Achieving regulatory compliance by improving data quality

Transcription

1 Achieving regulatory compliance by improving data quality This White Paper outlines some of the techniques used by a Tier 1 global bank to implement a regulatory metrics platform (RegMetrics) to obtain deep insights into data quality and achieve immediate usable results, which can be used to steer data governance and drive data quality initiatives. Published on 22nd February, 2017

2 Contents Contents What is driving the need for data quality metrics? Data governance: The Top-Down approach RegMetrics: The pragmatic Bottom-Up approach to data quality Implementing continuous data quality for RegMetrics Benchmarking and measuring data quality RegMetrics approach Define and understand the data Establish data taxonomy Define data quality rules RegMetrics platform implementation Data quality metrics Data remediation & escalation Conclusion About Datactics... 9 General Enquiries: Achieving regulatory compliance by improving data quality 2017 Datactics Ltd.

3 1. What is driving the need for data quality metrics? Recent financial regulations and directives, such as BCBS 239 and FATCA, are driving banks to get to grips with data quality as the regulations require banks to establish data quality management procedures, including data profiling, data provenance or lineage, monitoring, reporting and escalation procedures. While perhaps the ultimate intention of the regulations is to improve data quality, it is however important to understand that these regulations are also about ensuring that banks understand the quality of their data and have the processes to improve data quality. In practice, data quality regulation is being addressed in two ways: Data Governance and RegMetrics (data quality metrics for regulation). This paper focuses on the importance of the bottom-up approach of RegMetrics which is complementary to data governance and establishes many of the operational procedures that are implied by it. 1.1 Data governance: The Top-Down approach Broadly speaking, data governance is a top -down approach that changes the way organizations source, distribute and consume data, requiring buy-in at Chief Executive level, which has led to the rising importance of the role of CDO. In relation to data quality, data governance aims to define a policy for every piece of data sourced or generated. Data distribution systems must then undergo technical infrastructure changes to comply with data policies and such changes are far reaching and costly. Data governance is a vital but slow process and the pressure is on to realise tangible results from data quality initiatives. It is unwise to adopt a pure top-down approach: Unless data governance is guided by empirical evidence, the policies are making many assumptions regarding how data is sourced, distributed and consumed. Data governance needs to understand what is actually happening in the organization first. Is data from X really as good as you thought? Did you know it s consumed by Y when it was intended only for Z? 1.2 RegMetrics: The pragmatic Bottom-Up approach to data quality In contrast to data governance, RegMetrics derives insights from today s data quality, which then drive data quality initiatives (e.g. remediation) and shape data governance. Top Down Data Bottom Up Data Governance RegMetrics Data Remediation Unless data governance is guided by empirical evidence, the policies are making many assumptions regarding how data is sourced, distributed and consumed. 2 Achieving regulatory compliance by improving data quality 2017 Datactics Ltd.

4 Benchmarking actual data quality opens the door for pragmatic data quality initiatives that respond to the quality of data as it actually exists today, achieving beneficial results today. The rest of this paper outlines the methods used by Datactics and an innovative Chief Data Officer at a Tier 1 global bank to measure and improve data quality from the bottom up, yielding actionable metrics with significant benefits in a matter of weeks: Rapidly completed a significant proportion of the work required for regulatory compliance for example clear escalation procedures; Achieved instant insight into the actual current levels of data quality; Identified key areas where substandard data quality issues can be immediately addressed; Prioritized data governance initiatives according to the need as shown by empirical evidence; Established a framework within which to assess the effectiveness of data governance initiatives. 2. Implementing continuous data quality for RegMetrics Continuous data quality requires robust, repeatable, and transparent data quality measurement which necessitates the use of a stable and flexible data quality platform. The pragmatic approach to data quality requires a process of continuous data quality whereby the quality of data is measured continuously during both on-boarding and in BAU. In stark contrast to ad hoc data quality assessments that may be done when data is on-boarded, continuous data quality requires robust, repeatable and transparent data quality measurement which necessitates the use of a stable and flexible data quality platform such as Datactics RegMetrics. First, we define appropriate metrics then establish a baseline data quality level according to how the data is today. But of course since the nature of financial data is that it s constantly changing, we need to keep on measuring the quality and so we establish a continuous iteration of measurement, visualization, decision-making and data remediation. Data Remediation Appropriate Metrics Data Visualisation Continuous Measurement Establish a Baseline 3 Achieving regulatory compliance by improving data quality 2017 Datactics Ltd.

