For years, companies have invested heavily in software systems and automation, boosting efficiency but also creating rigid, monolith applications. In today’s fast-paced, data-centric world, this approach is a liability. The data produced by these systems often yields few insights. Moreover, the data is disjointed, duplicated, ambiguous, inaccurate, and incomplete. It’s time for a change. To stay agile and innovative in today’s digital world, businesses need to shift from being application-centric to data-centric. One way to start this transformation is for corporate leaders to provide their IT projects teams with data-centric guidelines to follow.

In this article, I’ll introduce you to eight data-centric guidelines to jump start your organization to get better, actionable insights from your data. Moreover, your IT project teams can start using these guidelines today to produce and transform data into a strategic asset, not a software by-product. If your organization starts following these guidelines, you will quickly turn your disjointed, ambiguous data into a corporate asset. As a result, your data will yield unprecedented Insights and enable bold, informed decisions throughout your organization.

Shifting the IT Focus: Treating Data as a Valuable Business Asset, Not Just a Byproduct.

As businesses increasingly acknowledge the value of their data, it is becoming more obvious that they need to adopt a data-centric mindset. More specifically, this data-centric viewpoint realizes that technology, software applications, and automation will come and go, but enterprise data will never grow obsolete. Hence, to embrace this data-centric approach, means that the IT department needs a change in focus, no longer treating data as a by-product. This also means that IT projects teams need to reorient their efforts around data, rather than just implementing new software or services. To get a better understanding of this change in IT focus, let’s look at some examples of IT projects, specifically project goals, expected outcomes, and data products. See below.

Typical Examples of IT Project Goals, Expected Outcomes, and Data Products 

1. Enhance Customer Relationship Management Project.
   • 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%.
   • Data Products: Customer behavior patterns, purchase history, and feedback data
2. Optimize Supply Chain 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%.
   • Data Products: Supplier performance metrics, logistics data, and demand forecasts.

Unquestionably, successful IT projects will always meet their project goals and expected outcomes, but what about the data products? That’s the problem. In many cases, the resulting data products such as purchase history or logistics data are treated as a byproduct. Further over time, the data slowly degrades even though the IT department diligently performs software updates and data backups. Worst, seldom is this data from one project “data-ready” for new IT initiatives and emerging technologies such as AI. Indeed, IT needs a shift in focus. What if IT used data-centric guidelines? As a result, IT teams would achieve both the project goals and corporate data-centric goals. So, let’s look at data-centric guidelines for IT project teams.

Eight Data-Centric Guidelines for Superior IT Project Results.

Without a doubt, data-centric 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-centric guidance to help their company nurture and make enterprise data more valuable. The bottom line, IT needs corporate-level data-centric guidelines. Based on my years of experience implementing hundreds of data-centric 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.

2. Reduce Duplication of Data.

Also, have your IT teams look for opportunities to eliminate redundant data to improve accuracy and streamline processes. For instance, 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, with fewer copies and one-offs of data sets, organizations will have increased confidence in data with one Single Source of Truth (SSOT).

3. Improve the Quality of Critical Data.

Here, ensure that the most important data is accurate, complete, and reliable. Indeed, not all data is the same. Thus, allocate resources to improve the quality of the data that drives decision-making, innovation, and competitiveness. For instance, if accuracy of a financial report is critical to corporate decision-making, then focus resources on making sure that particular report’s data stays up-to-date and complete. On the other hand, non-essential data such as “Henry’s favorite color is blue” does not need the same level of accuracy or quality checks.

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

Karen Martin 

4. Reduce Software Code Base.

Also, it is key to data centricity to minimize unnecessary software code. This is because less complex code improves software performance and reduces 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.

Now in the age of AI, reducing the code base is more challenging. This is because many programming teams are starting to use AI to generate code. 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.

5. Favor Data-Centric 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 that IT can use. You can find these more data-centric methodologies in the areas of data management, AI development, knowledge graphs, and enterprise architecture. 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

6. 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.

7. Minimize Custom Data Integrations.

Bottom line, this data-centric guideline will help to streamline and even eliminate costly data integration efforts, both now and in the future. For example, use pre-built connectors and APIs to integrate a new CRM system with existing ERP and marketing tools. Hence, this reduces the time and effort required for integration, ensuring that data flows smoothly between systems and 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.

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

8. 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 guideline cannot be overemphasized – ensure everyone is on the same page with clear, consistent definitions. Indeed, both systems and businesses need clear, measurable definitions. For instance, 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-centric 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.

Next Steps.

In this article I introduced you to eight data-centric 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-centric mindset. To aid businesses, I have developed a Data-Centric Business Strategy Checklist designed to help executives transform their organization into a data-centric business. For details, 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

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. 
• Lastly, for more on what is 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, 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.

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