Business teams use applications to help automate business processes to bring about efficiency and productivity. Activities performed within applications create data that is then processed, organized and structured into information that needs to be recorded, managed and integrated. Such information, when governed and made available to the right stakeholder at the right time, can provide significant insight in three areas:
In short, organizations use information to improve their business outcomes; help improve productivity, drive a better customer experience and more. Data is the leading asset to drive all such initiatives and effectively managing that asset is the foundation that helps an organization differentiate itself.
Data governance is the framework for managing data with the right monitoring and controls, enabling organizations to make fact-based decisions. Data governance involves:
Data Governance in Financial Services
The increase in regulations in the financial services space exemplifies the importance of governing data with the right analysis to meet such mandates. Many regulatory risk and compliance standards generate multiple risk models that are very complex and statistical in nature producing outcomes that are highly dependent on the underlying data that is used for measurement.
The results from these complex models are often monitored daily by a bank’s risk manager to avoid or mitigate risk. They also use these models to report risk results to regulatory boards and external agencies to demonstrate strong financial stability in the organization. As a result, the banks build customer confidence that encourages additional investments leading to successful growth. This waterfall – from monitoring to risk reporting to customer confidence – means the results from the data models must be accurate.
Risk data consumers are some of the key stakeholders for enterprise data governance teams. The more transparency and trust you provide around the data, the more leverage and fact-based decisions are made using the data. To build such trust that encourages usage, data governance teams need to focus on important data trust building initiatives such as data integrity controls, information architecture, metadata management and data quality.
Unlimited Data Needs to be Managed
The proliferation of IoT and big data initiatives has provided organizations with unlimited data, which often means data overload that results in poor decision making. This means wasted time and investment. With so much data to manage, data governance teams are tasked with identifying key data information to help business teams make good business decisions with the confidence that the data is accurate.
Some of the key data parameters such as accuracy, timeliness, consistency and completeness are also key data quality gradients to build trust with the data consumers to help them understand that they are using the right information and facts to support the decisions they make. To help facilitate building trust, data governance teams should provide transparency around such data quality parameters and dashboards reporting on that data.
Who Uses the Information We Have?
Data governance teams are tasked with identifying what data they have and understanding what information is available for data consumers. Such questions that need to be answered include:
Answering these questions will help organizations realize that their data is the key differentiator.
It’s important for governance teams to monitor data that is flowing across the organization to catch data anomalies and prevent them from reoccurring. Providing reliable, trustworthy data needs multi-level reconciliation against a standardized benchmark or enterprise source of truth to demonstrate that transparency.
To help achieve success, standardized data reconciliation controls are key for data governance teams. Controls that monitor data flow are especially important to include:
Balancing controls are an important aspect to governing data. They need to ensure trust across each stage in the data/process flow. Each time an aggregation is done, it’s important to balance the data post- and pre-aggregation to ensure the roll up is providing the right information.
The final piece of controls include quality monitoring controls that need to be in place across the data flow – to monitor different data quality gradients as the data moves through transformation and aggregation. Quality controls capture metrics that not only help report current quality status, but also provide trends to understand quality root cause.
In working with many leading financial institutions, controls are always in place but often lack enterprise visibility due to the customized controls that monitor data at discrete control points. This approach derails data governance transparency and fails to follow the business process flow between multiple systems to prevent bad data from proliferating. When best practices are followed to create enterprise control points that monitor a process from end-to-end the organization can achieve continuous enterprise visibility into the performance of the process. This approach also yields reusability in business rules and removes manual and semi-automated processes that are prone to error.
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