For years, I’ve watched companies invest heavily in software systems, boosting efficiency but also creating rigid, monolith applications. In today’s fast-paced, data-intensive world, this approach is a liability. The main culprit is that the data from these systems are treated as a mere by-product, yielding few insights for rapid, informed decision-making. So, as a result of this neglect, our data is disjointed, duplicated, ambiguous, inaccurate, incomplete, and out-of-date. Hence, all too often leadership teams are forced to rely on “gut instinct” because of their fragmented data and delayed reporting systems. It’s time for a change. To remain competitive in today’s digital world, businesses need to shift to being “data-ready”. This is where their IT infrastructure supports rapid, informed decision-making.

In this article, I’ll introduce to you ten “data-ready” guidelines that will jump-start your organization’s transition from fragmented silos to timely, actionable insights. Moreover, your IT project teams can start using these guidelines today to turn ambiguous data into a high-velocity engine for bold, informed decisions. Without a doubt, your toughest competitors are starting to build data-ready infrastructures. Are you? Please, read on to find out how you can align your IT data delivery with on-demand analytics, enabling high-velocity insights for every decision-maker within your organization.

The Data Evolution: Treating Information as a Strategic Asset, Not a Functional Byproduct.

In a world where rapid, informed decision-making is a competitive necessity, businesses must recognize that their data is their primary source of insight. Hence, a shift is needed for organizations to become “data-ready”. First, they must realize the need for a data-centric mindset, acknowledging that while software and automation are transient, enterprise data is a permanent strategic asset. Second, leadership must prioritize seamless access to raw data and organizational knowledge to enable high-velocity, on-demand analytics. This shift requires us to rethink traditional IT infrastructure, moving away from IT projects that just create new data products in isolation. By examining the consequences of these legacy approaches, we can better understand the urgent need for a disciplined,“data-ready” strategy.

a. Typical IT Projects Treat Corporate Data as a By-Product – an Afterthought

Let’s start with an example of the typical objectives of an IT Project. For instance, below are the IT project objectives to “Optimize a Supply Chain System”.

• IT Project 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%.
• The “Omitted” Data Byproducts and Guidance: For example, the project scope should detail which data products are most critical, guidance for syncing new data to organizational knowledge bases, and identify which data needs accessed real-time or batched?

This example highlights that data deliverables are conspicuously absent from most IT project goals and objectives. This includes a lack of specificity for both the data generated during the IT project and on-going data produced from any resulting new system updates. This common oversight leads to data generated—both initially and ongoing—becoming a mere by-product rather than a valuable corporate asset.

b. Corporations Treating Data as a By-Product: A Recipe for Disaster in this Digital Age.

In this digital age, any business that continues to treat data as a byproduct is a disaster waiting to happen. Without a doubt, data is now critical for rapid, informed decision-making and to fuel data-intensive technologies such as AI. To detail, below are the severe consequences of businesses not treating their data as a valuable, permanent asset.

• Continued Proliferation of Useless Data. Without changes in business practices, ongoing IT projects will continue to perpetuate disjointed, duplicated, outdated, and inaccurate data. As a result, this inevitably leads to few insights and further data degradation over time, despite diligent IT maintenance.
• Not “Data-Ready” for New Information Technologies Such as AI. Also, the continued practice of treating data as a byproduct limits businesses from leveraging existing data for new IT projects using data-hungry technologies such as AI. In fact for many businesses, it is becoming a common occurrence that corporate data for a new project is not “data-ready“.
• Fragmented Decision-Making Is a Competitive Disadvantage. Without a “data-ready” foundation, organizations remain reliant on “gut instinct” as their slow-moving digital infrastructures yield too few insights because of fragmented data and delayed reporting systems. While “data ready” competitors, when markets shift, make rapid, informed decisions in a cost-effective manner.

“… rapid, informed decision-making is a competitive necessity, businesses must recognize data as their primary source of insight. Hence, a shift is needed … to become ‘data-ready‘. “

Indeed, both businesses and IT need a shift in focus. What is needed are data-ready guidelines. As a result, IT teams can achieve both project goals and corporate data-ready goals. So, let’s look at data-ready guidelines for IT project teams.

Ten Data-Ready Guidelines for Superior IT Project Results.

Without a doubt, data-ready guidelines can help businesses turn their data into a strategic asset. The place to start is with their next IT project. Undoubtedly when it comes to enterprise data, IT projects cannot work in isolation. However, most IT teams lack the data-ready guidance to help their company nurture and make enterprise data more valuable. The bottom line, IT needs corporate-level data-ready guidelines. Based on my years of experience implementing hundreds of data-ready projects, below are recommended guidelines to help turn your business data into a strategic asset.

