Delivering Corporate IT Projects; Do data quality policies and initiatives help or hinder?

Transcription

1 Delivering Corporate IT Projects; Do data quality policies and initiatives help or hinder? John Adler IRS PMI Meeting May 21,

2 These materials are Best practices and observations, developed in consultation with other Data Governance, Data Quality, Data, and Master Data experts, and based upon our own professional experiences Setting up a Data program is not rocket science, but it is complex, and these materials are meant to provide a flavor for how it is typically configured (your mileage will vary its more complex then it looks) These are not representative of the situation at the IRS, or any specific organization 2

3 Data Quality Situation Information is a critical business resource. Organizational complexity and the complexity of corporate IT landscapes are ever increasing. The amount of business information is also increasing exponentially. (40% projected growth in global data generated per year McKinsey May 2011) And so information becomes increasingly difficult to manage. Information Quality problems increase organizational risks, and impact operating efficiency, brand perception, financial results, and corporate strategy. And, there are so many Data Quality issues, that they can not be Completely solved. 3

4 Significant Data Quality Trends 1. The Government s transparency agenda s continues to drive increasing efforts around information quality and governance Cross Agency (Intra and inter agency) Government -> Citizen / Private Sector (Public Facing) Private Sector -> Government (Compliance) (eg- Dodd-Frank, Solvency II, Basel III) 2. Information Transparency and Predictive Analytics, have proven ROI s, and are dependent upon high quality information Quality data is a necessary requirement for these programs 3. Data of various levels of quality is being leveraged in many enterprises as part of Big Data initiatives, and needs to be understood/controlled so that it doesn t get treated as High Quality data 4. Enterprise IT & Data projects are frequently caught in the middle of a love-hate relationship between Business and CIO 4

5 Some examples of emerging data changes.. Legal entity identifier (LEI) Driven by macro economic risk management concerns The LEI proposal, sponsored by the US Treasury Department s Office of Financial Research and mandated by G20, would require that all companies implement a common set of identification codes in order to better monitor and manage counterparty risk. No way to analyze systemic risk or manage financial stability without a common identifier of legal entities Regulatory red flags (mandates for reporting, change nature of added value, IP ownership, commercial formulas, cost of implementation Will be approved this month, Global Adoption expected over next 3 years 5

6 Financial Firms have some of the greatest Data challenges.. After the 2008 Financial Meltdown, analysis of root causes Observations on Developments in Risk Appetite Frameworks and IT Infrastructure (Senior Banking Supervisors, December 2010) Defined the relationship between data management and the adoption of a risk appetite framework Poor data infrastructure hindered ability of firms to manage financial risks as market events unfolded Firms must adopt a risk appetite to mitigate systemic risk Firms must be able to monitor risk through access to accurate, comprehensive and timely data Adoption of a formal risk appetite and implementation of a data infrastructure are linked Must be able to share data across functions to create a composite view of operations Firms can t implement a risk appetite framework without data management (executive management is accountable) 6

7 Data Governance is a collaboration between Business and IT 7

8 Business driven Data Governance Bodies sometimes rush for results The Best Practice is to have a Business Driven Data Governance body set priorities and direction. Sometimes handing this strategic direction setting to Business people, has expectations mis-match and unintended consequences which the PM has to identify and help resolve. The best way to address this disconnect is to be aware of the Data bodies and activities in your organization, and understand how they operate 8

9 The PM is on the front line of many changes Focus on Information Quality improvement (new Policies, Objectives, Tools) Continuously improving data storage and retrieval technology is driving change into the enterprise architecture (Big Data / Hadoop, SOA) SDLC changes driven by iterative development processes (Agile) Evolving / increasing focus on adoption of Business Rules engines to facilitate adaptation to business change PM managing to the triple constraints (cost, time, scope / quality) On long duration projects, the constraints and technologies that you started with may not be the ones that you end with. IT & PM delivery risk management is tied to the known and manageable 9

10 The typical genesis of the focus on Data Quality Data related compliance issues; Inconsistent reporting data Increasing cost of maintaining consistent data and tracking it Business changes, new products, are delayed or more costly due to data concerns Data warehouse is a parking lot for data which is largely unused, and/or the data is of unknown value/quality Data quality problems; 29 different names for customer, contract, vanilla 12 different codes from client status, blue dye Company lists more US customers than US population 10

11 Data challenges are common; 20% of critical data is flawed at Large Companies (Gartner) 20% of ERP project budgets are spent on Data Standardization (Corporate Executive Board) 20%-50% of Data warehousing costs used for Data Quality (Gartner) 90% of the data collected by large corporations are not used for analysis (Giga Information group) 11

12 How are Data / Quality Programs implemented? Data Programs typically include one or more of these depending upon what adoption phase they are in, and the business priorities: Business case for Data Quality Formal Business Driven Data / Data Governance Council Data Policy / Standards Creation or Refresh Data Project Charter and Oversight Ownership / accountability refresh Required data documentation / reporting Required standardized tools Eg ETL, Data Modeling, Data masking, Data Quality, Reporting Standardized Data Storage patterns 12

13 How do Project Managers experience Data changes? Project: Rules / Policies / Methodology changes Architecture Changes Scope changes Stakeholder changes Typical rollout patterns; a) for new projects only (least disruptive), or b) rolled out to all projects (more disruptive), or c) infrequently they are rolled out to all systems regardless if they are subject to change (most disruptive). 13

14 Some Failure Modes of Data Governance (Data Program) and the Impacts on the PM and Organization Failure Mode Examples Lack of Executive Sponsorship Weak or ineffective Architecture and/or IT methodology organization Data Governance communications are unclear or poorly coordinated Data Program value proposition is not clearly understood and articulated Data Quality program does not meet its improvement objectives Master Data Maintenance Process is not acted upon / managed Impacts Terminal state if not addressed Business grabs the wheel and advocates changes without understanding of enterprise impacts The PM is surprised to receive notices that previously approved approaches / architectures and solutions patterns are no longer acceptable. Lack of clarity about data responsibilities on team and customer Lack of support for data activities. Dysfunctional state. Since most integration projects include a budget of ~20% for data cleaning, any dependency on quality data which isnt met will impact project Scope Master Data quality declines, project assumptions are violated. Scope impacted. 14

15 Things to remember.. When/if surprised by data issues you need to work the issues and the process. There is an escape hatch in there somewhere, your Data Stewards and the Data Governance team is there to shine a light on it. Data Quality and Data Governance is a Marathon, not a sprint Data Governance is not a silver bullet, it is there to help the enterprise improve and adjust. There will be compromises. The data teams knows better then most that you can t boil the ocean 15

16 Delivering Corporate IT Projects; Do data quality policies and initiatives help or hinder? 16

17 Thank you DataMgmt.biz John Adler Improving Data Quality and Information through Risk Disciplines and best practices. Phone: (202)