Let’s be honest: most organizations aren’t “data-driven”—they’re data-drowning. I’ve spent years watching brilliant teams squint at dashboards that are little more than a combination of data fragments – disjointed, duplicated, ambiguous, inaccurate, incomplete, and, worse, out-of-date. The problem: our IT is obsessed with software applications and not on the data sources that fuel rapid, informed decision-making. Without a doubt, we’ve spent decades obsessing over the latest applications while treating the underlying data like an afterthought, a byproduct. However, more data-savvy businesses are shifting away from this application-centric mindset and toward a data-ready way of thinking.

In this article, I’m laying out a Data-Ready Strategy – a no-nonsense approach to move your organization from data chaos to a digital framework that produces on-demand, cost-effective insights. This data-first approach includes establishing enterprise-wide data-ready criteria for IT projects and driving consensus on key business terminology. Also, this checklist includes steps for executives to keep their data-ready strategy on track. Lastly, I’ll provide tips on how executives can prioritize IT projects to swiftly realize the benefits of data readiness.

Step 1 – Executive Commitment: Pivoting IT Strategy from Software-First to Data-First.

Despite being tech-savvy, most modern decision-makers still struggle to harness the wealth of information trapped in their systems. Their data is often siloed, duplicated, and of such poor quality that it is virtually unusable—and certainly untrustworthy. As a result, this information is not “ready” to support high-velocity insights or fuel advanced AI automation. To bridge this gap, we must pivot our IT strategy from software-first to data-first, a transition that begins in the boardroom, not the server room. Transforming into a data-ready organization requires a clear executive mandate; but first, let’s look at what “data-first” means and why it’s a strategic necessity.

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

Jay Baer

a. A Data-Centric Mindset: Putting Data First.

The first shift toward data readiness is for businesses 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. This is the first step toward data readiness and the organizational capability to make rapid, informed decisions. For those of you not familiar with the term data-centric, below is a definition.

Data Centric Definition

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

“Data centric refers to an architecture where data is the primary and permanent asset, and applications come and go … the data model precedes the implementation of any given application …”

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. A Data-Ready Business: Timely, Targeted Access to Relevant Information.

From a technical perspective, “data-centric” is a key principle for IT to follow, truly making data a primary and permanent asset. As a result, IT starts prioritizing data models over any given software application. At the same time, this is only the initial step for a business to be “data-ready”. Also, businesses must align their data to their decision processes. Data-Centricity is not enough to enable rapid, informed decision-making across the organization. Business leaders need to also prioritize data, linking it to corporate goals and use cases. As a result, this data-ready approach becomes both cost-effective and information rich, not data overload. What is needed is timely, targeted access to relevant information. See below for a definition of data readiness.

Be Data Ready: It’s About Relevant Information — Targeted and Timely — for the Best Business Decisions

Data-Ready Definition

“Data readiness is the state of being fully prepared to use data effectively for analysis, decision-making, and operational goals.”

For a more detailed discussion on data readiness, see my article, Be Data Ready: It’s About Relevant Information — Targeted and Timely — for the Best Business Decisions. Here, I share a 5-step decision-making process that a “data-ready” organization would use to rapidly gather targeted data, consisting of only the relevant information needed to make a decision.

c. The Business Benefits of Data-Readiness.

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-ready business. At the same time, this new way of doing business is challenging for organizations that are accustomed to an application-centric approach. However, the benefits of a data-ready 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
• Rapid, Informed 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 Knowledge Graphs.
• Streamlined Data Security, Integration, And Analytics

For more details on the benefits of being “Data First” , see my article, Data-Centric Benefits: Businesses Becoming More Innovative By Not Being Mired In Application Centricity.

d. Committing to A Data-Ready Strategy.

As discussed, data readiness is a must to remain competitive, enabling modern businesses to make rapid, informed decisions and fully leverage AI’s potential. As such, corporate executives must commit to a data-ready strategy. Now, at the same time, many organizations may already have a data management plan for their organization. Hence, executive leadership must revamp any current data management plans to align with a new data-ready strategy. For more detailed discussion on the shortcomings of traditional Enterprise Data Management, click here.

Lastly, with this new data-ready approach, corporate executives work across their organization, focused on developing and implementing the overall business strategy. In turn, this drives the overall data management strategy for the organization. Without a doubt, corporate leadership’s commitment is vital to effect change. This completes the first step of the Data-Ready Business Strategy Checklist, Executive Commitment. The next step in this checklist is for corporate leadership to establish a data-ready criteria for the IT department and its project teams.

