Transform your Data Office to adopt a Vision and Value-Driven focus – Data Citizens.

Executive Summary

This webinar explores the Chief Data Officer (CDO) mandate and data governance for organisations looking to monetise their data. Howard Diesel discusses data quality and governance, challenges, and considerations for CDOs. The webinar then highlights the importance of demonstrating value in data management programs and the challenges of implementing data initiatives and dealing with data quality issues that must be addressed.

Data visualisation and the use of business KPIs in customer experience and retention are vital for driving business value. Additionally, indirect costs in data analysis and commercial feasibility challenges must be considered. Howard spends time on the importance of data strategy and communication with business, business empathy, and understanding challenges. He also shares case studies and examples of data transformation and value realisation that can provide valuable insights, particularly in the consulting and oil & gas industries. Lastly, Howard recommends data valuation frameworks and risk management to ensure data initiatives align with business goals and objectives.

Webinar Details

Title: Transform your Data Office to adopt a Vision and Value-Driven focus – Data Citizens
Date: 26 February 2024
Presenter: Howard Diesel
Meetup Group: African Data Management Community Forum
Write-up Author: Howard Diesel

Data Monetization and CDO Mandate

Howard Diesel opens the webinar, noting a presentation discussing the importance of data monetisation and the challenges it presents. He emphasises the need for prioritising data quality and reliability to improve the effectiveness of data analytics. Chief Data Officers (CDOs) mandate data office return on investment (ROI) and the relevance of data value visibility in organisations. Additionally, Howard shares statistics about the average lifespan of CFOs and discusses techniques to help data professionals expose and package the value they bring to organisations.

Figure 1 “Transform your Data Office to adopt a Vision and Value-Driven Focus”

Figure 2 “Mandate Data Office ROI before it’s Mandated”

Figure 3 Fighting Data Management Evils

Data Governance and Data Quality Discussion

During a discussion, the topic of the mandate for the Chief Data Officer (CDO) was brought up, with the business often mandating the CDO to prove ROI before leaving the position, raising questions about the value the CDO adds. One participant raised the issue of prioritising data quality and data governance. Howard suggests that becoming value-driven should be the focus before determining the appropriate data quality and governance approach. The CDO has good management knowledge but addresses data governance topics through a highly conceptual and complicated approach. Lastly, the CDO summer school program is recommended for anyone interested in becoming a CDO.

Challenges and Considerations for Chief Data Officers (CDOs)

Chief Data Officers (CDOs) are classified into different generations based on their focus, data management, and analytics skills. CDO gen 1 focuses on establishing data management fundamentals within the organisation, and technical skills are crucial for them to help with data management fundamentals. Choosing the right company is essential for CDOs, as mismatched skills and organisational needs can lead to challenges.

The average tenure of a CDO is 2.4 years, compared to 6.9 years for a CEO, leading to speculation about the lack of value and direction CDOs provide. The first 100 days of a CDO’s journey are crucial for making a difference in the organisation. Experts like Peter Jackson and Carolyn Carruthers address ways to make a difference in the CDO Summer School.

Importance of Organization and Value-Driven Business

Howard emphasises the importance of choosing the right organisation and making a difference in the Chief Data Officer (CDO) position. He suggests using a “CDO in a box” approach, where an external individual initiates the CDO position to help others understand the organisation. The Human Resources Officer (CHRO) is crucial in understanding and making organisational changes. A value-driven approach is necessary, particularly in the Business Architecture Forum’s focus on business value proposition, goals, objectives, and strategy. However, prioritising data management over addressing business problems should be avoided, and a separation between the data and the data management strategy must be maintained.

Figure 4 More on Fighting Data Management Evils

Importance of Demonstrating Value in Data Management Programs

Establishing a data management program is crucial for the success of a Chief Data Officer and their data career. A data management program should include knowledge management, policies, procedures, and reference architecture to show value and direction in the work. A lack of demonstrating the program’s value to the business could lead to embarrassing situations and questions from the management about the program’s benefits. Thus, it is essential to clearly articulate the value of the work being done to the business. An internal audit of the data management program may lead to its re-evaluation and reshaping. Hence, individuals in data management roles should reflect on their ability to demonstrate the value of their work to the business.

Challenges of Dealing with Data Quality Issues

The importance of demonstrating a business case for addressing data quality issues and earning more money from fixing problems than spending on a data quality team is vital. However, there is a challenge in answering questions about the perception that consultants come in, make money, and leave businesses with all the problems.

The impact of data quality issues on integration between two lines of business commercial applications is significant. For example, missing attributes can cause problems for downstream reporting. The enterprise analytics team’s high expectations for data-related projects can lead to frustration, with valuable ideas and needs being turned away.

Challenges of Implementing Data Initiatives

A data factory is a response to the failure of departments to manage and report data correctly, resulting in the need for external consultants to address data management issues. Due to the lack of coordination and communication between departments, consultant payments are often grudged, reflecting dissatisfaction with the need for external assistance. Therefore, conducting a commercial feasibility study and technical feasibility assessment before initiating a data initiative to build a prioritisation matrix is paramount. Additionally, understanding how to measure the value of a data initiative is crucial, and templates and frameworks are available to aid in this process. The scope of data initiatives goes beyond building reports or products and includes activities such as data literacy programs, emphasising the intangible nature of data assets.

