Measuring Success in Data Management
It is a challenge facing many data practitioners - just how do you measure the success of your data management change programmes and operations in a meaningful and sustainable way? The deployment and adoption of tooling to support foundational data management capabilities often provides valuable performance insight for the data practitioner. However, attempts to derive business metrics often leads to highly bespoke solutions that quickly become complex and unmanageable. Quaylogic have established a framework that is sustainable and supports the business with meaningful measures of success.
Increasingly, data is being recognised as a critical business asset and the key to unlocking the potential of digital transformation, advanced analytics and artificial intelligence. Legacy infrastructure is under scrutiny in an effort to simplify data architecture and provision high quality data services. The ability to augment these data services with third-party sources to improve decision making, will continue to be a differentiator in promoting business agility and innovation. Meanwhile, as incidents of cyberattacks and data misuse rise, governance and control of data assets will continue to play an important role in sustaining the confidence of regulatory bodies, shareholders and customers. Ultimately, the performance of any data management organisation should be measured in terms of its contribution to the demands of the business. The business metrics should therefore represent desired outcomes in supporting digital transformation, regulatory compliance, operational efficiency, business intelligence / analytics, data risk mitigation as well as ethics, governance and sustainability.
Establishing foundational data management disciplines in any organisation includes the adoption of tooling to support capabilities such as Data Catalogue, Data Lineage, Data Quality, Issues Management and Data Engineering as well as Data Risk, Policies and Controls etc. This may include a combination of existing tools as well as the deployment of new third-party or in-house developed products. For many data practitioners, these tools not only support functional capability but also provide dashboards for monitoring the associated data management discipline.
However, these dashboards typically operate in silos and source their metrics from proprietary metadata repositories. These metrics can be easily interpreted by the data practitioner but are rarely of any meaningful value to the business stakeholder community. Further, the metrics are static and offer little scope for configuration - collating performance metrics across multiple data management capabilities is therefore manual and time-consuming.
The natural evolutionary step is to build an independent reporting hub comprising a custom data mart and bespoke dashboard. Custom interfaces can be built to extract metadata from each of the tools into the data mart where the underlying performance metrics can be used to derive more meaningful business metrics. Sadly, many of these initiatives end in failure. The data marts and dashboards become unmanageable as a result of diverging bespoke requirements for interfaces and reporting views; and the logic supporting each of the bespoke reporting views is of value to only a very small community of business users. Ultimately, the business stakeholder community becomes disillusioned by the inconsistent presentation and interpretation of business metrics.
Quaylogic have established a framework for measuring success that is sustainable. It comprises an integrated Metadata Repository with API / Adapter connectivity to the leading data management tooling and a Reporting Hierarchy that applies business rules and logic to derive meaningful business metrics - dashboards evolve organically in accordance with common structure and standards. Requirements from the business (measures / tolerances / outcomes) can be configured and easily maintained in the Reporting Hierarchy which then orchestrates the metadata to derive measures of performance and success that are meaningful to the business. Rules-based derivation of business metrics means that it is possible to drill down into the underlying performance metadata. For example, from the scorecard oversight of Data Risk, it is possible to drill down to a line by line account of outstanding data quality issues in a remediation queue or from the scorecard oversight of Digital Transformation, it is possible to drill down to the detailed status of data being ingested into the Operational Data Store. All of these metrics can be presented by business domain which may include a combination of function, legal entity, business line and/or geography. Further, role-based user access to the dashboards ensures a tailored and relevant experience for both business users and data practitioners alike.