“… we must pivot our IT strategy from software-first to data-first, a transition that begins in the boardroom, not the server room.”

Step 2 – Setting the Standard: Defining “Data-Ready” Protocols for IT Projects.

With a commitment to data readiness, executives next focus their business strategy on guidelines for IT projects and their data deliverables. At the same time, these guidelines will not need to fundamentally alter current corporate IT goals or project management methodologies. To fulfill the data-first mandate, leadership must establish and communicate clear “Data-Ready” protocols across all departments, particularly within the IT teams responsible for ground-level execution. Far from creating a new layer of complexity, these guidelines are designed to enhance existing efficiencies, ensuring that IT project teams move beyond simple functional outputs to deliver superior data products. The following guidelines provide a practical roadmap to transform your data ecosystem into a high-velocity, data-ready reality.

Ten Data-Ready Protocols for Superior IT Project Results

Data-Ready Guidelines for IT Projects: Delivering On-Demand, Cost-Effective Insights to Every Decision-Maker

• Reduce the Complexity and Number of Data Models.
• Reduce Duplication of Data.
• Decrease Software Code Base.
• Avoid Application-Centric Security Solutions.
• Minimize Custom Data Integrations.
• Use Measurable, Understandable Definitions for Key Business Terms.
• Directly Link IT Data Deliverables to Align with Corporate’s Data-Ready Strategy.
• Targeted Data Quality Based on Criticality.
• Favor Data-Ready Technologies and Methodologies.
• Architect for Data-Ready Access: Delivering Corporate-Wide, On-Demand AI Insights

For details and examples on how to get started with these data-centric guidelines, see my article, Data-Ready Guidelines for IT Projects: Delivering On-Demand, Cost-Effective Insights to Every Decision-Maker.

“… leadership must establish and communicate clear “Data-Ready” protocols across all departments, particularly within the IT teams responsible for ground-level execution.”

Step 3 – Commit to Universal Taxonomies: Establishing a Common Language for Humans and AI

To achieve true data readiness, leadership must establish a common language of key business terms for both humans and AI. This is not an IT task. In most industries, these critical business terms number only in the hundreds. However, when these business lexicons lack clarity, it is devastating to decision-making. In the supply chain industry, for example, terms like “shipped,” “invoice,” and “ETA” are often interpreted differently across departments, leading to serious misunderstandings. As a result, this semantic ambiguity overwhelms both organizations and their systems, resulting in high-cost, low-insight data that fuels poor decisions. To illustrate the scale of this problem, the example below details how the single term “shipped” can be misinterpreted across a typical supply chain.

Example of Varying Business Definitions: “Shipped”

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

• Carrier In Possession of Shipment. This unambiguous definition is less likely to be misinterpreted or construed. It’s measurable and conveys a meaningful event – The carrier took possession of the shipment and it is in transit.
• Barcode Label Printed. Many systems will generate a “shipped” status when the shipper prints a shipping label. At best, this is misleading causing misunderstandings.
• Ready For Pickup. Again, many shipper’s systems will generate a “shipped” status when the shipment is still on the shipping dock.
• Shipment Loaded on a Trailer. In this case, a shipper may communicate that a package is “shipped”, but in reality the package is in a trailer in the dockyard ready for the carrier to pick it up.
• Absolutely Nothing. Lastly, I have seen cases where someone queries a tracking system with an erroneous tracking number such as “123”. The tracking system then responds with a “shipped” status.

Amazingly, the term “shipped” is just one example of hundreds within one industry of how a business term can be either misconstrued or misunderstood by different stakeholders. Without a doubt, business leadership is needed to drive out ambiguity when it comes to key business terms. What organizations need to do is update their business lexicons and glossaries to provide measurable, operational definitions – not just loosely-defined dictionary definitions. 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.

“… business leadership is needed to drive out ambiguity … update their business lexicons and glossaries to provide measurable, operational definitions – not just loosely-defined dictionary definitions.

Step 4 – Executive Stewardship: Empowering the Enterprise with a Data-First Strategy.

Executive engagement shouldn’t end with a signed check; it requires ongoing stewardship to assure the successful implementation of the data-ready strategy. This means moving beyond passive “sponsorship” to active empowerment – ensuring that IT teams have the authority to prioritize data readiness over creating just one more software silo. As stewards, leadership must bridge the gap between technical delivery and business value, constantly reinforcing the data-first mandate across every department. This top-down pressure ensures that the organization remains disciplined, preventing the “drift” back into the comfortable but dangerous habits of application-centric thinking. Below are key functions of executive stewardship to drive a data-ready strategy.

