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My role as chief data officer

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A Presentation I gave to the Business Link North West Senior Management Team on My Role as Chief Data Officer

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My role as chief data officer

  1. 1. Data/M.I. Steering Group Workshop Presentation The Chief Data Officer: My Role Presented by Ged Mirfin Chief Data Officer, BLNW
  2. 2. “Bad data! I got bad, bad data. . . bad, bad data, and bad data's worse than no data at all!” Words and Music A. Marscher ©1996
  3. 3. “Through 2007, over 50% of data warehouse and CRM deployments will suffer limited acceptance, if not outright failure, due to lack of attention to data quality issues (0.8 probability).” T. Friedman Gartner Inc. January 2005
  4. 4. “Little wonder that one third of businesses have been forced to scrap or delay the introduction of a new computer system due to data problems… Business users are rightly intolerant of new systems that are delivered filled with rubbish data and may even fail to adopt the system. It’s like investing in a new sports car, filling it with the oil and fuel drained from your old vehicle, and then wondering why it fails to perform as it did on the test drive.” (source: PricewaterhouseCoopers)
  5. 5. “Many businesses blindly pursue costly CRM initiatives without understanding the challenges and costs involved.” Beth Eisenfeld, Research Director, Gartner.
  6. 6. Data Quality Data Quality refers to: 1. the quality of data. Data are of high quality "if they are fit for their intended uses in operations, decision making and planning" (J.M. Juran). 2. the state of completeness, validity, consistency, timeliness and accuracy that makes data appropriate for a specific use. (Government of British Columbia) 3. One industry study estimated the total cost to the US economy of data quality problems at over US$600 billion per annum (Eckerson, 2002).
  7. 7. The Gartner View •Biggest contributory factor in “outright failure” CRM is lack of process ownership. •The problem is nobody owns it. When CRM cuts across different departments it breaks down at the interface between different departments. There's no understanding of the end-to-end process. •No helicopter overview. •Therefore no CRM “grand strategy”.
  8. 8. Who owns Data? •Marketing •Operations •Brokers •IT •CRM Manager •M. I. Team •Business Improvement
  9. 9. Who has ultimate “ownership” of Data? •Everyone and No-One •Everyone has responsibility for their individual piece of the jig-saw •Devolved Control of the Management of Data is dispersed throughout the Business •Has led to anarchical and fragmented decision-making and ineffective quality control mechanisms •No one Individual or Team is in charge
  10. 10. Data Stewardship “Creation of a formalized management structure including the formalization of systems and processes and the establishment of standards, procedures and accountability for the processing of data throughout the business as part of the implementation of a programme designed to increase operational effectiveness, transforming data and its use into a highly valued “strategic asset”.”
  11. 11. “Many companies are spending millions of dollars on data warehousing, but many of these companies are not receiving optimum return on investments. Business professionals need to understand that data quality is not something IT can fix. IT professionals can help identify problems and suggest new ways to eliminate data quality issues. However, the business must be willing to own data, and change its processes to ensure data accuracy. " Scott Barnes, Director of the Data Services Practice at Collaborative Consulting
  12. 12. Chief Data Officer •Data Stewardship Programme led by Chief Data Officer (CDO) •CDO responsible establishing a high-level data governance structure with clearly laid out roles and responsibilities •Establishment of rules and policies for “data governance” •Formation of a dedicated Data Total Quality Management Team tasked with measuring and improving the quality of data including Total Quality Systems & Data Audit Manager •CDO is responsible for resolving conflicts across disparate groups and establishing enterprise standards on the use of data •When there are multiple approaches and Team Managers, Business/M.I. Analysts and Information Architects cannot seem to be able to agree on an approach, this person will step in to facilitate the best approach in the interests of the larger organization •Principal objective will be to drive greater value out of data through development of a well thought out data
  13. 13. Rationale •Companies have increasingly become aware of the value of data as a corporate •Management of data has become more visible and crucial. •With this new visibility, demand and importance of data, many companies have realized that they must better define strategic priorities for management and delivery of data throughout the enterprise, identify potential service users through the analysis of data, and significantly improve their performance through more effective use of data. •Increased recognition of need for a person who is responsible for crafting and implementing data strategies, standards, procedures and accountability policies. •As IT systems grow in size, complexity and cost, it will be increasingly critical to maintain oversight of data.
  14. 14. Data Governance • “Encompasses the people, processes and technology required to create a consistent, business view of an organization’s data in order to: • Increase consistency & confidence in decision making. • Establish consistent information quality across an organization. • Maximize the creation of added value opportunities from the potential use and exploitation of data. • Designate accountability for information quality.” •The sheer scale of investment in building and maintaining enterprise-wide data architectures and integrating their applications and systems across the Business means it is absolutely vital to create standards, policies and procedures for data. •Data Governance initiatives improve data quality by assigning responsibility to Chief Data Officer solely for data's accuracy, accessibility, consistency, and completeness, among other metrics.
