Many modern organizations are overwhelmed by data that yields few insights. This is because their business data is often disjointed, duplicated, ambiguous, inaccurate, and incomplete. However, more innovative companies are starting to make a critical shift to bring order to this data mess. Indeed, these corporate leaders are steering their organizations away from an application-centric mindset and towards a data-centric way of thinking. By recognizing data as a strategic asset, they simplify data management, enable seamless information flows, and foster organizational agility. As a result, data-centric businesses can make bold, informed decisions at all levels.

So, how can executives transform their organization into a data-centric business? In this article, I’ll provide a data-centric business strategy checklist to assist executives in leading their organization’s shift to datacentricity. First, I’ll stress the need and compelling benefits as reasons for corporate leadership to commit to data centricity. Then, I’ll detail the critical executive-level tasks required for success. This includes establishing enterprise-wide data-centric criteria for IT projects and driving consensus on key business terminology. This checklist will also provide tips for executive to keep a data centric strategy on track to better manage an organization’s data. Finally, I’ll provide tips on how executives can prioritize IT projects to swiftly realize the benefits of data centricity.

1. First, Business Leaders Must Recognize the Advantages of Committing to a Data-Centric Strategy.

Despite most corporate leaders being tech-savvy and frequently working with data, they still struggle to fully harness the wealth of information within their business systems. Indeed, their data is stuck in data silos, duplicated, and jammed into different formats. Further, it is of such terrible quality that it’s virtually unusable, and definitely untrustworthy. That is where the idea of data centricity comes in. It is an innovative way to treat data as a valuable, permanent asset, not a byproduct . Indeed, what businesses need is a data-centric strategy for managing their data within their organizations and systems.

“We are surrounded by data, but starved for insights.”

Jay Baer

a. What is a Data-Centric Mindset?

Undeniably for most enterprises, the state of business data is a disjointed mess that yields little insights. A change is needed. Businesses need to pivot away from just being data-driven and application-centric. Instead, they need to focus their organizations and systems towards data centricity. For those of you not familiar with the term data-centric, below is a definition.

Data Centric Definition

“Data centric refers to an architecture where data is the primary and permanent asset, and applications come and go.  In the data centric architecture, the data model precedes the implementation of any given application and will be around and valid long after it is gone.”

TDAN, The Data-Centric Revolution: Data-Centric vs. Data-Driven

I like this definition. First, it defines data as the primary component of business information technology. Specifically, this definition tells us that technology, software applications, and automation will come and go. However and more importantly, it tells us that our business data is extremely valuable and will never grow obsolete. Indeed, data is a permanent business asset that organizations do not need to constrain within any particular technology or software application. For a more detailed look at the business challenges of dealing with data overload and the concept of running a data centric enterprise, read my article, Being A Data Centric Business: It’s Going Beyond The Frenzy Of More Big Data And High Tech.

Being A Data Centric Business: It’s Going Beyond The Frenzy Of More Big Apps And High Tech

b. The Six Benefits Of A Data-Centric Business.

Data-Centric Benefits: Businesses Becoming More Innovative By Not Being Mired In Application Centricity

Now, transforming into a data-centric organization overnight can be challenging, particularly for those accustomed to an application-centric approach to IT. However, shifting focus toward data centricity can yield immediate benefits.These data-centric benefits include:

• Superior Business Agility: Not chained to data silos or legacy applications
• High Confidence In Data: There Is a single source of truth (SSOT) vs multiple versions or copies
• Improved Decision-Making: Having high-quality data that is complete, accurate, timely
• Simplified Software: Lower costs, increase reuse of code
• Faster Adoption of Needed Technologies: Able to leverage necessary and emerging data-centric technologies such as AI, IoT, and other data-intensive information technologies.
• Streamlined Data Security, Integration, And Analytics

Indeed, a shift to a data-centric way of thinking will differentiate those businesses that will thrive and those that will be left behind. For more details on the benefits of data centricity, see my article, Data-Centric Benefits: Businesses Becoming More Innovative By Not Being Mired In Application Centricity.

c. Committing to A Data-Centric Strategy.

Once corporate leaders understand the advantages of a data-centric approach, they’ll recognize the need to shift their organization’s focus from applications to data. This transition will require a well-planned data-centric strategy. While the idea of a data strategy isn’t new, existing strategies will need to be updated to prioritize a data-centric approach.