5 2.1 Benchmarking and measuring data quality One of the first things you do when embarking on a diet is to weigh yourself, i.e. record a baseline benchmark against which you can evaluate the effectiveness of your efforts. Otherwise you are just embarking on the diet in blind faith hoping that it will work and you won t be able to understand the effect of any deviation from the diet. We need to do the same with data quality initiatives: record a baseline benchmark for where the quality actually is today, against which we can measure the effectiveness of data quality initiatives. It is vital that any quantitative measurement is grounded in a clearly defined standard and data quality is no exception. We have chosen to adopt the Enterprise Data Management Council s Data Management Capability Assessment Model (DCAM) standard for data quality. DCAM is a framework using a consistent scoring model, to assess data management capability within the financial industry. Adopting the DCAM standard for data quality measurement allows us to benchmark against previous measurements, therefore providing a clear quantitative assessment of improvement (or deterioration) to data quality. DCAM defines seven data quality dimensions: Completeness Coverage Conformity Consistency Accuracy Duplication Timeliness These dimensions can be used as the basis for assessing data quality and can act as the Key Performance Indicators (KPI) on data quality dashboards such as those developed by Datactics. 2.2 RegMetrics approach With the data quality platform in place our attention moves to the data content. We need to clearly understand what we are measuring before we attempt to measure it Define and understand the data First the universe of data fields needs to be clearly defined e.g.: What data sets are in scope: customer data, transaction data, credit data etc.? What fields in the data sets are in scope: identifiers, Meta data etc.? This also provides an excellent opportunity to identify and document any caveats in the data, for instance deprecated fields. Next these data sets need to be clearly understood, not just in terms of content but also in terms of context: Ownership who s responsible for the data and correcting defects in the data? Origin where did the data come from? One or more sources? Consumers who consumes the data? Front office? Back office? Customers? Use case what s the intended use of the data? Are there usage restrictions? These data sets need to be clearly understood, not just in terms of content but also in terms of context. 4 Achieving regulatory compliance by improving data quality 2017 Datactics Ltd.

6 Although this is largely the realm of data governance, augmenting this information with data quality metrics enables you to understand the implications of a deterioration in data quality and establish clear escalation procedures for remediation Establish data taxonomy One of the most painstaking yet valuable tasks in this engagement was the systematic production of a data taxonomy and data dictionary by which the client could describe the data. Taxonomy is absolutely critical to master data management to ensure data is referenced in a consistent manner. In this engagement a Datactics Analyst helped the client build a data dictionary which was then stored in the Datactics database where it could be used for reporting on data quality metrics and in particular, navigating data quality metrics reports. Furthermore, the client then shared this data dictionary across internal departments enabling, for example, the credit department to share data with compliance department, both referencing data elements in a uniform manner Define data quality rules This is perhaps what most people understand by data quality analysis however, it s worth noting the preparation that was necessary before reaching this stage. For each data field in scope, we define simple binary rules for assessing the quality of the content. For example, consider a price field that we expect to be populated with decimal price values, but actually contains data of varying quality: PRICE NA OK Should be 2 decimal places Should be 2 decimal places Should not be blank Should be numeric Should not be negative OK We could define the following set of binary yes/no rules to determine the quality of the data in the field: Rule ID DQ Dimension Rule 1 Completeness Must be Populated 1 2 Conformity Must be Numeric 1 3 Conformity Must have 2 decimal places 1 Weight Each data quality rule belongs to one of the DCAM data quality dimensions. 4 Conformity Must be positive number 1 Each data quality rule belongs to one of the DCAM data quality dimensions described earlier and can optionally have a weighting if it should have a greater or lesser impact on the net data quality score. Rules like these can be defined for each of the data fields in scope and they can then be used to derive an overall data quality score for each of the data quality dimensions. 5 Achieving regulatory compliance by improving data quality 2017 Datactics Ltd.

