Summer Must-Reads to boost your Data Governance skills

#9 Or how to keep your knowledge fresh

Hello 😀

I know, i know, we should just cut screens and anything related to work during our holidays. But… when I leave my brain “alone”, I get to a point where I have new ideas. It usually happens after 2 or 3 weeks off. And this is the best.

Summer art moment at my parents house

It’s precisely at this moment that you should nurture your new ideas with a list of great and short things to read or to listen to.

Let’s take the inspiration wave :

1. A short course

2. Must-reads

3. Must-listens

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A short course

I’ve released a full course on Data Governance strategy on Udemy !

This course is designed for data professionals, business leaders, IT managers, and anyone responsible for managing and leveraging data within their organization.

We cover the following topics :

• Define and align your vision with the corporate stragegy

• Learn how to explain Data Governance with an analogy and the benefits

• Review the key components of a Data Governance Strategy : learn about vision, business goals, critical data, challenges, operating model, value proposition, and roadmap.

• Discover how to assess your current maturity level

• Get buy-in from stakeholders with a convincing business case

• Build a strong and flexible roadmap

• Avoid common pitfalls with best practices and tips

You get 3 materials :

• A Data Governance strategy template

• A Questionnaire for your maturity assessment

• A Data Governance maturity matrix

👉 The course is FREE until August 12th. Please leave reviews !

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Must-reads

Culture & organization

• ➡ Data Stewards : worst seat at the table?
  Article by MonteCarloData - May 2022
  A domain-first stewardship approach can better prioritize documentation, set requirements and give shared meaning to data within the operational workflow of the business.
  Still, a data steward dedicated to a domain will need specific training on data governance and clear incentives & rewards.

• 🌈 The Data Strategist : new role on the rise in the data industry !
  Article by Marie Lefevre - November 2023

  This position is becoming more and more important within organizations.
  Data Strategists are at the crossroads of data individual contributors, strategic consultants and team managers.
  They are fulfilling 3 types of roles :
  - Data Consultant : providing advice on methodologies and frameworks, structuring the organization
  - Head of Data : convincing the board, carrying the roadmap
  - Data Team Lead : managing a team of data profiles to deliver projects

• 💀 Is Data Governance dead?
  Article by Eric Sandosham - March 2024

  There is a failure to recognize data shelf-life. Data is a living thing, we cannot make it accurate once for all. Most people in organizations either don't understand Data Governance or think it's boring and don't deliver value.
  The expanded scope should be grounded on the following objectives:
  🛠 Data instrumentation & curation → address supply side of asset.
  ✏ Data enhancement & enrichment → address enhancement side of asset.
  💸 Data distribution & utilisation → address monetisation side of asset.

• 📍Data Mesh can’t win without a massive culture shift

  Article by Kim Thies - September 2023

  Data Mesh is failing because of cultural change. It's a lack of change management, lack of cultural alignment, and slow time to show ROI.
  Analysts and scientists complained that up to 80% of their time was simply spent in finding and cleaning data for analysis. The companies that are succeeding with data mesh will have a huge competitive advantage :
  - a better understanding of the business value and context data,
  - a solid foundation for automation via ML and AI technologies,
  - an increased level of accuracy and reliability in their outcomes.
  Enterprises that are able to adapt to cultural shifts, address organizational change, and process engineering, not just technical aspect, will become tomorrow’s leaders.

Expert topics

• ✅ Metadata quality

  Article by Kim Thies - September 2023

  Metadata provides context on what data means and how it should be used. It has been neglected because human data teams compensate for poor metadata by storing missing metadata in their minds. Metadata quality is now as important as data quality. And LLM applications need rich, high quality metadata in order to use data.
  👉 Having complete, clear and correct metadata in a centrally-accessible location, like a catalog or semantic layer, allows both humans and LLMs to benefit.

• 💎 How to price a Data Asset

  Article by Abraham Thomas - May 2024

  Pricing your data is incredibly hard.

  But there’s a new buyer in town : AI.
  🎯 Main strategies to price your data :
  You can use several dimensions : pricing by structured volume, pricing by use case, pricing by quality, pricing by access...
  Remember that usage rights are also simply monetizable !
  Don't forget to build a perpetual data machine that captures a steady stream of new data to get recurring revenues. The easier it is to objectively compute the ROI of your dataset, the larger its market.

