigm of data governance calls for the decentralization of data responsibilities within a collaborative environment.
Data quality is the foundational layer for a strong data governance program. Our team designed and developed the Data Quality Manager to help our clients ensure the quality of their data so they can truly rely on their data to drive business decisions. Here are some key features of the platform.
-
Automated and insightful data quality reporting – As business grows, it will require more data. Automation allows more data to be validated without additional staff. These data quality reports inform users on the root causes of data issues and empower them to remediate these issues collaboratively all on one platform for a single source of truth.
-
Unlimited and customizable access enables collaboration – As noted repeatedly, data democratization requires strong collaboration to maintain data quality control. There is no user limit, so everyone from an organization’s CDO or DPO all the way down to BAs can access a single source of truth.
-
Intuitive interface facilitates the development of data culture – Users are not required to have deep data science knowledge or be able to code. The interface enables individuals who are not data-driven to start dealing with data. For example, a BA can write business rules to validate data elements by using plain human language. This way, anyone in your organization is empowered to become more accountable for the quality of their data.
-
Simple integration enables quick implementation of data governance – Change will not be overnight. An existing IT infrastructure will persist while implementing automated data quality control. Again, this can be a way to ease an organization towards data reliance, by first ensuring there is a layer of data quality control before adding other key components to the data governance framework.
BaseCap Analytics’ team of data experts has helped large financial institutions achieve their data governance milestones in 1/10th of their expected timeline. By leveraging the Data Quality Manager, we also helped a client investment firm double its mortgage business revenue without adding any staff.
Contact us today to see a demo of how the BaseCap and its Data Quality Manager can help you achieve your data initiatives.
This article was part of the “Data Governance for the Digital Age” series:
Part 2: Organizational Structure, Evolving Roles and Responsibilities for the Digital Age
