Most businesses make a point to keep their offices spotless, but when it comes to their data, many seem not to care. Business leaders often appear indifferent or even neglectful, leading to a very messy data environment. Indeed, most businesses’ data is fragmented, duplicated, somewhat ambiguous, and spread across disconnected systems that offer little insight. Large businesses, in particular, lack a solid enterprise data management strategy that provides effective guidelines to both safeguard their data and extract the maximum value from it. It is time for a change. This is because data – and more critically, information – has become the lifeblood of businesses to gain the insights necessary for making superior decisions and staying competitive.

This article is the first in a series about why businesses need a data-centric strategy for managing data. In this first article, I’ll look at why current data management often fails, highlighting the need for change. First, many businesses only try to control data instead of also focusing on maximizing its value. Further, even though most businesses can collect data efficiently, they find it hard to make it understandable and accessible for employees and business partners. Plus, current data management practices often increase security risks rather than reduce them. It’s clear that change is necessary. One solution is for business leaders to adopt a data-centric strategy that will empower effective data management within today’s highly competitive digital environments.

1. Enterprise Data Management Today Focuses More on Controlling Data, Not Making it More Valuable.

For decades, businesses have concentrated their data management policies on controlling system access to ensure compliance and prevent breaches. While these priorities remain crucial, they result in a narrow perspective, focusing primarily on privacy protection and the security of sensitive corporate information. However, more innovative organizations are now recognizing and treating their data as a strategic asset to fully leverage for greater competitive advantage.

So, this mindset of tightly controlling data to safeguard it often results in data being locked away in secure silos. Hence, it is difficult for employees and business partners to access it. As a result, businesses that use this approach to just control data hinder collaboration, slow decision-making, and prevent organizations from gaining insights to drive innovation. To get a better understanding of how most businesses manage their data, let’s start with a definition of data management.

Data Management Definition.

“Data management is the practice of collecting, processing and using data securely and efficiently for better business outcomes.”

IBM

So, Data Management aims to secure information and derive insights for better business decisions. However, many organizations concentrate on governance policies that solely focus on data protection and accountability. Consequently, employees often perceive data management as merely about control, red tape and bottlenecks. While data security is crucial, if organizations fail to extract valuable insights, the data becomes a costly liability instead of a strategic asset.

2. Organizations’ Data Strategies Are Too Focused on Collecting Data Versus Making Data Understandable and Accessible.

Let’s Breakthrough The Data Interoperability Nightmare: It Is The Best Way To Unlock Supply Chain Innovation

In today’s digital world, data pours into businesses at an unprecedented pace. Companies collect it from myriad sources, from customer and suppliers’ interactions to Internet-Of-Things (IoT) sensors. Yet, in the rush to amass data, many organizations neglect to ensure it’s understandable and accessible. Indeed, data remains in its raw form or is stuck in software application data silos with quality issues and inconsistencies. This renders the data, in many cases, worthless for insights and decision-making. For more on this data interoperability problem, see my article, Let’s Breakthrough The Data Interoperability Nightmare: It Is The Best Way To Unlock Supply Chain Innovation.

As mentioned above, prudent organizations typically prioritize Data Governance, but focus on policies that ensure compliance with both internal policies and external regulations. While many businesses concentrate on controlling their data through governance, another crucial aspect of Data Management is the development of a Data Strategy. A Data Strategy outlines how an organization will use data to achieve its business objectives and maximize the value of its data. To clarify the difference between a Data Strategy and Data Governance, here are some definitions.

Definitions of Data Governance and Data Strategy

“The primary objective of data governance is to ensure that data across the organization is standardized, secure, and used in compliance with both internal policies and external regulations. It aims to manage data as a valuable asset and mitigate risks associated with data handling.

“Data strategy, on the other hand, aims to identify how data can be utilized to achieve business objectives. It focuses on maximizing the value derived from data by identifying key opportunities for data analysis and utilization.”

Secoda

So, a data strategy should ideally focus on how a business will extract value from its data. Specifically, this involves organizations developing a business strategy for their data, defining organizational roles, establishing data architecture, and how they should manage data on a daily basis.

However, many times the leadership team will simply hand off a data strategy to IT or the data team without incorporating a real business strategy. As a result, the tech team lacks direction on what data is important or how it should be used. Consequently, they end up collecting massive amounts of data for different business areas like accounting, HR, and operations. Moreover due to limited business involvement, the data is often poorly defined and not structured to aid in corporate decision-making. As a result of an unclear business strategy, the business becomes overwhelmed with data but gleans few insights from it.