Executive Stewardship Functions to Drive a Data-Ready Strategy

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

• Refocus Data Management: From Control to Maximizing Insights. Stewardship requires moving data management policies beyond a purely defensive, compliance-based control model. Thus, executives must lead the transition from “protecting the data” to “maximizing its utility” across the enterprise. For a more detailed look at the shortcomings of traditional Enterprise Data Management, click here.
• Assure that Data Deliverables Are Linked to Business Outcomes. Stewardship ensures that every IT data project is explicitly mapped to a specific business use case rather than a generic technical requirement. By enforcing this alignment, leadership guarantees that technical outputs translate directly into measurable value and high-velocity decision-making.
• Champion the Shift to a Data-Ready Culture. A data-ready culture doesn’t happen by accident; it requires a top-down mandate that fosters both data readiness and high-velocity analytics, resulting in rapid, informed decision-making over “gut instinct” decisions. Executive stewards must consistently message this shift, ensuring that every department views data readiness as the key ingredient for business success.

” As stewards, leadership must bridge the gap between technical delivery and business value … preventing the “drift” back into the comfortable but dangerous habits of application-centric thinking.

Step 5 – Reallocating IT Investment: Building Data-Ready Infrastructure Versus Legacy Application Silos.

The final step with implementing a data-ready strategy is a cold, hard look at the balance sheet. To build a cost-effective, data-ready future, we must re-look at how we are prioritizing IT projects. Reallocating IT investment toward a data-ready infrastructure is a strategic rationalization: it’s about choosing a scalable, interconnected ecosystem over fragmented, one-off software solutions. By prioritizing IT investments that promote data readiness, organizations can drastically reduce the cost of insights while building the high-velocity infrastructure necessary to outpace the competition. Below are some tips to prioritizing IT projects that advance data readiness.

Tips for Prioritizing Data-Ready Implementations

• Start Small. To gain organizational confidence in this new data-ready 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-ready criteria to that project. These types of projects offer great opportunities to eliminate data silos, improve data quality, increase accessibility and provide a unified view of enterprise data.
• All IT Projects Follow Data-Ready 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-ready criteria into their projects and solutions. This will require collaboration across the organization, especially with business leaders, to determine how best to enhance enterprise data as a whole.

“By prioritizing IT investments that promote data readiness, organizations can drastically reduce the cost of insights while building the high-velocity infrastructure necessary …”

Conclusion.

Indeed, innovative companies are starting to make a critical shift to bring order to their business data by adopting a data-ready 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-ready businesses can make rapid, 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 readiness. 

More References

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

• FAIR Principles: GO FAIR’s FAIR Principles. The FAIR principles provide guidance on making data more “machine actionable”.
• Enterprise Data Models: TDAN’s article, The Enterprise Data Model. This article details a data-centric approach for managing data. Also, Markus Harrer’s blog posting, Why Enterprise Data Models don’t work. This posting spells out the challenges and dangers of enterprise data models.
• Data Strategy: Amplitude’s article, What is Data Strategy? Guide with Examples, that identifies the crucial elements of a data strategy.
• Master Data Management: Atlan’s article, Difference between Master Data Management(MDM) and Metadata Management.
• Competency Questions to Drive Data Engineering: Mark S. Fox’s paper, The Role of Competency Questions in Enterprise Engineering.
• High-Velocity Decision-Making: SC Tech Insights’ article, High-Velocity Decision Systems for Executives: The Three Ways To Best Exploit AI Tech And Data Analytics

High-Velocity Decision Systems for Executives: The Three Ways To Best Exploit AI Tech And Data Analytics

Need help with an innovative solution to make your supply chain data ready? I’m Randy McClure, and I’ve spent many years solving data readiness challenges to help decision-makers gain better, faster insights and for organizations to leverage data-intensive technologies. As a supply chain tech advisor, I’ve implemented hundreds of successful projects across all transportation modes, working with the data of thousands of shippers, carriers, and 3rd party logistics (3PL) providers. I specialize in pilot projects and program management for emerging technologies. If you’re ready to modernize your data infrastructure or if you are a solution provider, let’s talk. To reach me, click here to access my contact form or you can find me on LinkedIn.

For more from SC Tech Insights, see the latest articles on Data and Information Technology.