Data Value Realization and Measurement

Tom Redmond’s approach involves a 1 in 10 ratio for processing correct vs. incorrect transactions, with the cost of fixing them also considered. Working with dirty data can have a significant financial impact, which can be quantified using metrics and guidelines from sources like Gartner. However, it’s important to be cautious of unrealistic ROI statements.

To realise the value of data, it’s crucial to work with accountants and CFOs to account for intangible assets such as data literacy, education, regulation, data quality, products, and risk mitigation. A framework for measurement is necessary, which starts with a value taxonomy to allocate work to value, as discussed in Infonomics by Doug Laney and Data Juice.

Value Compass and Data Visualization

Howard discusses using a value taxonomy or data compass to explain financial metrics and benefits to businesses. This tool is used to showcase where value is coming from in a use case. In a case study of the telco industry, the only increasing revenue is mobile broadband, while other revenues are dropping.

Howard goes on to mention that threats to telcos include competition from Facebook, Amazon, Netflix, and Google and new technologies like eSIMs. To better relate to businesses’ concerns and objectives, the speaker advises using industry-specific use cases when presenting.

Figure 5 Case Study by Industry

Figure 6 Telco Industry: ROI Case Studies

eSIM Technology and Use Case Ideation

eSIM technology is a great option for the users as it allows a single SIM card to work across multiple providers, providing increased flexibility. However, there are still some issues with roaming charges and connectivity. In the telco industry. Use case ideation requires understanding key performance indicators (KPIs) like churn tracking and customer lifetime value. It is essential to have business literacy, including awareness of external opportunities and threats, to develop effective strategies in this industry.

Figure 7 Telco KPIs

Figure 8 Telco Case Studies

Importance of Business KPIs in Customer Experience and Retention

Vodafone’s primary objective was to improve customer experience and retention, which was achieved by aligning business KPIs to prove the success of data management capabilities. High satisfaction scores, reduced churn, and optimised network resources formed the basis of the company’s ROI, and monitoring these metrics allowed for clear communication and value recognition within the business.

The business benefited from allocated benefits by increasing customer lifetime value and reducing churn through data and analytics. Ongoing negotiation of KPIs-related benefits was important to avoid future issues and ensure initiative value recognition.

Indirect Costs in Data Analysis

Howard emphasises the importance of maintaining business value through data analysis, using examples from companies like Vodafone, AT&T, and Deutsche Telekom. Key concepts include customer churn, lifetime value, risk mitigation, network traffic analysis, and minimising reputational risks. A “value compass” is proposed to understand the indirect costs’ impact on business revenue, and quick wins in data analysis are highlighted as a way to solve existing problems and achieve faster ROI without creating new products.

Commercial Feasibility and Revenue Model Challenges

The challenges faced by product owners, data delivery and operational teams regarding revenue model feasibility, data sharing and management are highlighted. A specific example is when a business used manual methods and lost revenue. The value of addressing these challenges is quantified by examining the cost of manual data sharing and the impact on lost revenue.

There is a perception that big data and enterprise analytics are primarily focused on making money for the organisation and delivering business insights. Stakeholders often require a clear ROI statement before investing in data-related solutions. The goal is to demonstrate that revenue generation is not just about fancy products or big data but also about addressing practical business problems and generating insights.

Case studies and examples of data transformation and value

Howard touches on an example of the national highways in the UK, which prioritised software licenses over data costs and found that they were getting more value from the licenses. They implemented a digital transformation to demonstrate the connection between data products and customer features, highlighting the value of their assets. Vodafone also underwent a digital transformation and saw the benefits of its data assets.

Figure 9 Value Compass: Vodafone

Telcos and New Business Capabilities

Telcos like Vodafone and Deutsche Telekom are diversifying their services by offering location-based insights and fleet management systems. This expansion allows them to generate revenue from new sources and partner with data management vendors to enhance their capabilities in traffic management and smart fleets.

Deutsche Telekom has developed big data products like soccer analytics, smart parking, and traffic management, demonstrating their diversification beyond traditional telco services. These internal use cases generate indirect revenue in marketing, customer service, product enabling, and networking, offering unique benefits from their respective data products.

Figure 10 Value Compass: Deutsche Telecom

Figure 11 Telco Opportunities

Data Strategy and Communication with Business

The importance of data strategy in addressing internal use cases such as real-time media performance tracking and contact centre productivity is highlighted. The process involves identifying necessary data, calculating benefits, and getting agreement from the business on the value statements.

Ideation sessions can help address challenges and opportunities, and even partial data can improve decision-making and insights. Effective communication with the business is crucial, and it involves listening to their challenges and finding ways to improve using data. Howard recommends exploring the Data Juice book for case studies on data value to share in the future.

Consulting and Oil & Gas Industry

Howard notes that as a consultant, it’s important to consider questions about making changes, adding new components and engaging with clients at a high level. The existence of certain tools or processes is often due to other related factors. Reservoir engineers in the oil and gas industry typically have programming skills and use Python and SQL Server.

Encouraging engineers to adopt new methods may be met with resistance. PPDM is a widely used standard in the oil and gas industry for defining terms and measuring value. However, the platform in question is maintained by one individual, leading to potential risks in case of departure. Therefore, key management is crucial to mitigate the risk associated with dependency on individuals. This is exemplified by the implications of personnel changes and the importance of stability in complex financial systems, such as when a financial analyst retires and leaves behind complex automated processes in Excel.