3. Enterprise Data Management Challenged to Secure Data When It is Fragmented, In Many Silos, Duplicated, and Ambiguous.

Lastly, data security is crucial in today’s world of rising cyber threats and regulations. However, many organizations face challenges because their data management practices actually make security more complicated, despite having formal data governance policies. This happens because data is often scattered across different applications and duplicated in various systems, offering more attack surfaces for hackers. Additionally, ambiguous data makes it harder for organizations to know where to apply appropriate data access controls. Moreover without a unified view of their data, businesses are challenged to detect anomalies and breaches. Lastly, the constant transfer of data between systems also adds risks. Consequently, businesses struggle to tackle their data security challenges. Specific data security challenges include:

Data Security Challenges

Data Sensitivity: What You Need to Know For Your Business

a. Assigning Data Sensitivity Levels to Data. 

Businesses today are drowning in data and it is difficult for them to know which data is important to secure. This is because, in many cases, data is not well defined. Thus, business data owners can end up spending unnecessary resources securing non-sensitive data. Worse, they end up not securing data that is highly sensitive. For more information on data sensitivity, see my article, Data Sensitivity: What You Need to Know For Your Business.

b. Protecting Application-Centric Data Silos. 

Indeed, securing data across a large set of disparate data silos is difficult at best. For instance, it is time consuming and difficult to assure compliance with a large number of unrelated data structures. Moreover, these data silos are controlled by software vendors that have varying degrees of security mechanisms within their software applications.

c. Securing Custom-Built Data Integrations. 

As businesses use more software applications to include cloud-based software services, they have a need to exchange more data between all these systems. As a result, businesses end up with many custom and proprietary data interfaces. In many cases these custom data integrations are not well documented, unsecure, and transferring duplicate, fragmented data across the enterprise. Thus, these custom integrations create a multitude of data security challenges, making the organization very vulnerable to hackers.

Custom-Built Shipment Statuses: Digital Supply Chains Can Do Better And Need A Reckoning To Eliminate This Insidious Habit

d. Capability to Verify, Authenticate, and Authorize Access to Business Data. 

Lastly, there are the challenges with business owners providing access to their data. As businesses continue to leverage more digital technologies, they need to provide access to not just humans, but also systems, devices, and even AI agents. For more discussion on the challenges of digital identities, see my article, Digital Identity In Logistics And What To Know – The Best Security, Scary Risks.

Digital Identity In Logistics And What To Know – The Best Security, Scary Risks

Next Steps.

To summarize, traditional data management is struggling as businesses become more digital. Although many companies have data governance policies, they still can’t keep up with the growing amount of data. This makes it hard for their organization to gain useful insights, authorize access to data, and safeguard their data. Indeed, business leaders need to rethink how they manage data, especially in large companies. Without a doubt, executives need to prioritize their enterprise data management policies to be more data-centric, treating data as a valuable asset, not just a back-office necessity.

In the next part of this series, I will look at the advantages of a data-centric strategy for enterprises managing data. This includes the need for business leaders to become less application-centric in their approach to data. Lastly, I’ll detail how businesses can develop a data-centric strategy for managing their data. See below for more articles from my series on a Data-Centric Strategy for Businesses, see links below

Articles on a Data-Centric Strategy for Businesses

• Traditional Enterprise Data Management Is Floundering To Make Business Data More Valuable, Accessible, And Secure (this article)
• Data-Centric Benefits: Businesses Becoming More Innovative By Not Being Mired In Application Centricity
• A Data-Centric Business Strategy Checklist: The Way To Energize A Digital Enterprise To Be More Agile, Bold, And Simplified
• Data-Centric Business Tech: New And Better Ways To Shatter Our Obsession With Software And Automation

For more on what it means to be data-centric, see my article, A Data Centric Business: The Best Way To Agility, One Truth, Simplicity, Technology Innovation.

You Need To Think Data Centric To Be A Successful Business: Stop Being Data Driven, Application Centric

References.

For more references on enterprise data management used in this article, see below

• Analytics8’ 7 Elements of a Data Strategy. This article includes a data maturity model that can help organization assess where they are in regard to data management.
• Stitch’s Data Strategy Explained – Framework, Examples, and Tools offers more ideas on developing a data strategy for your business.
• Secoda’s How do data governance and data strategy differ in a business context?
• Atlan’s What is Data Governance? provides some great tips on modernizing data governance policies.
• Fluree’s Introduction to Data-Centricity provides an introduction to data Centricity from a technical architecture perspective. Also, it details the questions an organization should ask when re-shaping their data strategy to a data-centric strategy.

A Data Centric Business: The Best Way To Agility, One Truth, Simplicity, Technology Innovation

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