1. Reduce the Complexity and Number of Data Models.

Knowledge Graph Tech: Enabling A More Discerning Perspective For AI

First, have your IT project teams focus on simplifying their data models to enhance clarity and reduce maintenance costs. For example, when developing a new software application avoid creating a new data model. Ideally, use a single, unified data model instead of multiple, siloed models. For instance, look at using knowledge graph tech to link existing data structures together. Bottom line, reducing data model complexity simplifies data management, reduces errors, and increases business agility. Moreover, It makes it easier for both current and future stakeholders to understand and use the data.

“Ideally, use a single, unified data model instead of multiple, siloed models.”

2. Reduce Duplication of Data.

Also, have your IT teams look for opportunities to eliminate redundant data to improve accuracy and streamline processes. For example, many software applications contain customer profile data. In this case, the IT team could look to share customer profile data through a common data structure versus having many copies and versions of the same data. As a result, this solution would ensure that customer information is consistent across sales, marketing, and customer service. Further, this reduces the risk of data discrepancies and improves customer service. Lastly, by eliminating duplicate and one-off datasets, organizations can establish a Single Source of Truth (SSOT), drastically increasing confidence in their data.

“… by eliminating duplicate and one-off datasets, organizations can establish a Single Source of Truth (SSOT), drastically increasing confidence in their data.”

3. Decrease Software Code Base.

Additionally, reducing your software code base makes your business more “data ready” . This readiness benefits AI performance, traditional automation, and decision-makers. This is because less complex code improves software execution, reduces complexity of software updates and minimizes code errors. Also, this simplifies software portfolios, lowering both initial and ongoing maintenance costs. For example, when building a new ecommerce platform, use modular and reusable code components such as a headless ecommerce solution. This reduces the overall code base, making the system easier to maintain and less prone to bugs, which improves performance and user experience.

However, some IT programming teams are using AI to “vibe code”, resulting in an increase in code base and making their business less “data ready”. While this speeds up development, it can lead to complex, hard-to-manage “spaghetti” code. So, software teams, regardless if they use AI, need to have mechanisms in place to minimize bloat and the negative consequences of large code bases. For example, developers can minimize code base by leveraging data structures to store business logic, instead of embedding specific business rules within the software code.

“… less complex code improves software performance, reduces complexity of software updates and minimizes code errors.”

4. Avoid Application-Centric Security Solutions.

In this case, IT needs to implement security measures that protect data across all applications and not at the application level. Otherwise, what happens is that business data gets locked up within each software application with different flavors of data security. For example, a business could implement a centralized identity and access management (IAM) system that secures data across all applications. As a result, the implementation of digital identify tech at the enterprise level ensures consistent security policies and reduces the risk of data breaches, protecting both the business and its customers.

“… implement security measures that protect data across all applications and not at the application level.”

Best Use Of Digital Identity Technology In The Supply Chain

5. Minimize Custom Data Integrations.

Data Interoperability Tech: It Is More Than Integrations – It’s Understandable, Secure, And High Velocity Technology

Without a doubt, we need to move away from custom data Integrations to improve data readiness. First, customized data integration projects can take months to implement, using expensive IT resources. Also, these “one-off” solutions often break when an interfacing business software application requires updating. Moreover, without standardized, easy-to-access interfaces, data gets locked in software functional silos. Hence, it is critical to keep data Integrations simple, and standardized where possible. For example, use pre-built connectors and APIs to integrate a new CRM system with existing ERP and marketing tools. Hence, moving away from custom data integrations reduces implementation efforts and ensures that data flows smoothly between systems, improving operational efficiency.

For more ideas on technical solutions to reduce custom data integrations, see my article, Data Interoperability Tech: It Is More Than Integrations – It’s Understandable, Secure, And High Velocity Technology.

“… moving away from custom data integrations reduces implementation efforts and ensures that data flows smoothly between systems, improving operational efficiency.”

6. Use Measurable, Understandable Definitions for Key Business Terms.

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

This particular guideline cannot be overemphasized – ensure everyone is on the same page with clear, consistent business definitions. Indeed, both systems and businesses need clear, measurable definitions. For example, when Company A’s “urgent order” means“ship within 24 hours” but Company B’s means “ship next available,” no amount of technical integration will prevent service failures. Indeed, if businesses and their IT teams followed this data-ready guideline, they will both Improve interoperability and streamline data analytics 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.

“… both systems and businesses need clear, measurable definitions.”