• 🔍 Data ownership : a practical guide

  Article by Mikkel Dengsoe - February 2024

  43% of data practitioners think that ambiguous data ownership is their biggest challenge while preparing data for analysis. Because the person who notices a problem is not the right person to fix it.
  What works well :
  📝 Setting expectations for owners : are they responsible for addressing issues on data assets owned, notifying downstream stakeholders of issues, etc.
  👤 Defining ownership : group your stack into well-defined areas with clear boundaries. Make sure to also define ownership in code vs. UI, cross-tool ownership, and ownership across dependencies.
  🛎 Notifying the right people with the right context : ownership should be managed across the stack, but data teams, upstream teams, and downstream stakeholders need different alerts.
  🌱 Overcoming cultural ownership challenges : ownership is as much a cultural challenge as a technical one. Start small, find a team where you have senior buy-in, and beware of alert overload.

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Must-listens

Inspiring leader

🛍 Launching a Data Governance program with a data mesh approach DataGen podcast episode with Yannick Beltran - Kering’s Head of Data Governance

As global Luxury group, Kering manages the development of a series of renowned Houses in Fashion, Leather Goods and Jewelry : Gucci, Saint Laurent, Bottega Veneta, Balenciaga, Alexander McQueen, Brioni, Boucheron, Pomellato, DoDo, Qeelin, Ginori 1735 as well as Kering Eyewear and Kering Beauté.

What’s so great about how Kering did their program is how they managed to find the right organization despite their existing strong silos due to the number of Houses.
Employees are organized around each Data Domain. There are 5 key roles and entities within Kering's Data Governance program:

• Yannick's Central Team, made up of 3 people who have defined the program and support the various teams involved in its execution.

• Data Domain Owners, who are responsible for advancing all tasks related to a domain (e.g. Supply Chain): defining indicators, prioritizing Data Products, identifying sources to be used and associated use cases.

• Data Stewards, who are responsible for a sub-area of the Data Domain (e.g. a Supply Chain Data Product) and work in collaboration with a Data Squad to deliver the Data Products in particular.

• Data Squads, made up of business and technical profiles who deliver the Data Products.

• A federation made up of employees attached to a Data Domain who contribute to various tasks: defining indicators, prioritizing Data Products, etc.

Technical topics

📖 Who’s buying Data Catalogs and why? Catalog & Cocktail podcast with Neil Burge

Senior executives like the CEO or COO often seek solutions for business problems without knowing about specific tools like data catalogs. They rely on their data teams, who may have been struggling with inefficiencies for years. When senior management allocates funds to address these issues, it presents a key opportunity for vendors to engage with them and provide solutions.

4 clusters appear when it comes to the “why” :

1. “We must comply” reason : When a regulation like HIPAA is enacted, data teams may inform the board, which often sees it as important but not urgent. A catalyst for action usually occurs when a similar company faces a public issue with the same regulation.

2. “We must keep up with growth” reason : Some organizations have data teams providing valuable insights, leading to sudden growth due to better data access. This often sparks increased demand from business users for more data insights, sometimes through tools like Power BI. When executives, unfamiliar with terms like "data catalog," realize the need for better data management to sustain growth, they will find budget to invest in top-tier solutions.

3. “Help me follow the plan” reason : This usually occurs after a change in senior leadership, often the CEO, influenced by a brand consultancy's guidance. The consultancy conducts a data maturity assessment, revealing that the company lags behind its peers but has significant revenue opportunities through data use cases. The CDO rapidly assembles a team of data scientists, engineers, and governance professionals, then faces the immediate task of selecting a data catalog.

4. “Efficiency” reason : These companies may have grown organically or through acquisitions, leading to inefficiencies and data inconsistencies. Triggers often include boardroom presentations with conflicting data, prompting questions about the actual number of customers. This drive for efficiency often competes with hiring more data engineers or analysts rather than investing in data catalogs. The process involves detailed RFPs, POCs, or pilots with various vendors, aiming to solve issues like data lineage and gaining control over the data estate.

☀️ Time for a break, I’ll see you in September.

Take care,

Charlotte

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I'm Charlotte Ledoux, freelance in Data & AI Governance.

You can follow me on Linkedin !