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. Most importantly, I’ll spell out the five critical tasks that executives need to champion to implement a data-centric business strategy. This includes establishing enterprise-wide data-centric criteria for IT projects and driving consensus on key business terminology. Also, this checklist includes steps for executive to keep their data centric strategy on track. Lastly, I’ll provide tips on how executives can prioritize IT projects to swiftly realize the benefits of data centricity.

Step 1 – Business Leader Commitment: Executive-Level Ownership of the 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 . Moreover, what businesses need is a data-centric strategy for managing their data within their organizations and systems. Most importantly, it is executive-level commitment to this strategy that will make it successful.

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

Jay Baer

a. What is a Data-Centric Mindset?

Undeniably, the state of business data as a whole 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 a data centric mindset. For those of you not familiar with the term data-centric, below is a definition.

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

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

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.

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

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

The good news is companies can start receiving immediate benefits as they transition to a data-centric business. At the same time, transforming into a data-centric organization is a significant undertaking. In particular, this new way of doing business is challenging for organizations that are accustomed to an application-centric approach. However, the benefits of a data-centric approach are tremendous and 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

Without a doubt, businesses that adopt a data-centric mindset will thrive, while those that fail to make this shift risk being left behind in today’s competitive landscape. 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 executives commit to a data-centric strategy, they will need a new data management approach to shift their organization’s focus from applications to data. At the same time, many organizations may already have a data management plan for their organization. However, it is likely that it will need to be superseded by a more effective data-centric approach. This new data-centric strategy should include the following components: business strategy, self-assessment, data architecture, organizational responsibilities, data governance, and a roadmap. For more details on the components of an overall data strategy, see Analytics8’ article, 7 Elements of a Data Strategy.

Most importantly corporate executives need to focus exclusively on the business strategy component when implementing this new data-centric approach. Without a doubt, organizational leadership’s commitment and involvement is vital to effect change. This completes the first step of the Data-Centric Business Strategy Checklist, Business Leadership Commitment. The next step in this checklist is for corporate leadership to establish a data-centric-criteria for the IT department and its project teams.

Step 2 – Establish Data-Centric Criteria: Guidelines for IT Project Teams to Follow.

With a commitment to a data-centric mindset, executives can now 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 to drive a successful data-centric strategy, executives will need to establish and communicate clear data-centric guidelines, particularly to the IT department. This is because IT project teams will be the main drivers for shaping your organization’s new data-centric approach. Unquestionably, they are the ones on the ground level implementing new IT capabilities to include how your organization treats data. At the same time, these new guidelines for project managers should not adversely affect project-level goals or efficiencies. In fact, in many cases, these IT guidelines should help project teams to be more efficient and greatly enhance their resulting data products. The specific guidelines outlined below are designed to support this transformation.

Examples of Data-Centric IT Project Guidelines

Data-Centric Advice to Reduce IT Complexity and Make Tech Remarkably More Useful

• Reduce the Complexity and Number of Data Models.
• Reduce Duplication of Data.
• Improve Quality of Critical Data.
• Reduce Software Codebase.
• Favor Data-Centric Technologies and Methodologies.
• Avoid Application-Centric Security Solutions.
• Minimize Custom Data Integrations.
• Use Measurable, Understandable Definitions for Key Business Terms.

For details and examples on how to get started with these data-centric guidelines, see my article, IT Project Data-Centric Guidelines: Results That Are More Informative, Time Sensitive, And That Empower Business Data.

Step 3 – Agree on Key Operational Definitions: Assure Business Terms Are Measurable for Mutual Understanding and Interoperability.

To develop a successful data-centric business strategy, corporate leadership must establish clear, shared operational definitions of key business glossary concepts and terms. In most organizations these key business terms number only in the hundreds. To elaborate, examples of key business terms in the supply chain industry include “shipped”, “invoice”, “seller”, and “ETA”. Surprisingly, many organizations and departments will have different understanding of these key terms, routinely resulting in misunderstandings. As a result, both organizations and their systems become overwhelmed by ambiguous data that yields little insight, leading to poor decisions.

To illustrate the problem with the ambiguous glossary 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.

Ambiguous Business Glossaries Cause Misunderstandings: Detailed Example – “Shipped”

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

• 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”. 

Amazingly, the term “shipped” is just one example of hundreds within one industry of how a business term can be misunderstood by different stakeholders. 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. 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.

Step 4 – Executive Follow Through: Engaged Executive Leadership Needed to Implement a Data Centric Strategy.

Without a doubt, executives need to provide on-going leadership to implement a successful data-centric strategy. This means corporate executives need to provide effective guidance and oversight in how their corporate data is managed.  

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

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

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.

Step 5 – Prioritize IT Data-Centric Implementations and Minimize Application-Centric Initiatives.

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 data centricity forward within IT projects.

Tips for Prioritizing Data-Centric Implementations

• Start Small. To gain organizational confidence in this new data-centric approach, start small to demonstrate tangible, cost-saving results. Adjust approach as necessary and then go forward with more ambitious projects.
• Prioritize Data-Intensive Projects. It is key to quickly identify upcoming data-intensive IT projects and identify how best to apply data-centric criteria to that project. These types of projects offer great opportunities to eliminate data silos, improve data quality, and provide a unified view of enterprise data.
• All IT Projects Follow Data-Centric Criteria. It is critical to review all IT projects immediately, both current and future, and have their teams figure out how best they can incorporate data-centric criteria into their projects and solutions. This will require collaboration across the organization to determine how best to enhance enterprise data as a whole.

Conclusion.

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. 

Lastly, if you are in the supply chain industry and need help to implement a data-centric strategy, please contact me to discuss next steps. I have implemented 100s of tech pilot projects and innovative solutions across the supply chain as well as all transportation modes. I specialize in proof-of-concepts (POC) for emerging technologies and data-centric software development methods. To reach me, click here to access my contact form or you can find me on LinkedIn.

More References

For more references used in this article and to develop a data-centric business strategy, see below.

• 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.

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