Indeed, today every business already has some form of data strategy in place to manage data. Some may have a formal data strategy document, while others may take an ad hoc approach. Specifically, an effective data strategy should include components like an overarching business strategy, self-assessment, data architecture, defined organizational responsibilities, data governance, and a roadmap. For more details on the components of a data strategy, see Analytics8’ article, 7 Elements of a Data Strategy.

Now, when developing or updating a data strategy, corporate executives should primarily focus on the business strategy component. It is this part of the plan that will provide the necessary executive-level guidance for their organization to effect change. Indeed, executive leadership’s commitment to a data-centric mindset is critical to change the way organizations manage data. Without this commitment, a data-centric strategy can’t be effectively developed and implemented. Only with executive-level support can an organization change how it manages and uses data. The rest of this article will identify the executive-level tasks necessary for a business-centric data strategy to take hold.

2. Next, Executives Need to Establish a Set of Data-Centric Criteria to Guide IT Project Use Cases and Implementations.

With a commitment to a data-centric mindset, executives can start developing a business strategy in earnest. It is this business strategy that will shape their organization’s approach for implementing information technology projects. At the same time, this new business data-centric strategy will not necessarily require any fundamental change in corporate goals or project management methodologies.

However, executives do need to define and disseminate going forward new data-centric criteria for project managers to use across the organization. Further, this criteria will guide project managers, but should not overly constrain project-level goals, approaches, or outcomes. To illustrate, below are examples of project-level use cases that are not necessarily changed with a data-centric approach. Further in this article, I will also include examples of enterprise-level data-centric criteria that an executive team could use to shape project solutions corporate-wide.

a. No Need to Fundamentally Change Project-Level Use Cases’ Goals and Expected Outcomes.

So, from a project management perspective, adopting a data-centric approach doesn’t necessarily require a change in methodology. Indeed, organizations will not have to fundamentally alter how they develop project-level use cases to identify goals and expected outcomes. To illustrate, the following are examples of project use cases that could be found within many businesses, regardless of whether they follow a data-centric approach or not.

Examples of Project-Level Use Cases 

1. Use Case for Enhancing Customer Relationship Management.
   • Goal: Increase customer lifetime value by 20% through personalized experiences.
   • Expected Outcome: Personalized marketing increases engagement by 30%, proactive customer service reduces churn by 15%.
2. Use Case for Optimizing Supply Chain Operations.
   • Goal: Reduce supply chain costs by 15%.
   • Expected Outcome: Reduce unplanned downtime by 50% through predictive maintenance, while optimized inventory management cuts waste by 10%.

Again, for a data-centric business, it is not necessary to fundamentally change how organizations develop use cases to include stating project goals and outcomes. On the other hand, there is a need to include project criteria within these use cases that favor both data-centric outcomes and approaches. See below, for examples of data-centric criteria to include in a business data strategy.

b. Do Include Criteria for Use Case Outcomes that Advance Data-Centric Benefits.

Now, every organization is unique, with different priorities, resources, and challenges. As a result, each will take a different approach to achieving their corporate objectives and implementing IT projects. Indeed, this holds true for organizations adopting a data-centric mindset. In this case, executives must establish guidance for implementing projects that prioritize data-centric benefits. Moreover, this guidance should avoid hindering project implementation or being technology-specific. Instead, this executive-level criteria should shape project outcomes in order to realize data-centric benefits. To illustrate, the following are examples of data-centric criteria.

Examples of Data-Centric Project Criteria.

1) Reduce the Number of Data Models and Complexity of Data Structures.

This type of criteria will help advance superior business agility by reducing the complexity of data that decision-makers have to work with.

2) Reduce Duplication of Data.

With less copies and one-offs of data sets, organizations will have increased confidence in data.

3) Improve Quality of Critical Data.

This criteria will increase the quality of information for improved decision-making. Specifically, this will encourage organizations to cultivate data that is complete, accurate, and timely.

4) Reduce Code Base.

This criteria will help to simplify software portfolios resulting in lower costs, both upfront and on-going maintenance. Also, it promotes increased reuse of code.

Data-Centric Business Tech: New And Better Ways To Shatter Our Obsession With Software And Automation

5) Favor Data-Centric Technologies and Methodologies.

Indeed, this criteria will lead to faster adoption of both needed and emerging data-intensive technologies such as AI, IoT, and data analytics tools. For more information, see my article, Data-Centric Business Tech: New And Better Ways To Shatter Our Obsession With Software And Automation.

6) Avoid Application-Centric Security Solutions.

As a result, this criteria will help to streamline and standardize overall data security by favoring security measures at the enterprise or data level, not the application level.

7) Minimize Custom Data Integrations.

