Transform your Data Office to adopt a Vision and Value-Driven focus for Data Managers

Executive Summary

This webinar covers the data value realisation domain and provides an overview of the content from a recent webinar. The webinar focused on data-value-driven strategy, the responsibilities and skills required for the Chief Data Officer role, and the importance of implementing and using data strategies. Howard Diesel covers adaptive data strategies, strategy planning and implementation, and strategic alignment models. He also discusses valuation models, measurement frameworks, and the alignment of business value and data management.

Webinar Details

Title:Transform your Data Office to adopt a Vision and Value-Driven focus for Data Managers
Date: 20 March 2024
Presenter: Howard Diesel
Meetup Group: African Data Management Community Forum
Write-up Author: Howard Diesel

Data Value Realisation Domain

Howard Diesel opens the webinar by noting that he will focus on the Data Value Realization domain and move from a plan-driven to a vision and value-driven approach in data strategy. The presentation will be divided into two parts, one focusing on the fundamentals of the data office and the other on the data office transformation or moving from plan to value-driven. A summary of the presentation, which consists of approximately 290 slides, has been created on LinkedIn, allowing for downloading of a PDF summary.

Figure 1 Data Value Realisation Domain

Figure 2 LinkedIn PDF Download

Overview of Webinar Content

Howard provides an overview and explanation of important topics related to ISO, SC40, IT governance, data governance, data quality, EDM Council, and the data office Roi. Contact details such as LinkedIn, WhatsApp, and Telegram are provided for further inquiries. Howard clarifies critical abbreviations and acronyms used. He also discusses the work of the EDM Council. He highlights the importance of publishing the EDM Council data office Roi Playbook or Word documents, which are available upon request.

Howard covers the concept of DVR (domain that NDMO setup) and data value realisation, which is identifying the financial impact of enterprise data in terms of increasing revenue, reducing costs, and mitigating risks. Howard explains that data value realisation encompasses revenue generation, cost reduction, risk mitigation, and customer value propositions. Lastly, Howard discusses ROI, a financial metric used to evaluate and compare investment profitability.

Figure 3 Howard Diesel Contact Details

Figure 4 Data Management Program Advisor

Figure 5 Data Office ROI – EDM Council

Figure 6 Critical Abbreviations

Data Value-Driven Strategy

Howard stresses the importance of being “data value-driven” instead of “data-driven.” He argues that it can take several years to become fully data-driven, but an organisation can prioritise data value realisation from the beginning. Howard criticises the effectiveness of creating policies and procedures around data if executives do not understand their value. To help businesses understand data value, Howard mentions various frameworks, such as EVM, EV, and TADS.

Responsibilities and Skills of Chief Data Officers

ISO, which stands for International Society of Chief Data Officers, is an organisation that caters to those who have been promoted to the role of CDO (Chief Data Officer) within their respective organisations. CDOs are responsible for building and executing a data strategy, dealing with cultural issues, and managing data ROI. They develop an enterprise data vision and value proposition, and their primary focus is on the value of data and business challenges, leading to the development of data initiatives and standards.

Figure 7 CDO (Chief Data Officer or Head of Data Office) Job Description

Figure 8 Summation of Responsibilities

Figure 9 Primary Responsibilities

Responsibilities of a Chief Data Officer

A Chief Data Officer (CDO) establishes and implements enterprise data management, change management, and decision support capabilities. They need to have a comprehensive understanding of business and financial literacy to apply financial models in calculating the value of assets. CDOs are also responsible for leading the development of enterprise analytics, AI, business intelligence, data visualisation, and data storytelling. Additionally, they need to measure the operating model and efficiencies of procedures and policies in the data office, ensuring that data assets are well-managed, shared properly, and compliant with regulations.

