In the world of enterprise data management, two paths dominate: one that tightly controls data, stifling flexibility and access, and another that lacks a strategy, leading to fragmented, duplicated, and ambiguous data. Moreover, despite recognizing the value of data, businesses often spend more time collecting it than making it understandable and accessible. To me its clear: to stay competitive and thrive, companies need a new approach to rapidly gain valuable insights from their data.
In this article, I’ll look at why current data management often fails, highlighting the need for change. Indeed, companies are now operating in highly competitive digital environments where past business practices fall short. This is why businesses need more than just data management and governance. What they need is a new approach, a data-centric strategy, to make their data more insightful, accessible, and secure.
1. Enterprise Data Management Today Focuses More on Controlling Data, Not Making it More Valuable.
For decades, businesses have established data management policies that focused on controlling data access to ensure compliance and prevent breaches. This has resulted in a too narrow focus on protecting personal privacy and securing sensitive corporate information. As a result, this approach, despite its merits, has also locked data away in secure silos, hindering collaboration and preventing organizations from gaining valuable insights.. At the same time, more innovative organizations are starting to recognize that data is a strategic asset that should be leveraged for competitive advantage, rather than just controlled for security.
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.”
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, then their data is just a costly liability instead of a strategic asset.
“… if organizations fail to extract valuable insights, then their data is just a costly liability instead of a strategic asset.”
2. Rethinking Data Strategies: Beyond Governance and Collection.
Also 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. So, let’s look more closely at what a data strategy is. One that goes beyond mere data collection and governance, focusing on making data a strategic asset that drives business value.
a. Data Strategy Versus Data Governance.
As mentioned above, prudent organizations will prioritize Data Governance, but focus almost solely on policies that ensure compliance with both internal policies and external regulations. What most organizations forget is another crucial component of Data Management. Namely, a Data Strategy. To gain a better understanding of what a Data Strategy, let’s look at the difference between a Data Strategy and Data Governance. First, below 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.”
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.
b. From IT Amassing Data to Business Led Strategy to Turn Data Into Insights.
Also, a well-crafted data strategy is essential for guiding how data is collected and used. However, leadership teams often hand off this responsibility to the IT department or data teams without providing clear guidance. As a result, these teams collect vast amounts of data across various business areas, but the data is often poorly defined and not structured to support decision-making. Consequently, this lack of clarity leads to businesses being overwhelmed with data but gaining few valuable insights. To improve data strategy, businesses should prioritize clear guidance and involvement from leadership to ensure that data is collected and used effectively.
For specific examples on bad data as a result of a sub par data strategy, see my article, The Data Interoperability Challenge For Supply Chains: 12 Reasons For It And Why Tech Will Never Overcome It Alone.
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, but many organizations struggle due to poor data management practices. Despite having formal data governance policies, most organizations have their data scattered and duplicated across different software applications and systems. As a result, this creates more vulnerabilities and attack surfaces for hackers. Consequently, this makes it harder to apply data access controls, detect anomalies, and prevent breaches. To detail, I’ll share with you four key data security challenges below.
a. The Problem with Assigning Data Sensitivity Levels to Ill-Defined 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. More Difficult to Protect 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. Security Issues Created with 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. Following traditional data integration practices, businesses end up with many custom and proprietary data interfaces. Moreover, most of these custom data integrations are not well documented and unsecured. Additionally, these custom-built interfaces transfer duplicate, fragmented data between countless systems across the enterprise. Thus, these custom integrations create a multitude of data security challenges, making the organization very vulnerable to hackers. For example of the dangers of custom-built data interfaces, see my article, Custom-Built Shipment Statuses: Digital Supply Chains Can Do Better And Need A Reckoning To Eliminate This Insidious Habit.
d. The Increasing Complexity 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.
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. A primary reason for this is that most organizations do not have a Data Strategy to make their data useful. At best they just have a Data Governance strategy to control and restrict their data. As a result, many businesses are drowning in data with few Insights. Moreover, many can’t access the data they need, while overwhelmed stamping out data security vulnerabilities. Indeed, business leaders need to rethink how they manage.
More 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 Strategy Checklist: The Way To Energize A Digital Enterprise To Be More Agile, Bold, And Simplified
- Also, 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.
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 Data, Interoperability, and Information Technology.
Greetings! As a supply chain tech advisor with 30+ years of hands-on experience, I take great pleasure in providing actionable insights and solutions to industry leaders. My focus is on supply chains leveraging emerging LogTech. I zero in on tech opportunities and those critical issues that are solvable, but not well addressed, offering industry executives clear paths to resolution. I have a wide range of experience to include successfully leading the development of 100s of innovative software solutions across supply chains and delivering business intelligence (BI) solutions to 1,000s of shippers. Click here for more info.