Bottom line, this criteria will help to streamline and even eliminate costly data integration efforts, both now and in the future.

8) Leverage Operational Data Definitions that are Measurable and have Shared Meaning.

Poor Operational Definitions Impede Supply Chain Tech Adoption: Now Is the Time For A Big Change

This criteria would help to Improve interoperability and streamline the use of data analytic tools for faster, better enterprise-wide decision-making. For more details, see my article, Poor Operational Definitions Impede Supply Chain Tech Adoption: Now Is the Time For A Big Change.

For more ideas on how executives can proceed with a data-centric strategy, see Kevin Doubleday’s article, Introduction to Data-Centricity. Also, for a more scientific perspective on advancing a data-centric enterprise strategy, see Mark S. Fox’s paper, The Role of Competency Questions in Enterprise Engineering. In this paper, the focus is on determining axioms to guide enterprise engineering when using ontologies and knowledge graphs.

So, these data-centric criteria illustrate how organizations can move forward in realizing the benefits of a data-centric business. However, remember at the corporate level, it’s best not to specify the type of technologies IT projects use, nor place undue constraints on project implementation.

3. Gain Executive-Level Agreement on Key Business Terms and Their Operational Definitions to Assure Mutual Understanding and Interoperability.

Next, in developing a data-centric business strategy, corporate leadership must establish clear, shared operational definitions of key business concepts and terms. This is crucial in the age of digitalization, where a lack of mutual understanding can lead to organizations being overwhelmed by meaningless data that yields little insight.

Often, this lack of shared understanding stems from ambiguities in data transmitted between systems and partner organizations. As a result, even critical data may not be informative enough for decision-makers to take action. Worse, ambiguous data can lead to poor decisions. Clear, measurable, and mutually agreed-upon operational definitions are essential. Without a shared and precise understanding of key terms and processes, business will struggle to realize the benefits of digital technology.

To illustrate the problem with the ambiguous definitions, see below. In this example, I identify all the ways that the term “shipped” could be understood for a shipment status within a typical supply chain.

Examples of Possible Meanings of a Shipment Event Labeled “Shipped”

• Carrier In Possession of Shipment. The carrier took possession of the shipment and it is in transit. This is the most common interpretation, but not necessarily what actually happened.
• Barcode Label Printed. The shipper printed a shipping label and placed it on the package. Many systems will generate a “shipped” status based on this event.
• Ready For Pickup. The shipment is on the shipping dock ready for the carrier to pick up. Again, at least from a customer perspective, this is not “shipped”.
• Shipment Loaded on a Trailer. The shipment is on the trailer in the dockyard ready for the carrier to pick up. Here again, this event is routinely identified as “shipped”.
• Absolutely Nothing. I have even seen cases where a user accidentally enters an erroneous tracking number like “123” into the data entry field of a tracking web page. Then surprisingly, the web page provides an updated status of “shipped”. 

The term “shipped” is just one example of how a business term can be misunderstood by different stakeholders. In a typical organization, there are hundreds of these key business terms that require shared understanding across systems and partner organizations. Indeed, for any business, but especially a data-centric one, mutually understood definitions are crucial. Hence, organizations must update their business lexicons and glossaries to provide measurable operational definitions, not just a loosely-defined data dictionary.

Undeniably, this shared understanding across the enterprise is the foundation of a data-centric strategy, enabling everyone to speak the same language and make data-driven decisions with confidence. Without a doubt, a loosely-defined data dictionary is insufficient. What’s needed are measurable, operational definitions that both systems and humans can understand. For more information on this topic, see my article, Poor Operational Definitions Impede Supply Chain Tech Adoption: Now Is the Time For A Big Change.

4. Engaged Executive Leadership Is Needed to Follow Through with the Development and Implementation of a Data Centric Strategy.

Also, part of developing a business data-centric strategy is to provide guidance and direction to how the organization manages data. Obviously, today all organizations manage their data. The real question is how engaged is executive leadership in providing effective guidance in managing their corporate data.  

a. Most Business Executive Teams Do Not Provide Much Corporate Guidance on Managing Data.

Now, some organizations do have a documented data management plan that includes formal guidance on both data strategy and governance. Also, there are several large corporations where executives are definitely involved where they have even appointed a chief data officer (CDO). On the other hand, many executives choose to take a hands-off approach, delegating all responsibilities to the IT department. Further for most organizations, their data strategies are more focused on controlling data versus also seeking to maximize the value of their data. For more details on the traditional role of data management, see my article, Traditional Enterprise Data Management Is Floundering To Make Business Data More Valuable, Accessible, And Secure.

b. The Need for Executive Leadership to Champion a Business Strategy to Both Secure Data and Extract Maximum Value from It.

