Recently, I delivered a Keynote about data management trends. While preparing it, I discovered we have challenges defining data management trends.
Let me share my observations and propose a way out.
Before diving into the subject, I want to share my understanding of the terms “driver,” “trend,” and “challenge.”
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.”
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.
Challenge 3. An unaligned definition of data management within the 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.
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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.
In the follow-up articles, I will demonstrate trends per each DM capability.
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