MDM_Whitepaper_EDG_Finance

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ENTITY WHITE PAPER IS EFFECTIVE DATA GOVERNANCE A CHOICE OR A NECESSITY IN FINANCIAL SERVICES TODAY? WHAT IS DATA GOVERNANCE? Data Governance is the activity of co-ordinating people, processes and systems to ensure that the information that powers an enterprise is accurate and trustworthy. All enterprises rely on information from many sources, both internal and external, to feed critical business processes. Data Governance, if implemented effectively, can enable better decision making, reduced risk, regulatory and legal compliance, enhanced sales, enhanced customer satisfaction, business efficiency and reduced operational cost. Effective Data Governance plays a pivotal role in deriving value from the data the drives the organisation. WHY DO DATA GOVERNANCE PROGRAMMES SUCCEED OR FAIL? The probability of the success, or indeed the failure, of Data Governance programmes increases due to a number of factors. LACK OF AN IDENTIFIED BUSINESS PROBLEM A number of Data Governance initiatives fail because organisations implement them focusing purely on Data Quality without considering the context and scope of the business problem. Data Governance is too often implemented for the sake of Data Governance. Without a compelling strategic business driver that C-Level decision makers are aware of and care deeply about, the likelihood of obtaining Executive Sponsorship for a Data Governance programme is low. Furthermore, if the programme commences without clear definition of its objectives, board level backing will be finite as other, better specified projects compete for important resources; the programme will only progress if there are measurable and incremental results against strategic business drivers. It is therefore important to understand and define the specific business problems that a successful Data Governance initiative will address. In the main, in the banking industry, C-Level concerns relate to regulatory compliance, risk management, revenue generation, operational efficiency, decreased cost, customer loyalty, customer service and improved decision making. A specific business need might be to improve the identification of credit risk with respect to organisations with complex global ownership structures. In our experience, the foundation of this is rooted in data quality, master data management, hierarchy management and data governance principles. These contribute to customer onboarding and ongoing customer risk management processes. NS Alternatively, timely and accurate regulatory reporting is predicated on the ability to aggregate data in a consistent and meaningful form across many business units and geographies. Defining the business problems specify “WHY” an organisation is tackling Data Governance and what outcomes are expected. Individual Data Governance initiatives should align to business objectives – data quality processes, data stewardship, the technology utilised - all relate to “HOW” you get there. If an organisation allows process and technology to shape business decisions rather than the business priorities driving technology and process, then the business benefits hoped for from the Data Governance initiative will never be reached. 4 Conversely as a Data Governance programme incrementally tackles the identified business problems, it gains support from the business functions and momentum to extend its scope to additional areas.
IS EFFECTIVE DATA GOVERNANCE A CHOICE OR A NECESSITY IN FINANCIAL SERVICES TODAY? ENTITY WHITE PAPER OBTAIN EXECUTIVE SPONSORSHIP A passionate belief that data is the lifeblood of a modern organisation is essential at a very senior level if a Data Governance initiative is to be successful. Somebody at the top must own and care deeply about Data Governance and expect significant return on investment through its eventual implementation across the organisation. In order for Data Governance initiatives to be successful they require cross departmental thinking and organisational change and therefore need C-Level buy-in and leadership. Effective sponsorship at the right level in the business increases the probability of success with the Data Governance initiative. Executive level sponsors are more likely to fund projects that align with the strategic objectives for the organisation. In a banking context, one would expect the newly required (according to Dodds-Frank) Chief Risk Officer to be an Executive-Level sponsor, given the impact that accurate data has on the effective assessment of enterprise-wide risk. Support should also be forthcoming from other C-Level Executives including finance, marketing, customer service, retail banking, corporate banking and compliance, as each function is dependent on data to perform effectively. Effective sponsorship, however, requires a lot more than being an advocate for Data Governance and securing its funding. Effective sponsorship requires passionate leadership with a vision for business change, that the project is funded, and that you make those responsible for implementation accountable for realising the business benefits outlined in the business case. This awareness of accountability at the early stages of the Data Governance programme increases the probability of a successful implementation. BUSINESS CASE Executive-level sponsorship is more likely if the initiative is reinforced by a strong business case, with quantified Return-on-Investment. Executives must allocate resources to competing projects, and those with clearly identified benefits and value have more chance of moving forward. Furthermore, those projects without a clear business case are more likely to be cancelled or to be perceived as failures, simply because quantifiable business outcomes were not defined for the project at the outset. As with Master Data Management, Data Governance is an enabler of future value and its benefits can be realised across multiple business functions simultaneously, for example, finance, customer service, risk management and marketing. Example business cases might be expressed in terms of customer satisfaction, cross sell, up sell, operational efficiency, improvements to strategic decision making, or regulatory compliance. In the current post-recessionary climate, projects that progress also tend to be those whose value can be derived as a series of deliverables staged over time. ‘Big Bang’ initiatives where all the value is at the end of a multi-year programme have proven to fail all too often and therefore don’t even get off the starting blocks in the current climate. It is important to document, agree and continually measure the value of each business benefit, and when that value will be delivered. CONDUCT A MATURITY ASSESSMENT Before any Data Governance programme is initiated, you must first understand the maturity of the organisation in terms of existing policies, practices and processes. An assessment must also be made as to the desired/attainable future state (which may not be the perfect end state) but represents a sensible vision for Data Governance. This high level Data Governance Maturity 5
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