Practical steps for developing Data Strategy and Governance

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Course Purpose:

With Data at the forefront of business, the need for organisations to create a comprehensive Data Strategy is greater than ever, with both the increase in data regulations and the focus on data driven business strategies.

However, creating an Enterprise data strategy and supporting data governance model can be a formidable task.  Often, it’s difficult to know where to begin, and how best to prioritise efforts with the large number of stakeholders and competing initiatives.

Data is at the heart of all organizations, like blood flowing through its arteries and veins.  However, all too often Information is not professionally managed with the rigour and discipline that it demands.  Nonetheless the implications of poorly managed information can be catastrophic, from legal and other regulatory sanctions ultimately to business collapse.

Professor Joe Peppard (European School of Management, Cranfield) summed it up when he said:

“the very existence of an organisation can be threatened by poor data”.

This course will provide concrete practical approaches to get you started on your Data Strategy, the typical contents of a Data Strategy, and the ways in which your supporting data governance framework can be organised.   

Learning Objectives

Level set understanding & terminology:

    • Understand the key components that constitute a Data Strategy.
    • Learn about the need for and application of Data Asset management and Governance for different categories of challenges
    • Understand the Data Asset management and Governance implications of a variety of regulatory acts and how to prepare your organisation for compliance.
    • Learn how to create a case for obtaining business buy-in for a data strategy.
    • Explore a Business focused Data Governance framework and understand how it aligns with other architecture frameworks
    • Understand the critical role that Data Governance plays in the Information disciplines of Master Data Management and Data Quality management.
    • Understand the detailed Data Governance roles together with the activities required for them – including Data Owner, Data Steward and Data Custodian.

Pragmatic Learning

    • Learn the different motivations for Data Asset management and Governance and how best to implement DG approaches
    • Develop a set of usable techniques that can be applied to a range of information management challenges
    • Learn the best practices for managing Enterprise Information needs
    • Understand the different operating models for Data Governance & determine which is the best fit for your organisation
    • Learn how to create an actionable road map to implement your data strategy.
    • Understand how to identify the additional activities that are necessary to support the data strategy.

Who Should Attend?

This course is intended for personnel involved in Data Asset management and Governance, Master Data Management and/or Data Quality, initiatives including:

    • Information Managers,
    • Information Quality Practitioners,
    • Executives,
    • Technology Leaders,
    • Business Technology Partners,
    • Business Analysts,
    • Enterprise Architects,
    • Information Architects, and
    • Data Architects.


Course Topics

Components of a Data Strategy

What is the scope of the data strategy & where do I start?

Ultimately the strategy should cover ALL data and Information across the organisation, we should recognise that the implementation of recommendations will need to be prioritised.   

If there are any Business areas or types of data that are out of scope, this should be clearly stated, and the rationale for their exclusion made clear.

For example, unstructured data should not be out of scope for ever, since for many organisations the breadth of the people impacted will be enormous, plus “records” management has a legal obligation.  So, for example, it may be that records management isn’t the first area implemented in the transition steps of the roadmap, but for sure it must be covered.

Building Blocks of a Data strategy & Architecture

Understanding the business strategy & motivation

Current state maturity assessment

Target state maturity assessment

Architecture components & pre-requisites

Implementation roadmap

Establishing Goals & Gaining Buy-In

Motivation and drivers

What is the compelling business reason that has made the company decide to embark upon a Data Strategy?  The case for change needs stating here, e.g. there is data dependency for achieving core business strategies for customer growth/cross sell etc, and real stories of what has happened in the recent past.  We should include some facts and figures (how many breaches, manhours to respond to regulatory requests for information, customer complaints etc)

Internal factors

Here we need to list the internal factors such as efficiency, duplicated effort, reliability of information, lack of trust in data, agility to respond to change, desire to “go digital”, morale amongst knowledge workers, etc.  Incidents & case studies should be cited.

External factors

What are the market forces and regulatory factors driving the need to change: Competitors, need to offer wider range and platforms for services, mobile services, GDPR, FoA, etc

Communication

Communication of the approaches for developing a strategy & effectively communicating needs and expected return on investment (ROI) to senior stakeholders

Data Management Maturity Assessment

A maturity / current state assessment, together with a statement of the required target end state.  The maturity assessment should cover the Data Management Disciplines AND the Organisational Enablers for data management.

Data management maturity assessment of the disciplines of data management.

This may focus on a few specific areas, vs covering all 11 of the DAMA Data Management disciplines.  The core areas of Governance, Modelling, Quality, Metadata, Master data and Architecture will probably suffice.

Notwithstanding this, a strategy should at least mention on the other disciplines.  Security (classification), Data Integration, Document Management etc.

Maturity for Organisational Enablers of Information Management

In addition to assessing the level of maturity within each of the disciplines of Data Governance, there are 6 other “enabling” aspects of capability building which should be assessed.  Along with the detailed maturity components these are considered in the creation of a roadmap for improvement.

People:  Relates to the human side of Information Management, looking at how people are trained, measured, motivated and supported in IM related activities.  Organisations that motivate staff to think about information as a strategic asset tend to extract more value from their and overcome shortcomings in other categories.

Executive Sponsorship/Policy:  The message and support shown to the overall program and individuals from senior leadership.  The assessment considers whether individuals are required to administer and maintain information assets appropriately and whether there are consequences for inappropriate behaviours.  Without good policies and executive support, it is difficult to promote good practices even with the right supporting tools.