Chief Data Officer Skills and Qualifications

A Chief Data Officer (CDO) is responsible for managing and leading data-related initiatives within an organisation. The role of CDO Gen 2 includes IT knowledge, machine learning, data knowledge, leadership, and management skills, whereas CDO Gen 1 focuses on sorting out data management for organisations. Ensuring reliable and trustworthy data before conducting analyses is crucial; alignment with business strategy is a key responsibility. The minimal qualifications for a CDO include certifications like cdmp, cbip, and cpmp, business management experience and data management expertise. Knowledge of statistical and data mining techniques, as well as graph analysis, can be beneficial.

Figure 10 Knowledge, Skills and Abilities

Figure 11 Minimum Qualifications

Figure 12 Minimum Qualifications zoomed in

Adaptive Data Strategies

Adaptive data strategies are becoming increasingly important in contrast to traditional static strategies, which are becoming outdated more quickly due to the rapidly changing nature of the data landscape. Leaders find that plans often need to change significantly after development, leading to failure to achieve strategic objectives. Only 38% of enterprises believe they are agile enough to change quickly, highlighting the need for adaptive strategies. The concept of a strategy sprint involves executing strategies as early as possible and responding to changes as they occur. Embracing uncertainty is an opportunity to reassess and modify strategies in response to changing conditions. To ensure strategies are aligned with customer needs and preferences, crowd-sourcing is recommended as a strategy for involving customers in developing value propositions.

Figure 13 Strategic Planning Essentials

Figure 14 Adaptive Business Strategy Practice

Strategy Planning and Implementation

When it comes to strategy planning, it’s important to use crowd sourcing and ideation, customise planning activities, assign ownership to initiatives, perform measurements and pressure test initiatives, leverage real-time insights and analytics, continually scan for changes in the business environment, distribute decisions and strategy making through crowdsourcing, utilise sprints for continual strategy planning, experiment with different outputs and options, and be cautious of lock-ins when implementing distributed data governance models like data mesh. By following these common-sense actions and strategies, organisations can build minimal viable strategies that are effective and adaptable to changing circumstances.

Figure 15 Common-Sense Actions

Figure 16 Focus on a MINIMALLY VIABLE STRATEGY (MVS)

Figure 17 Adaptive Strategy Building Blocks

Data Strategy and Feasibility Study

Howard notes that new technologies significantly impact data governance models, making it necessary to consider their relevance and necessity. Building data products without a data mesh is possible, but customisation is required to make them practical and relevant to business units. Before initiating any data-related project, a feasibility study is a must to ensure its viability with available resources and commercial considerations. Clear ownership of data initiatives is crucial to avoid challenges and align cascading plans across business units.

A data strategy aligned with business use cases and owners, focusing on KPIs and pressure testing, is necessary. It is essential to consider technological, political, economic, social, trust, regulatory, and environmental factors in strategic planning. Gartner’s approach to trend analysis highlights the need for adaptive and minimally viable strategies. Finally, the path principles should be considered when building a data strategy.

Figure 18 Customise planning to meet participants where they are

Figure 19 Feasibility Studies to make sure resource capability

Figure 20 Be clear about who owns WHAT

Figure 21 Cascade plans side to side, not just top-down

Figure 22 Focus KPIs on Key Strategic Assumptions

Figure 23 Pressure-test plans against a limited set of future scenarios

Importance of Implementing and Using Data Strategies

During a training session, Howard observed that only 10% of attendees had a clearly defined data strategy, indicating a lack of awareness and utilisation of such strategies. He emphasises the importance of regularly referring to the data strategy when making decisions about data initiatives, as very few people reference the strategy in their decision-making process. A story about a customer with an impressive digital strategy is also shared. Still, during a use case ideation session with the business, it was discovered that most people had never heard of the strategy. Howard introduces the concept of the “principle of the path,” which is described as the tendency to make bad decisions or not pay attention to certain aspects until a critical situation arises, leading to a realisation of being in a difficult position financially or professionally.

Figure 24 Principles of the Path

Principles and Direction

It is important to follow principles in the family to avoid trouble. Howard notes that we may not realise we are lost until it is too late, and it is crucial to reassess and change course to stay on the right path. To determine if we are lost or aligned with our destination, we should question if our actions are appropriate and understand our current position and strategic alignment.

