Data can be an asset, but poorly managed data can be a hindrance. Offensive data strategies add business value by developing client insight, building the customer base and actively monetising information. It is vital to properly manage the data you collect; to ensure both its quality and security. Without the fundamentals in place, collecting and storing data can be costly mistakes that increase the risk profile and reduce customer trust. Poorly managed data can also give inaccurate business intelligence and poor reporting information, leading to misinformed decision-making processes.
Increased regulatory pressure
In addition to business risks, there are also regulatory concerns. The FCA and the Bank of England recently published a ‘Dear CEO’ letter on transforming data collection. This brings firms’ data operating model, data governance, data quality, data architecture, data quality and metadata approach under increased scrutiny. There are three key components of the new data transformation programme:
1 Improving the quality of data, including legacy processes and technology, to increase efficiency (in effect technology debt and data debt)
2 Interpreting regulatory requirements into machine readable formats and improving transmission methods to reduce any translation issues
3 Adopting common data standards across the industry
To achieve this, firms must develop a coherent framework to improve their data collection processes and continue to derive value from it.
In our previous article, we looked at the role of strategy and the importance of data fundamentals, including data management. In this article, we outline the building blocks you need to achieve data management and data collection best practice: from why data governance matters to data architecture principles.
A proactive approach: from data governance to data architecture
A proactive approach to data management maintains data integrity, and makes sure it is in a usable, coherent format for wider use. It can also impact regulatory compliance, making sure all data types are clearly labelled and treated in line with relevant data protection laws.
Data management enablers
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Figure 1: The key components of a data strategy
Data governance
The main data governance principles are ownership, responsibility and accountability for data. This includes sharing information and consulting with relevant stakeholders over how to use, handle or store the data. Setting appropriate data domains by product or business line helps to manage the information and maintain data integrity.
Effective data governance delegates an appropriate steward to be responsible for the data in each domain, with forums and regular governance meetings to align stewards’ approaches. This will be underpinned by robust policies, procedures and standards, including any specific actions needed for each in-group entity or different jurisdictions.
Metadata management
Effective metadata management can be the difference between having good quality data, and useable good quality data. In addition to making the data categorisable and searchable, metadata management also supports compliance activities and inform decision-making processes. Given the volume of data flowing through an organisation, it can be difficult to differentiate which data elements/attributes are truly business critical.
Prioritisation of Critical Data Elements (CDE) is essential to metadata management, as they will feed into key business processes and operations, such as annual reports, legal and regulatory, audit or risk management. Examples may include those denoting customer data, including personally identifiable information and inputs for regulatory reporting.
Data quality
Poor data quality can result in suboptimal customer experience, including customer complaints, increased costs and regulatory scrutiny reporting. It can also limit your ability to cross-sell or upsell to customers, which may impact the bottom line.
Data quality business rules establish the thresholds for each data element (for example across customer, product, rates, account and transaction data sets), ensuring greater consistency, completeness and accuracy in the quality of data collection.
Appropriate metrics can gauge the quality of the information and identify where content, structure or metadata needs attention. This is supported by data profiling, which assesses the data for its usability, limitations and potential applications. High quality data must be accurate, complete, unique, timely and consistent.
Data lineage
Understanding where the data has come from and how it has been processed is vital to maintaining its integrity. This includes tracing movement from the source of data to its final usage, identifying controls and closing any gaps where possible (ideally for critical data elements).
Good data lineage will help isolate genuine anomalies and identify any unintended changes to the information as well as discovering insecure end-user computing applications.
Data architecture
Data architecture is a set of rules, models, policies and standards that govern how data is collected, stored, managed and integrated in relation to the database system(s).
Good architecture enables authoritative data sources, which are essentially the go-to, trusted repositories for master data. Having a single source of truth is integral for analytics, business intelligence and repeatable automated processes.
Reference data
Reference data can be industry-wide or business-specific, and is used in conjunction with master data. It is an important mechanism to ensure there is a common language of data, especially between organisations, and between legal entities of the same organisation. Reference data is especially important in the completion of transactions to describe and catalogue counterparty and security identifiers when making a trade.
Implementing data management best practice
There are some simple things you can do to enhance your data management practices and prepare for the data collection transformation journey vision laid out by the FCA and Bank of England. Reviewing existing capabilities can improve governance, metadata, quality and lineage. A robust data management review will also identify any areas for remediation, leading to new data execution plans. This will include legacy quality issues that may impact customer, transaction or account data, which can impact regulatory compliance and reporting. Getting these foundations in place can help a business evolve from using data as a reporting tool, to a valuable asset in its own right.
