The rapid progress in data analytics and artificial intelligence (AI) is prompting business tech experts across many disciplines to rethink how they implement information technology. This shift is known as data-centricity. As more technologists embrace this data-centric mindset, it’s beginning to challenge the tech industry’s focus on enterprise software and automation. Traditionally, business tech solutions have gravitated toward implementing complex enterprise SaaS solutions and automating manual tasks. Though these efforts have enhanced efficiency, they have also led to a landscape ruled by rigid systems and a fixation on workflow automation.

As the volume of business data proliferates and AI apps become more common, there is a shift in focus in the tech industry from software applications to data. Indeed, many tech disciplines are now using data-centric methodologies. This includes areas like data management, AI development, knowledge graphs, and enterprise architecture. There’s even a Data-Centric Manifesto endorsed by numerous experts. Moreover in today’s digital world, business leaders are beginning to recognize the benefits of adopting this data-centric mindset. To show how significant this change is in the tech industry, below are examples of traditional and new tech disciplines adopting a data-centric approach.

1. Data Management: A Data-Centric Strategy and Use of MetaData.

It is true that most large organizations have data management policies in place. However in many cases, the focus is on controlling and protecting the data within various functional data silos of the organization. Now, this is beginning to change. Business executives and data managers are realizing that data is at the heart of decision-making, operations, and strategic planning. Indeed, this new data-centric mindset enables increased trust in data, increased visibility, accessibility, more connected insights, and continuous learning.  Moreover, this data-centric management perspective is now starting to refocus such activities as data catalog management , metadata management, DataOps, and data governance.

For more information, see Atlan’s article, 10 Unignorable Benefits of Data Centric Culture. Also, see my article, Traditional Enterprise Data Management Is Floundering To Make Business Data More Valuable, Accessible, And Secure.

Traditional Enterprise Data Management Is Floundering To Make Business Data More Valuable, Accessible, And Secure

2. Enterprise Architecture: A Move Away From Application-Centric Architectures.

Indeed, many Enterprise Architects are now realizing that software applications are not the center of the universe. This is because IT teams have learned the hard way that software does not age well and soon becomes legacy software. As a result, IT is seeing that new solution implementations require a lot of overhead to include custom access controls, lengthy integration projects, and lots of data replication. Hence, IT departments are starting to look at transitioning to a more data-centric enterprise architecture that provides more flexibility to implement new solutions fast with less resources. For more information, see Dan DeMers’ article, What Is Data-Centric Architecture? Reasons to Adopt It.

You Need To Think Data Centric To Be A Successful Business: Stop Being Data Driven, Application Centric

3. Artificial Intelligence (AI) / Machine Learning (ML) App Development: a Data-First Versus a Model-Centric Approach.

Traditionally, AI/ML developers have focused on optimizing the code. Now, many AI developers are taking a data-centric approach where optimizing data quality is the central objective. Hence, data consistency becomes key as well as taking an iterative approach to improving data quality. For more information, see Neptune.AI’s article, Data-Centric Approach vs Model-Centric Approach in Machine Learning. Also, see DataHeros’ article, Data-Centric Approach: The Key to Driving Model Success.

How Data And AI Work Together To Better Empower Analytics

4. Software Coding: A Data-Centric Approach to Streamline Bloated Code.

Yes, there is even a movement within traditional software coding to take a data-centric approach to application development. With this approach, a lot of “bloated” code can be done away with. This is because a “data centric” software development process just focuses on the software functions, and not business data content. For example, the software does not need a lot of “if” statements bloated with data-specific business parameters as it can be handled in the data structure itself. As a result, this reduces development time and costs while improving the quality of software products.

For more information, see Yehonathan Sharvit’s blog posting, Principles of Data-Oriented Programming. Also, see my article, IT Project Data-Centric Guidelines: Results That Are More Informative, Time Sensitive, And That Empower Business Data.

IT Project Data-Centric Guidelines: Results That Are More Informative, Time Sensitive, And That Empower Business Data

5. Semantic Knowledge Graphs: Building Knowledge and Structure to Data for Apps to Use.

Knowledge Management Breakthrough – The Best Way With AI, Data, And Graph Tech

Dave McComb, president of Semantic Arts, is a leader within the tech industry’s data-centric revolution. Moreover, his company’s core capability is semantic knowledge graph development and implementation. Indeed, tech leaders are starting to take notice of knowledge graph technology. This is because it is an excellent enabler for helping businesses be being data-centric. To explain, knowledge graph tech is a graphics-based data network structure where entities such as individuals, places, and objects are nodes linked by edges representing their mutual relationships. Thus, knowledge graph tech centers on turning data into knowledge-based repositories that users, software applications, and even AI agents can access. 

For more on data centricity and knowledge graph tech, see TDAN’s article, The Unfolding of the Data-Centric Paradigm. Also, see David Shapiro’s article, Beyond Vector Search: Knowledge Management with Generative AI for more on the synergies of knowledge graph tech, data, and AI.

Data-Centric Business References.

First, for more on what it means to be data-centric, see my article, A Data Centric Business: The Best Way To Agility, One Truth, Simplicity, Technology Innovation.

A Data Centric Business: The Best Way To Agility, One Truth, Simplicity, Technology Innovation

Lastly, the question is, “is your organization ready to become data-centric?” If the answer is yes, then I’ve created a Data-Centric Business Strategy Checklist for you. Moreover, I have designed this data-centric checklist to help busy executives to transition their organization away from being application-centric to being data-centric. Specifically, it details critical executive-level tasks, such as establishing data-centric guidelines and aligning key business terms. Bottom line, this checklist provides a pathway for success for organizations to leverage their most valuable asset: data. For details and to get started, see my article, A Data-Centric Business Strategy Checklist: The Way To Energize A Digital Enterprise To Be More Agile, Bold, And Simplified.

A Data-Centric Business Strategy Checklist: The Way To Energize A Digital Enterprise To Be More Agile, Bold, And Simplified

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