Figure 25 Principle of the Path

Strategic Alignment Model

Howard emphasises the importance of strategic alignment at a business level, which includes vertical and horizontal alignment. He highlights the need for data strategy to align with data quality, metadata, data governance, and data management strategy. Howard mentions a Strategic Alignment Maturity Assessment and a Strategic Alignment Maturity Model, emphasising the importance of aligning data strategy with digital strategy, technology, and business strategy. Howard introduces a strategic alignment model diagram from the DMBoK, revealing the nine areas that need to be assessed for alignment, such as business scope, governance, IT, architecture, processes, and skills. Continual reassessment is also emphasised to ensure alignment with the business’s direction.

Figure 26 Strategic Alignment Model (SAM) & Amesterdam Information Model (9 Cell)

Figure 27 Mapping the Organisational Strategy

Aligning Business Value and Data Management

High-level executives often face a dilemma between focusing on business value and data management, which can create tension and lead to potential imbalances. When data management is solely focused on compliance, it can be perceived as an audit department, while an exclusive focus on business value can impede scalability. Striking a balance between revenue generation and compliance, known as the “sweet spot,” is essential to achieving progress and value. Existing data management departments face challenges in demonstrating their value to the company and being seen as a hindrance rather than a help.

Figure 28 D-VD Playbook Balances Competing Demands

Figure 29 The Roadmap to Aligned Direction & Mature Engineering

Data Strategy and Business Alignment

One of the major challenges faced by businesses is the lack of enough people to support their strategies. Additionally, international and national regulations pose significant challenges. Whether it is possible to focus on regulatory compliance and still achieve business growth, to align the data strategy with the business objectives, a set of use cases should be chosen to deliver on the strategy, allowing for incremental progress. Vertical alignment involves assessing digital strategy, potentially using PowerBI for assessments.

Figure 30 DMO – Hurdles to Overcome

Business Maturity and Strategy Planning

The high variance between business and DMO beliefs may indicate disunity and architectural problems. Therefore, analysing the maturity path, including business efficiency, effectiveness, and transformation, is crucial. The goal should be continuous strategy and planning, reviewing every 100 days, and executing use cases. The horizontal strategy should include a data management strategy covering all DMBoK areas, including data architecture, modelling, quality, and classification. It is important to ensure that these areas align and stay connected.

The data value realisation framework should prioritise use cases and connect to data strategy and BI. The process flow should include a 100-day Sprint encompassing ideation, feasibility, data strategy, oversight check, and implementation. Moreover, the implementation should include data quality checks, issue remediation, asset cataloguing, advanced analytics, and BI building.

Figure 31 Data-Value Driven Direction – Alignment

Figure 32 Business, Information and Technology

Figure 33 Alignment Maturity Analysis

Figure 34 Replacing IT with Information Management

Figure 35 NDMO Data Management Functional Areas

Figure 36 Data Value Realisation = Prioritisation Framework

Strategic Alignment Model and Assessment

The Strategic Alignment Model is a framework with nine key areas for effective data management. These areas include governance, partnership with the business, skills of data people and business people, alignment with cyber security strategy, scope and architecture, communication, understanding value measurement, and performance measurement. Level four communications involve bonding and unified competency partner value governance managed across the organisation to enable and drive business strategy. The maturity assessment focuses on the capability of the data management office to manage data with a focus on creating data deliverables rather than delivering business objectives.

Figure 37 Data-Value Driven Management Playbook

Valuation Models and Measurement Frameworks

Howard notes that the webinar series will cover different valuation models, including market-based and economic dimensional models, and a review of eight valuation frameworks, such as EDM, Doug Lany, Gartner, and APQC. He notes the importance of Understanding and selecting the right measurement framework and ROI framework, along with choosing and applying the correct measurement system for data initiatives.

Figure 38 Replacing IT with Information Management pt.2

Figure 39 Data Valuation Framework

Figure 40 Data Office ROI