  15. 15. Data Strategy •Identifying ways to improve current data whilst ensuring all appropriate data is available for campaign selections and modelling and segmentation including a deep understanding of customer characteristics, attitudes & behaviours and channel preference in order to drive development of customer strategy designed to reduce attrition of service usage and drive end user recruitment. •Ensuring all marketing campaigns and activities are measured and reported effectively and in a timely fashion such that a continued improvement in value creation is achieved. •Searching the market for external data that can further enrich current dataset and provide further insight into prospects whilst actively managing a test programme with data/list suppliers. •Establishing a programme for managing suppliers. •Monitor both the quality of this data, how it is imported and the impact that data imports will have on overall internal data volumes/counts and overall data quality metrics. • Implementing effective control procedures to ensure that imported data conforms to internal rules and standards and in accordance with standard operating procedures. •Determine which types of data are critical to the organization and therefore warrant heightened management and apply change management rules and workflow processes to highly managed data •Measure the impact of data overwrites/uploads on M.I. Reporting. •Establishing submission and review processes to filter which user changes and record amends and up-dates should be accepted/saved.
  16. 16. Communicating the Benefits of Data as a Corporate Asset “Clearly articulating and communicating roles and responsibilities of marketing, client service managers, consulting and external partners in the provision and maintenance of customer and prospect data” • Selling the vision for data for the business going forward. •Evangelizing the adoption of DTQM measures internally by publicising Data Quality Issues and producing Regularised and Ads-Hoc Reports on the Quality of Data. •CDO will develop effective working relationships with key data users and the ICT team to develop performance management solutions. •Actively pursue collaborative and cohesive working relationships with all internal personnel.
  17. 17. Data “Total” Quality Management •Managing the downstream impact of data improvement strategies and the replacement of bad data with good data on teams across the business. •High level understanding of current “data blockages” and how bad data is affecting delivery of BLNW services within key functional areas of the organization is critical to its continued success or failure. •Ensuring the successful implementation and effective execution of Data Quality Management Initiatives and projects will involve the measurement of progress and demonstrable by using simple metrics to establish and sustain continuing high levels of Data Quality and Accuracy - Measuring the success and progress of the initiative such as a reduction in the level of error percentages. •Identifying issues early and escalate support when necessary, setting clear expectations about what needs to be delivered and quickly adapting plans in response to change.
  18. 18. The Gartner View “The challenge of poor data quality presents a vicious circle. If business users don’t trust the existing data in a system, they take less care themselves when entering new information, which only compounds the data quality problem. With data being recognised as an organisation’s second most valuable asset, and poor data quality losing organisations up to a quarter of their revenue, this is a topic that cannot be ignored.”
  19. 19. Maintaining Data Integrity
  20. 20. Policies and Procedures • Need to take the organization out of the reactive mode and establish a raft of data policies and procedures. • Will address the roles played by IT, Marketing and Operations on issues such as data ownership, data sharing, and data privacy. • Formulation of a Data Governance Charter, Definition and Prioritization of activities, establishing Rules and Procedures, Standard Documents and Forms and detailed roles and responsibilities for the handling of data. • Data standardization - a business rules engine that ensures that data conforms to quality rules • Monitoring - keeping track of data quality over time and reporting variations in the quality of data. Software can also auto-correct the variations based on pre-defined business rules. • Maintaining Data Integrity - ensuring that end-users are prevented from breaking the CRM System’s business rules • Ensuring that data is "whole" or complete – preserving the condition in which data is maintained during any operation such as transfer, storage or retrieval
  21. 21. Steps to address data quality • Define data quality in a broad sense, establish metrics to monitor and measure it, and determine what should be done if the data fails to meet these metrics. • Undertake a comprehensive data profiling. • Incorporate data quality into all data integration and business intelligence processes from data sourcing to information consumption by the business user. • Data quality issues need to be detected as early in the processes as possible. • Presentation data that meets very stringent data quality levels. • The level of data transparency needed can only result from establishing a strong commitment to data quality and building the processes to ensure it. • The poor design of user interfaces is another source of bad data quality. Poorly designed data entry screens are frustrating to the user.
  22. 22. Failure to Implement Data Total Quality Management: the Risks •Recent studies have indicated and have clearly proven that bad data costs money; results in poor and uninformed decision-making and eventually missed business opportunities. •The cost of poor data can equal anywhere from 10-25% of the total operating costs of an organization. •Difficult challenge is maintaining high data quality on an ongoing basis. •Contact data, one of the most critical elements of a CRM system, typically erodes at a rate of 33% per year. Without proper attention, the data will inevitably become incorrect, unusable and ultimately untrustworthy. •We should not underestimate how critical the need for high quality information is to the business and how bad data really affects the business. •Poor Quality Data is puts Operational goals at risk! Operational data issues are hindering the delivery of Key Targets and Objectives. •Poor Data Quality is seriously lengthening workflows and increasing timescales for execution “easy/normally straightforward” tactical Marketing/Broker/Relationship Management & Building and Business Development activity. •It has also resulted in an engrained and pervasive lack of user confidence in and beneficial usage of CRM system. •CRM is increasingly frustrating users promoting conflict & disharmony amongst Teams and individual Staff Members as they wrongly accord blame •Poor quality data leads directly to the poor planning, management and delivery of marketing campaign activity resulting in an inability to “target” businesses that should be correctly targetted

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