Technology:  The tools that are provided to teams within each discipline to properly meet their Information Management duties.  While technology on its own cannot fill gaps in the information resources, a lack of technological support makes it impractical to establish good practices.

Compliance:  Surveys the external Information Management obligations (if any) of the team.  A low compliance score indicates that the organisation is relying on luck rather than good practice to avoid regulatory and legal issues.

Measurement:  Examines how the organisation identifies information issues and analyses its data.  Without measurement, it is impossible to sustainably manage the other aspects of the framework.

Data Management Processes / Practice:  Considers whether the organisation has adopted standardised approaches to Information Management.  Even with the right tools, measurement approaches and policies, information assets cannot be sustained unless processes are consistently implemented.  Poor processes result in inconsistent data and a lack of trust by stakeholders.

Data Governance: Managing people, Organisation & Process

In an effective data strategy, we must define how the data asset will be managed.  This section defines the recommended Target Operating model for Data Governance covering:

Steering and Governance

The organisation structure for data governance is recommended here. This will include the reference for a Governance model e.g. Federated versus Centralized versus Hybrid

Charters or terms of reference for steering group(s) are developed and the recommended constitution of each group produced.

Sponsorship, communication (including the role of an executive sponsor) will be addressed here.

Roles & Responsibilities & People Capabilities

The roles that are necessary are highlighted here.  Detailed development of role activities & the capabilities for core Data Management roles may be covered in a strategy.

Importantly this section clarifies the roles of Data Owner & Steward & Custodian.

Data Management Process

A strategy should tie in the Change Management Process / Software Development Process and the data touch points during the SDLC.

Importantly the Data related activities and artefacts should be referenced here.

Principles & Minimum standards for Data Governance

The principles for data management with rationale, implications minimum standards and metrics.

Prioritising Business critical data & capabilities

Defining & managing the business-critical data & the capabilities required for their management.

Building the right technical architecture for the data needs

Recommending the overall Technical Data Architecture for actioning the priority needs of the strategy.  Here we would typically refer to categories of Data Management tools that are usually used during a data improvement program.  These may be technical data management tools and non-technical like data dictionary, data governance, data modelling, process modelling, etc.

Defining an actionable roadmap

Success metrics

From the Principles & Minimum standards, quantifiable success metrics can be developed.  Examples will be used to illustrate this.

Priorities & Quick wins

Business initiatives and priorities that are used in the formulation of the roadmap and transition steps. In particular, the transition steps will be aligned with business initiatives.

Roadmap, dependencies and transition steps

Roadmap of the recommended activities to move the data initiative forward. There may be specific business teams who are keen to be early adopters.

The overall roadmap must make it clear that there will be dependencies with some activities, for example to undertake XYZ Master Data Management, a minimum viable Data Governance process and responsibilities must be established for area XYZ.

The overall “Roadmap” is made up of Transition steps which can be bundled into Transition projects.  The key consideration here is that the most successful transitions are where they are aligned with business initiatives and are not simply “data projects”.

Culture, Communication, Sustainability & Education

Development of a communication plan regarding the data strategy.  The communication plan needs to have at least: Audience, Message, Method, Frequency.

Development of an education plan to raise Data Management competencies across the organisation & ensure the sustainability of the strategy.

Funding model

Recommendations on funding approach for Data initiatives.

Additional activities to support the strategy

Every Data strategy will have several additional activities necessary for its success, these include:

Identify candidates for roles

The roles, responsibilities, and the skills necessary to undertake the Data Management roles will need to be developed.  The identification of named potential candidates for the necessary roles must be undertaken by, particularly with involvement of the HR department.  The consulting team can provide mentoring and guidance if required.

Determine data owners & stewards

This is a specific subset of the item above, i.e. to identify named candidates for data owners and stewards.  A Business Level Conceptual Data Model is an essential step in determining data subject areas for which Data owners should be appointed.

Assess current roles and skills, perform gap analysis

This is a supporting activity for the task above (identify candidates for roles).  The current level of skill attainment (for each of the skill types identified in the skill / roles descriptions) will need to be assessed.

Identify training required to address gaps

Identify the types of development required by role & category of user to address gaps in skill sets.  will need to arrange the development (including training) that is needed to address the Data Management skill gaps identified.

Brief and mentor Data owners

To make Data Governance a reality & start a real roll out of DG, it is crucial to provide tailored training and mentoring for Data Owners and Stewards. This is a subset of the task above and applies to those staff whose role in DG is ‘Owner’ or ‘Steward’.

Define Data Subject Areas & develop Conceptual Data Model

Define the Data Subject Areas and conceptual data model (CDM).  This is to support several of the Data Governance standards that will be produced and to provide the means of agreeing data areas & data owners for governance.

Determine & prioritise business areas for Data Governance rollout

Determine the Business areas and / or the initiatives to prioritise and sequence for the rollout of Data Governance across the organisation.

 

Kouluttaja:

CHRIS BRADLEY

Information Strategist, Independent Advisor & Trainer. Vice President Professional Development at DAMA International

Christopher Bradley has spent 38 years in the forefront of the Information Management field, working for International organisations in Information Management Strategy, Data Governance, Data Quality, Information Assurance, Master Data Management, Metadata Management, Data Warehouse and Business Intelligence.

He is VP of Professional Development for DAMA-International, the inaugural Fellow of DAMA CDMP, past president of DAMA UK.  He is an author of the DMBOK 2 and author & examiner for professional certifications.



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