IPL Proposal Project: Date: 6th October 2015 Issue Number: Issue 1 Customer: Crown Commercial Service Page 1 of 8
Copyright notice This document has been prepared by IPL Information Processing Limited ( IPL ) and, subject to any existing rights of other parties, IPL is the owner of the copyright of this document. No part of this document may be copied, reproduced, stored in a retrieval system, disclosed to a third party or transmitted in any form or by any means, electronic, mechanical, photocopying, recording or otherwise, without the prior written permission of IPL. IPL Information Processing Limited 2015 Contents 1. Data Governance 3 2. Detailed Service Definition 4 2.1. Data Governance as an Enabler 4 2.2. IPL Data Governance Framework 4 2.2.1. Foundation 5 2.2.2. Implementation 5 2.2.3. Organisation 5 2.2.4. Direction 6 2.2.5. Reporting and Assurance 6 3. Benefits of the Data Governance service 6 4. Keywords 7 IPL Information Processing Limited, registered in England and Wales with registered number 01418818 Page 2 of 8
1. Data Governance IPL offers a Data Governance Service based on its Data Governance Framework (DGF). This service helps organisations put governance in place in order to manage their data as a critical business asset, analogous to the governance controls that will already exist for the management of other corporate assets such as finance, people and property. The framework defines the components required to establish formal asset management for data, focusing on the people and process aspects that are required to successfully manage and deliver any information and data management function. The DGF is designed to quickly establish the necessary roles and responsibilities, reporting in to existing governance structures. Data governance, when done correctly, is not a job creation scheme, but provides formal support and recognition to individuals already fulfilling what are in fact data governance roles. A key feature of the framework is to provide direction to an organisation s workforce ensuring that data is being managed as an asset across the enterprise. This includes establishing the core data management principles from which policies, processes and procedures can be derived. These people and process aspects provide the control mechanism for all data management activity and enable the organisation to assess its maturity, continually improve its performance, and report on performance at board level. Data governance puts in place the foundations on which to successfully deliver data management functions such as data quality, master data management, business intelligence and analytics, and to support initiatives such as big data, cloud and digital transformation. The diagram below illustrates why data governance is required across the complete Information Value Chain, that is, the transition and exploitation of raw data into actionable intelligence: Figure 1 Data governance and the Information Value Chain IPL consultants are Enterprise Information Management (EIM) professionals with significant data management expertise; they possess accreditation and certification from renowned data management organisations. All consultants are members of the Data Management Association (DAMA) and have achieved Certified Data Management Professional (CDMP) certification. In recent years the focus of our work has moved increasingly towards cloud based services and solutions. Page 3 of 8
2. Detailed Service Definition 2.1. Data Governance as an Enabler Data governance is the exercise of authority and control (planning, monitoring, and enforcement) over the management of data assets. The data governance function guides how all other Data Management Functions (DMFs) are performed 1. Therefore data governance is the critical enabler to all other DMFs. This is because data governance establishes both the formal roles required to manage data as an asset, and the principles and policies required to embed data governance as business as usual within an organisation. To undertake any DMF requires ownership of the data as decisions will have to be made on how the data is managed. Without the ownership and policy in place issues do not get resolved as there is nothing in place to stop buck passing. Figure 2 below illustrates how data governance sits at the centre of all DMFs and is derived from a combination of IPL consulting experience and DAMA best practice guidance: Figure 2 Data Management Functions As can be seen, there are six core DMFs underpinned by four further DMFs. This is because any of the core DMFs will have to address the lifecycle of data, security issues, compliance issues and require data management software tools. However, all the DMFs require data governance to be established to some degree and cannot be effective without it. 2.2. IPL Data Governance Framework The IPL Data Governance Service delivers the components of the Data Governance Framework (DGF) typically in order to provide organisations with the data governance wrap around one or more of the core Data Management Functions (DMFs) shown in Figure 2. The DGF comprises of five components; Foundation, Implementation, Organisation, Direction, and Reporting and Assurance. The first two components consist of one-off activities required to establish Data Governance. The remaining three components then provide the wrapping around the DMFs, controlling and monitoring the execution and outcomes. This is shown in Figure 3 below: 1 Definition of data governance, Data Management Association (DAMA) Page 4 of 8
Page 5 of 8 Figure 3 IPL Data Governance Framework Each component addresses a different aspect of the framework. Every data governance implementation is different, depending on the aspirations of the organisation concerned. The following paragraphs are an indication of the services IPL deliver. 2.2.1. Foundation The first steps in any data governance implementation involve identifying the existing governance structures and recommending a future state, whilst securing buy-in for the change. Key is identifying the scope of the initial phases of implementation. Data management maturity assessment Strategy for implementing data governance Target Operating Model complementing existing governance structures Implementation roadmap for data governance and the required DMFs Blueprints (various models) mapping the relationships between data, systems, processes and people 2.2.2. Implementation This component establishes the programme for implementation, usually requiring a small team to be set up. Actual roles required will be dependent upon the DMFs in scope. At this stage establishing effective communications to the workforce is essential. Data Governance Team in place (with coaching and mentoring from IPL) Data Governance Office in place Online repository/knowledge base established for all things data governance related Communications strategy and plan Core communications material aimed at different levels of stakeholders 2.2.3. Organisation This component addresses the people aspects required, formalising a number of roles that already exist across the organisation. Critical is to embed accountability for data across the organisation which is achieved via a
blend or ownership and stewardship data stewards being those with subject matter expertise in specific domains of data. Data Owners, Stewards and Custodians identified and trained New role descriptions agreed and in place Data Governance Council established with clear Terms of Reference Executive support/championing of data governance evident Data Working Groups actively improving data management practices Data Governance Office organising working teams 2.2.4. Direction This component provides the overarching direction, to those with data governance roles as well as all others within the workforce. Focus needs to be on drafting, agreeing and embedding policies that can be enforced and measured, as this help to encourage the required behavioural change. Set of data management principles Set of policies derived from the principles and covering the desired DMFs Set of process models and, if required, subordinate procedures defined Applicable data standards identified Annual data management training provision with suitable learning also embedded in induction 2.2.5. Reporting and Assurance The final component in the DGF is designed to ensure that the organisation evolves and grows its data governance regime. The Data Governance Council and wider governance bodies require assurance that data is being managed as an asset, with regular maturity assessment conducted to identify progress and/or areas of weakness. Data governance dashboard reporting on KPIs Maturity assessment reports Lessons learned and best practices adopted 3. Benefits of the Data Governance service Some of the benefits of IPL s Data Governance service are: Organisations manage their data as an asset, with recognition across the workforce of the value of data to the business. Establishes governance mechanisms to ensure that all data management functions within an organisation are coherent and effective. Ownership for all important business data is in place, with those responsible for its management trained in best practices. A scalable solution that can be expanded in scope to bring in other data domains or business areas. A solution that utilises and formalises existing roles rather than recruitment of additional resources. More efficient management of risks associated with data. Improved compliance with regulatory regimes. Page 6 of 8
4. Keywords Data governance, data governance framework, data management, data management functions, data quality, master data management, data owner, data ownership, data steward, data stewardship, data custodian, data custodianship, data assurance, data principles, data policy, data processes, data standards. Page 7 of 8
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