7. Directly Link IT Data Deliverables to Align with Corporate’s Data-Ready Strategy.

Data-Driven Decision-Making: Its Enormous Impact And The Truth On Limitations

Historically, businesses have measured IT success by its application and hardware milestones—often relegating data as just a byproduct. To treat data as a strategic asset, project teams must shift to a “data-ready” delivery model tied to corporate goals. For example, as part of a corporate data strategy, an organization can provide IT project teams rigorous policy guidance on topics such as schema design and seamless integration with existing corporate ecosystems. These strategic priorities ensure project-level digital outputs aren’t just stored, but are immediately usable corporate-wide. Ultimately, these data deliverables must be recognized as permanent, high-value resources designed to fuel on-demand insights for every decision-maker across the enterprise, long after an IT project’s final deliverable.

It may be a statement of the obvious, but data is key for decision-making and for fueling data-intensive technologies like AI. In fact, by treating data as a strategic asset, it becomes the trusted source for every insight and every critical decision made within an organization. For more on how data impacts business decision-making, see my article, Data-Driven Decision-Making: Its Enormous Impact And The Truth On Limitations.

“… data deliverables must be recognized as permanent, high-value resources designed to fuel on-demand insights for every decision-maker across the enterprise …”

8. Targeted Data Quality Based on Criticality.

A robust corporate data strategy must distinguish between critical and non-essential information to ensure that resources are allocated in a cost-effective manner. Not all data is created equal; therefore, the strategy must provide clear guidance on which assets require high levels of accuracy, completeness, and real-time updates versus those that can be handled by less intensive quality processes. For example, if financial report accuracy is vital for corporate decision-making, the project team must prioritize the integrity and timeliness of that specific data stream. Conversely, data that is not critical to decision-making is not prioritized. By tiering data requirements based on their strategic impact, organizations can maximize the quality of the insights that drive innovation while maintaining operational efficiency.

“When everything is a priority, nothing is a priority.”

Karen Martin 

9. Favor Data-Ready Technologies and Methodologies.

For instance, choose tools and methods that prioritize data integrity and accessibility. This also means avoid locking your data into application-centric architectures that grow rigid with age. Without a doubt, this particular guideline will lead to faster adoption of emerging data-intensive technologies such as AI, IoT, and data analytics tools. 

Moreover, there are many emerging data-centric methodologies in how IT can use existing and emerging technologies. For example, these include data management, AI development, knowledge graphs, and enterprise architecture to name a few. For more information, see my article, Data-Centric Business Tech: New And Better Ways To Shatter Our Obsession With Software And Automation.

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

“… avoid locking your data into application-centric architectures that grow rigid with age.”

10. Architect for Data-Ready Access: Delivering Corporate-Wide, On-Demand AI Insights

How Data And AI Work Together To Better Empower Analytics

To move beyond static reporting, both IT architecture and their project teams must transition from a “request-and-wait” model to a “data-ready” foundation for high-velocity decision-making. Without a doubt, both AI and data do not work well in fragmented silos. Their power is entirely dependent on an underlying architecture that ensures data is “accessible, accurate, and ready for use” across the enterprise. 

For example, when it comes to decision-making, a global organization could move beyond isolated departmental databases toward an API-driven, microservices ecosystem. This type of approach allows any authorized stakeholders – from a regional manager to a CFO to an AI agent – to pull up-to-date performance metrics on-demand. By prioritizing access at the architectural level, organizations create an environment for high-velocity decision-making necessary for executives. As a result, corporations can turn their raw data, information, and knowledge bases into immediate, on-demand insights that drive competitive advantage. For more on this topic, see my articles, High-Velocity Decision Systems for Executives and How Data And AI Work Together.

“… corporations can turn their raw data, information, and knowledge bases into immediate, on-demand insights that drive competitive advantage.”

Next Steps.

In this article I introduced you to ten data-ready guidelines that aid IT departments with transforming their organization’s data into a strategic asset. At the same time, these recommended IT guidelines need to be part of a broader business strategy for an organization to take on a data-ready mindset. To aid businesses, I have developed a Data-Ready Business Strategy Checklist designed to help executives transform their organization into a data-ready business. For details, see my article, A Data-Ready 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

References.

For more references on how executives can proceed with a data-centric strategy, see the following articles:

• Kevin Doubleday’s article, Introduction to Data-Centricity. 
• 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. 
• Also, see my article, Agile Supply Chain Decision-Making: First You Need to Know The Truth About Enterprise Software, for more on the challenges of relying solely on Enterprise Software for corporate decision-making.
• For more on being data-ready, see my article, Be Data Ready: It’s About Relevant Information — Targeted and Timely — for the Best Business Decisions

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

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.

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