For organizations to extract maximum value and insights from their data, executive leadership is needed. Indeed, corporate executives are needed to champion a data-centric strategy to shape how the enterprise manages data. As stated previously, a data-centric business needs to treat data as both a permanent and valuable asset. However, when there is no definitive direction from executive-level management, the business will in most cases take an haphazard approach to manage their data. In fact, this is why most businesses today have data that is fragmented, duplicated, somewhat ambiguous, and spread across disconnected systems.

c. Engaged Executive Leadership Also Needed to Sponsor the Development and Implementation of a Data-Centric Strategy.

So for a data-centric strategy to take hold, it needs a champion – someone who will passionately advocate for this new approach and drive it forward. This executive-level champion will build a coalition of support, communicate the vision, and overcome resistance. By having a strong champion, organizations can navigate the inevitable challenges of change and create a data-centric culture that becomes ingrained in the company’s DNA.

5. Identify and Prioritize IT Projects that Adopt a Data-Centric Rather Than an Application-Centric Approach.

The final step of developing a data-centric business strategy is to start identifying specific projects that can best help the organization to start realizing data-centric benefits. Below are some tips to move forward with data centricity within IT projects. This includes starting small to gain confidence, prioritize projects that can yield the most data-centric benefits, and strive to include data centric criteria in all IT projects. See below for details. 

a. Start Small to Gain Confidence in Tangible, Cost-Saving Results, Then Go For More Ambitious Projects. 

In many cases, it is best to start small when transitioning to a data-centric business. Indeed, these first projects may be small, but will yield tangible data-centric benefits. Moreover, it is likely that many of these projects following a data-centric approach will actually save money versus a more costly application-centric approach. Hence with these data-centric cost savings, it will be easier to take on more ambitious data-centric projects in the future.

b. Prioritize Data-Intensive Projects that Are Sure to Yield Data-Centric Benefits.

Further where feasible, prioritize IT projects that will require data-intensive solutions. This is because these types of projects have more potential to eliminate data silos, improve data quality, or provide a unified customer view. Indeed, successfully applying data-centric criteria to these types of projects will build the momentum and set the stage for a broader data-centric transformation.

c. Include Data-Centric Criteria for All IT Projects.

Also, almost any IT project should include data-centric criteria in their use cases as laid out by executive management. For instance, a typical IT project could include replacing a poor performing accounting system, a new ERP data interface, an AI pilot project, changing out a cloud provider, or phasing out a poor performing database technology like MS Access to name a few.

Conclusion and More References.

Indeed, innovative companies are starting to make a critical shift to bring order to their business data by adopting a data-centric approach to data management. By recognizing data as a strategic asset, they simplify data management, enable seamless information flows, and foster organizational agility. As a result, data-centric businesses can make bold, informed decisions at all levels. For organizations to truly achieve this transformation, executive leadership must play a crucial role in driving this innovative shift towards data centricity. 

In this article, I have provided a data-centric business strategy checklist to assist executives in leading their organization’s shift to a data-centric business. For more references used in this article and to develop a data-centric business strategy, see below.

More References

• Semantic Arts’ Presentation, Case Studies in Applying Semantics to Enterprise Systems. This presentation provides examples of successful data-centric projects using ontologies and knowledge graphs.
• GO FAIR’s FAIR Principles. The FAIR principles provide guidance on making data more “machine actionable” which is much aligned with a data-centric mindset. FAIR stands for Findability, Accessibility, Interoperability, and Reuse of digital assets.
• Alan Morrison’s article, Trusted, automated data sharing across spreadsheets and other documents. This article focuses on graph-based data centricity to contextualize electronic business documents so that they’re interoperable.
• TDAN’s article, The Enterprise Data Model. This article details a data-centric approach for managing data.
• Markus Harrer’s blog posting, Why Enterprise Data Models don’t work. This posting spells out the challenges and dangers of enterprise data models.
• Amplitude’s article, What is Data Strategy? Guide with Examples, that identifies the crucial elements of a data strategy.
• Atlan’s article, Difference between Master Data Management(MDM) and Metadata Management, provides a good technical overview of how Master Data Management (MDM) and metadata can help minimize data silos.
• Mark S. Fox’s paper, The Role of Competency Questions in Enterprise Engineering provides some great points on determining axioms to guide enterprise engineering when using ontologies.

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

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