Challenges with Data Management Trends: Part 1
Dr. Irina Steenbeek
Data Management Practitioner & Coach | Data Management and Governance Frameworks | DM Maturity Assessment | Data Lineage | Metadata | Keynote Speaker | Author: The O.R.A.N.G.E. Data Management Framework & 4 books
Last month, I delivered a Keynote presentation at the DataCampus meeting in Hamburg. The topic was trends and challenges in data management. While preparing the presentation, I discovered that we not only have trends and challenges in data management but also experience challenges in defining trends.
In Part 1 of this article, I will:
Before diving into the subject, I want to share my understanding of the terms “driver,” “trend,” and “challenge” that I will use in this article.
A data management trend is a general direction in which data management evolves.
A driver is a factor of the external business environment that determines trends.
A challenge is “a?new or difficult task that tests somebody’s ability and skill .”
Trends and challenges can impact each other. Challenges can lead to trends. For example, the challenges of high IT costs for onsite IT tool deployments led to the development of cloud technologies.
Trends can pose challenges. For example, digitalization and online communication have brought challenges related to cybersecurity, privacy issues, and digital well-being.
Now, let’s discuss the challenges associated with defining data management trends and trends themselves.
Challenge 1. Different sources have pretty different viewpoints on trends in data management.
I analyzed 18 sources that discussed data management trends in 2022 and 2023. Gartner and Dataversity are examples of these sources. The total number of trends they mentioned reached 114! This number of trends was a starting point to dive deeper and try to make sense of these trends.
Challenge 2. Trends are mixed with business drivers that lead to these trends.
When I went through these trends, I discovered at least four factors or business drivers that cause trends in data management. Some authorities consider these factors as trends. I have different opinions and recognize them as the reasons that cause trends in data management. I will briefly discuss these four drivers:
Economic uncertainty
This factor forces companies to focus on generating more monetary value from data and decreasing IT-related costs. These goals will motivate companies to use cost-effective technology.
Regulations pressure
For all companies worldwide, personal data protection regulation is one the most important regulations that impact data management solutions. Analysts at Gartner have predicted?that?65% ?of the world’s population in 2023 will be covered by laws similar to GDPR. For financial institutions, complying with risk-related regulations is the biggest driver for implementing data management.
Cybercrime
Cybercrime has grown over the last few years. Ransomware is one way for cybercriminals to profit off a company’s data at its expense. To mitigate the risk of cybercrime, companies must invest in cybersecurity.
Development of artificial intelligence (AI)
The latest factor that significantly impacts data management is the development of artificial intelligence. Multiple data management (DM) functionalities provided by DM tools can be enriched using AI. I mean, for example, data mapping and cataloging, metadata management, anomaly detection, data analytics, and data quality.
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Challenge 3. An unaligned definition of data management within a data management community leads to challenges in defining the trends.
After carefully analyzing all these 114 trends, I realized that the key reason for this impressive number of trends is different viewpoints on data management.
I shared the results of the analysis in multiple articles and webinars . This article will present one of the data management capability models I use in my practice. This model, demonstrated in Figure 1, describes data management in the following way.
Figure 1: The model of a “Data Management Capability”
(the “Orange” data management framework).
The core value proposition of data management is enabling a data lifecycle and delivering information to all relevant stakeholders.
So, data lifecycle management is the core capability of data management, focusing on value delivery.
Two capabilities, data governance, and business architecture, provide direction for data management development. These capabilities belong to the strategic level of the data management capability map.
Then, we have multiple supporting capabilities. They enable the data lifecycle.
And, of course, to bring these capabilities into operations, we need a set of policies, processes, roles, IT tools, and other resources to deliver the intended artifacts of each capability. Data governance supports their development.
Let’s consider examples of trends that characterize each of these capabilities.
Trend 1. The maturity level of data management worldwide has grown over the last four years.
Figure 2 illustrates the overall data management maturity trends. The results are based on the anonymous? DM maturity scan available at the Data Crossroads site, shared in the “Data Management Maturity Assessment Review 2022 .”
Figure 2: The trends in data management maturity.
About the author:
Dr. Irina Steenbeek is a well-known expert in implementing Data Management (DM) Frameworks and Data Lineage and assessing DM maturity. Her 12 years of data management experience have led her to develop the "Orange" Data Management Framework, which several large international companies successfully implemented.?
Irina is a celebrated international speaker and author of several books, multiple white papers, and blogs. She has shared her approach and implementation experience by publishing?The "Orange" Data Management Framework,?The Data Management Toolkit,?The Data Management Cookbook, and Data Lineage from a Business Perspective.
Irina is also the founder of Data Crossroads, a coaching, training, and consulting services enterprise in data management.?
To inquire about Irina's training, coaching, or participating in your company webinar or event, please, email to?[email protected] ?or book a free 30-min session at https://datacrossroads.nl/free-strategy-session/
PhD (Law), DBA, PgDip (RQF-L8), LLM, MBA, MDataSc, MCNeuroSc, MSR ? Head of Apsley Labs & Global AI Program Director ? Robotics ? Data Architecture & Governance ? MIT-IBM Watson AI Lab Partner ? Ex-Accenture & Capgemini
1 年Great article, Dr. Irina Steenbeek. The challenges in defining these trends, particularly the variance in viewpoints among different sources, echo broader struggles in keeping pace with rapid technological evolution. The identification of economic uncertainty, regulatory pressure, cybercrime, and AI development as drivers for these trends is astute, as these forces significantly shape data management strategies. Additionally, the emphasis on aligning definitions within the data management community is pivotal, as it underscores the need for a common language and understanding in this ever-expanding domain. Steenbeek's approach to categorizing trends based on data management capabilities provides a structured framework for tackling these challenges. It's a timely contribution to a field that's becoming increasingly vital in our data-driven world, emphasizing the importance of agility and adaptability in managing data effectively.
Data Management Consultant, CDMP
1 年800 companies and still Level 1 has been unrealistically empty for 4 years. Self-assessesment is biased(?), which is a thought-starter by itself.
Datamanagement | Management Systems: Quality (ISO 9001) | Information Security (ISO 27001 NEN 7510) | Safety (ISO 45001) | Data Quality (DQMS) | Knowledge (ISO 30401).
1 年Great distinction between drivers, challenges, and trends!
Enterprise Architecture & Data Management as a Service / Digital Strategist and transformer & collaborator / Speaker / Simplifying your business
1 年Hi Dr. Irina Steenbeek love your model.. it tells so many stories. You have left off MDM.. was that intentional or just flavours. I see a lot of companies that can’t get a single source of truth sorted..