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, 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.

1. Examples of IT Project Use Cases: Goals, Expected Outcomes, and Data Products.

To illustrate the practical application of data-centric guidelines, let’s first look at a few IT project use cases. The two examples below describe the IT project’s goals, expected outcomes, and the data products that you may expect to find in a typical organization.

Examples of Project-Level Use Cases 

1. Enhance 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%.
   • Data Products: Customer behavior patterns, purchase history, and feedback data
2. Optimize Supply Chain.
   • 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.

In this article on data centricity, I use these IT project use cases to show that there’s no need to change the fundamental objectives of an IT project. What may change, however, is the approach the IT team takes to meet those objectives. With corporate-level data-centric guidelines, the IT team will likely adopt slightly different technical solutions to make the data more valuable. Hence, this technical shift will ensure that the data is more useful and aligned with the company’s broader goals of data-centricity.

Now, what will change is the nature of the data products themselves. Indeed, the data generated from the IT project or system will no longer be a by-product or for exclusive use. Instead, the IT project team is now focused on making project or software generated data a corporate-level data product with data-centric characteristics. As a result, the project team will make the data a more valuable, permanent asset for the organization. Thus, the team is both meeting the IT project goals and also meeting corporate-wide data centric goals. Next, let’s talk about eight specific data-centric guidelines that will make this transition happen.

2. Eight Data-Centric Guidelines for Superior IT Project Results.

More and more businesses are realizing that their data is a strategic asset. The question is how do you optimize your corporate data so that it can be fully leveraged across the organization to maximize insights? This is where data-centric guidelines can help businesses turn their data into a strategic asset. The place to start is with their next IT project. Indeed, I recommend that all IT projects start following data-centric guidelines. To detail, below are my suggested guidelines to help turn your business data into a strategic asset.

Examples of Data-Centric Project Guidelines

a. 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.

b. 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).

c. Improve 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 that report’s data to assure accuracy. 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 

d. Reduce Software Codebase.

Also, it is key to data centricity to minimize unnecessary software code. This is because less code improves software performance and reduce 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. This reduces the overall codebase, 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 codebase is more challenging. This is because many programming teams use AI to generate code. While this speeds up development, it can lead to complex, hard-to-manage “spaghetti” code. So, software teams should continue using AI, but provide oversight to minimize bloat and the negative consequences of large codebases.

e. Favor Data-Centric Technologies and Methodologies.

For this data-centric guideline, 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

f. Avoid Application-Centric Security Solutions.

For this guideline, implement security measures that protect data across all applications, 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, this ensures consistent security policies and reduces the risk of data breaches, protecting both the business and its customers.

g. 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

h. 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 to jump start your organization to start getting better, actionable insights from your data. Specifically, your IT project teams can start using these data-centric guidelines to produce and transform business 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.

Lastly, these guidelines are just one component of a Data-Centric Business Strategy Checklist that I developed. This checklist is designed to help executives with leading their organization’s shift to a data-centric business. Specifically in this checklist, I detail the critical executive-level tasks required for success. This includes establishing enterprise-wide data-centric guidelines for IT projects and driving consensus on key business terminology. Bottom line, this checklist provides a pathway to success for executives to better leverage their organization’s most important asset, their data. 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

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