7 2.3 RegMetrics platform implementation For each data field in scope, data quality rules like the example above were implemented in the Datactics Data Quality Platform using Datactics FlowDesigner GUI to define the rules and Datactics Data Quality Manager (DQM) to automate the execution of data quality analysis and production of data quality metrics which are stored in the Datactics database. Inputs: Vendor Feeds Bloomberg Reuters S&P IDC Direct Exchange feeds Internal Risk data Financial Reference data Internal transaction data Web services Internal Documents Etc. Central Data Warehouse Replicated Central Data Warehouse Consumers: Trading Risk PnL GL Clients Regulators Etc. Data Quality Regulatory Reports For this particular client, data was already housed and served from a central data warehouse, however if the data had been housed in disparate silos then an aggregation and normalization process would have been implemented typically using a tool like Datactics for ETL operations. The client chose to run all data quality analysis on a duplicate of their central data warehouse. This approach enabled them to freely interrogate data without impacting the performance of the production system and provided a safe environment to test the effects of any remediation that may be required as a result of the data quality analysis. In addition to the production data quality metrics, Datactics was used to produce regulatory compliance reports for submission to Wolters Kluwer OneSumX. 6 Achieving regulatory compliance by improving data quality 2017 Datactics Ltd.

8 2.4 Data quality metrics While there are many excellent business intelligence and data visualisation tools on the market today, it s very difficult to use these by themselves to measure data quality of large data sets, since they are simply not designed to execute the huge numbers of computations necessary for data quality analytics on big data. What s required is an efficient platform for large-scale data quality measurement and generation of comprehensive data quality statistics which can be visualised in any business intelligence suite. For this particular project Datactics software automated the profiling of the data, generating data quality statistics which are stored in a report optimized database for easy access. What s required is an efficient platform for large scale data quality measurement and generation of comprehensive data quality statistics. We created data quality management dashboards in Qlik which consumed data quality statistics directly from the Datactics database without having to do extensive aggregate calculations which results in a very responsive and fluid user experience for exploring the information. Figure 1: Screenshot of Data Quality Manager Dashboard The dashboards provide the CDO complete visibility into data quality status of the measured data universe and allow navigation and filtering by the firm s data taxonomies that have been stored in the Datactics database. Furthermore, the dashboards also allow instant identification and examination of failing records which can be assigned to specific data owners for remediation. 7 Achieving regulatory compliance by improving data quality 2017 Datactics Ltd.

9 2.5 Data remediation & escalation Since each record is linked to metadata describing the source, owner, escalation procedure etc. it is then possible for the data quality platform to execute a remediation path based on the content of this metadata for example: Automatic remediation o Fixing the failing record with the Datactics built in cleansing methods for the data e.g. date format, instrument identifier, postal codes etc. o Passing the record to an external cleansing engine, e.g. proprietary software robots And if automatic remediation fails to fix the record, fall back to manual remediation. Manual remediation o Failing records are quarantined in the Master Record Manager where they can be interactively reviewed and resolved by a human o A work ticket can be automatically created in the issue management system e.g. ServiceNow, and assigned to the record owner for interactive review Establishing a system as described above has effectively set up a data quality firewall, whereby all data passing predefined quality criteria are allowed into the system, and data failing the quality criteria are rejected and held in quarantine. This means that there is minimal disruption to downstream users since the whole data on boarding process doesn t have to be stopped and restarted due to poor quality data. A further implication of linking data records (failing or otherwise) with metadata describing the source and owner, is that it then allows for the production of benchmark data quality statistics between data sources or vendors, and the historical data quality metrics allow subscribers to see if data quality from there chosen source is improving or degrading over time. 8 Achieving regulatory compliance by improving data quality 2017 Datactics Ltd.

10 3. Conclusion The data quality metrics for regulation or RegMetrics dashboards have created an open window of data quality for our Tier 1 global bank client, and offers the CDO valuable insights that steer data governance and data remediation actions. Our collaboration is still unfolding now armed with a database populated with near real-time and historical data quality metrics, our client is now investigating the possibility of using statistical predictive analytics for financial data quality identifying characteristics that could be predictive indicators of an impending change to data quality. Furthermore, Datactics is now investigating the possibility of applying machine learning to the data remediation component of its platform, so that when failing records are fixed interactively by a human, the system can learn from these actions and automatically apply them to similar failing records in the future. But perhaps the most significant implication of data quality metrics is that as more firms accurately and reliably measure the data they receive from third party vendors, the more pressure there will be on these vendors to improve the quality of the data they sell, which should have the effect of driving up data quality standards and/or reducing data costs, which will be beneficial for everyone. 4. About Datactics Datactics ( a market leader in data quality and matching software, is ideally positioned to meet the specific data requirements of firms operating in the financial sector as they prepare for emerging regulations. We provide sophisticated tools to help financial institutions get their data in order and quickly respond to new standards. We offer agile data quality and powerful matching software for reference, entity and instrument data. General Enquiries: Website: address: info@datactics.com Phone number: +44 (0) Achieving regulatory compliance by improving data quality 2017 Datactics Ltd.