Enterprise Data World 2024 Takeaways: Trending Topics in Data Management

I was privileged to deliver a workshop at the Enterprise Data World 2024. Publishing this review is a way to express my gratitude to the fantastic team at DATAVERSITY and Tony Shaw personally for organizing this prestigious live event. Participating in such events has multiple advantages, including becoming familiar with trending topics in the data management community worldwide, sharing your experience, and learning from leading data management practitioners in face-to-face mode.

I want to share some insights I gained during this event. This article will review the key trending topics discussed at Enterprise Data World 2024. Please note that this review is general and does not reference particular presentations delivered at the conference

Data governance, data modeling and architecture, data science, and other data management capabilities were core subject areas discussed at the EDW 2024, as shown in Figure 1.

Data Governance

Data governance, a concept lacking a universally agreed-upon definition in the data management community, was a key focus at the event. According to DAMA-DMBOK2, it is "the?exercise of authority, control, and shared-decision making?(planning,?monitoring, and enforcement) over the management of data assets ." This definition underscores that data governance governs data management, not data itself! However, this crucial distinction is often overlooked, leading to varied interpretations of data governance. DAMA-DMBOK2 identifies a data management operating model, policies, processes, and roles as the core deliverables of data governance. Yet, many professionals use data governance interchangeably with data management in the DAMA-DMBOK2 context. If you're interested in this topic, I recommend reading this series of articles .

At the EDW 2024, participants focused on the following six key topics:

Data governance and Stewardship

The key takeaways to think about are:

• Data management professionals use different frameworks to describe the content of data governance. For example, the DCAM data governance model includes multiple deliverables that, from the DAMA-DMBOK2 perspective, can be considered deliverables of data modeling and architecture (i.e., business glossary, data products) and metadata management.
• Different approaches exist to defining data management roles. However, there is often no distinction between functional roles (included in the organizational structure, such as data architect) and virtual roles (to be assigned to the functional roles, e.g., data owner). The role of a data user who balances the role of a data owner along a data chain is not always considered.
• Data policies are essential to data management as they define how the data management function should operate.
• Data management operating models receive considerable attention. There is growing interest in a federated operating model, which is one of the key components of data mesh architecture. It is worth noting that different data management operating models may have various levels of agility. A company should seek the opportunity to create a democratized operating model as an alternative to centralized and federated. I don't entirely agree that some models are generally better than others. I believe an operating model must be aligned with a company's profile and the applied data architecture type. However, embedding the democratization approach can increase the efficiency of any operating model.
• Implementation of data governance in public and scientific institutions has its specifics.
• Involvement of an organization's executives in data governance activities requires many communication efforts, but it's worth it.

Data Culture

Data culture is a set of collective behaviors, values, and norms in an organization related to the business value of data and its importance for achieving its goals.

It would be best if you thought about the following:

• The maturity level of data culture can be measured. Each organization should strive to reach the level where data culture is fully embedded into the organizational culture and is a part of business operations. Each employee must be involved in the journey.
• Building robust data management requires an entrepreneurial mindset. It includes strong stakeholder management and communication strategy.

Data Literacy

Data literacy is the ability to read, understand, create, and communicate data as information. It encompasses the skills required to ask the right questions about data, interpret data and its analysis, and understand the ethical implications of data use.

The key topics to think about are:

• Many business challenges are rooted in a poor understanding of data's role and inefficient data management. This leads to a situation where data is considered a debt, not an asset, and depletes organizational resources. Enterprise leaders must support the establishment of data literacy programs.
• Gamified learning is one of the ways to promote and increase the efficiency of data literacy programs.

Data Monetization

Data monetization is an organization’s ability to generate economic benefits from its data and data assets.

The key takeaways regarding data monetization are:???????

• Economic benefits can be monetary and non-monetary.

An organization can get monetary benefits by selling data, directly or indirectly, to third parties.

Non-monetary benefits can be achieved by enhancing existing products and services, improving internal efficiency, and decreasing costs through insights derived from data.

• A data monetization strategy encourages organizations to consider data as a product and apply the corresponding general process or program to develop, implement, market, and deliver these products to the right consumers.

Data Privacy and Protection

Data Privacy and Protection is an organization’s ability to safeguard personal or sensitive information from unauthorized access, use, disclosure, disruption, modification, or destruction. ?

An organization should keep in mind the following:

• Personal data has a wide range of identifiability. To protect data, organizations should use multiple techniques, technologies, and tools like data anonymization, encryption, de-identification, hashing and tokenization, cryptographic computation, statistical disclosure limitations, etc.
• Implementing data protection requires changes in data governance and architecture.
• Protecting data in the cloud requires different approaches.

Data strategies

In my practice, I use the following definition of a data strategy: “A data management strategy is a long-term future state document that demonstrates the intention of a company to manage and use data by its business strategy .”

Many organizations nowadays put efforts into developing data strategies. It is worth noting the following:

• Different frameworks and models of data strategy exist. In developing strategies, organizations do not always follow a strategy content recommended by the industry’s authorities (more in the article: “A Data Strategy: Theory vs. Practice .”
• ·A data strategy should include strategies for developing various data management capabilities.
• In developing a data strategy, an organization can use commonly used techniques, such as a business model canvas and SWOT analysis.
• Even ChatGPT knows that the core pitfalls in data strategy implementations are a lack of skills, clear business objectives, resistance to change, and cultural barriers.
• Organizations should consider the “green information management” approach when developing data strategies. This approach focuses on minimizing the environmental impact of information-related processes.

In Part 2 of this article, I will share key takeaways on data science, data architecture & modeling, and other data management capabilities.

The presentations and valuable input of these leading experts inspired me to write this review:

Malcolm Chisholm Ph.D. , John Ladley , Peter Aiken , Robert S. Seiner , David Kowalski ,

Leonardo Blunk , Jeff Brock , Kari Albers , Vanessa Lam , @Michael Colbert, @Teresa Hennel, Steven MacLauchlan , Becky Lyons , Gretchen Burnham, CDMP , Julie Amling , David Collister , Ilya Artsyman , Tim Goswell , Shannon Moore, CDMP , Deron Hook , @Agnes Hutchins, Danette McGilvray , Aakriti Agrawal, MBA, CDMP , Phil Greenwood , Douglas Laney , Nicole Janeway Bills , David Hendrawirawan , Leena Bongale , Marilu Lopez , Lorraine Battaglia , David Alzamendi , Dora Boussias , @Jim Young, Urmi Majumder , Fernando Aguilar Islas