Data Chaos in Business – 10 Warning Signs

Where Does Data Chaos Come From?

Every commercially operating organization strives for continuous growth, expansion into new markets, the development of new business lines, and the acquisition of competitors. These efforts typically require expanding existing IT infrastructure, implementing dedicated systems to support business operations, and integrating systems acquired through mergers or acquisitions. The result is a rapid increase in the number of processes, systems, databases, and customer information sets.

These data are stored across various locations – distributed databases, reports, and, quite often, still widely used Excel spreadsheets – leading to a range of operational and analytical challenges.

Lack of Consistent Data Standards

One of the main challenges is data inconsistency. In practice, this means that the same customer may appear in different databases with varying data, such as different residential address, email, phone number, or business classification. 

For example, the marketing department might have a different contact address for a customer than the sales team, making personalized communication difficult and leading to misunderstandings. Similarly, a customer may be categorized according to an internal classification system in one database, while in a system acquired through a merger, no such classification exists, or it is based on entirely different criteria. 

As a result, customer data might not be usable for business purposes— for instance such as in a targeted marketing campaign. So how do you determine which data are valid and should be used? 

Duplicates and Lack of Unique Identifiers

Another significant issue is data duplication. Without a central customer identification system, the same person or company may be entered multiple times. Beyond common typographical errors in company names or mistakes in customer-provided data, a frequent cause of duplication is the differing data requirements across various departments or systems. 

This leads to reporting errors and so-called “ghost record” in corporate database, resulting in inefficient marketing and sales activities. Resources are wasted on reanalysing customers who have already been verified, just under a slightly different name. Can we really afford a situation where the same customer appears under several different records?

Difficulties with Data Integration and Migration

Further challenges include the lack of unique customer identifiers and difficulties in integrating data from various sources, often based on different structures and standards. The greater the number of independent systems – databases, CRM, ERP, or e-commerce platforms – the more effort is required for time-consuming processes of data transformation, cleaning, and merging. 

This approach not only generates additional costs but also increases the risk of errors. Do we really want employees manually searching for differences and merging data from various sources in Excel? Or would it be wiser to automate this process using dedicated tools, possibly powered by artificial intelligence?

GDPR and Security Issues

Lastly, scattered data make it difficult to meet compliance and security requirements, including regulations such as GDPR, which mandate easy access to complete and accurate customer data upon request. 

But how can this be guaranteed when data is stored across five to ten systems, with no unified method of customer identification?

How can you ensure that employees only access specific, authorized information when each system has a different permission model, its own role structure, and, in extreme cases, offers no access control over data from particular business units or geographical regions?

MDM, Data Catalogues, and Central Repositories – What Should You Choose?

To address outlined issues, companies increasingly adopt solutions like central data repositories, Master Data Management (MDM) systems, and tools for automatic duplicate detection and merging.

The key to success lies in a conscious approach to data management and close collaboration among departments that utilize the data. This point is emphasized in Gartner’s report, Data Quality: Best Practices for Accurate Insights, which highlights the importance of data quality, consistency, accuracy, timeliness, and validity.

Ready-Made or Custom-Built Solutions?

Tools supporting centralised data systems are available as ready-made products that can be adapted to organizational needs. However, be aware of the risks: data from each additional system may not integrate easily, and flexibility might be limited due to dependency on a specific vendor.

When Is It Worth Building a Custom Tool?

An alternative is to develop a dedicated system from scratch, tailored to your company’s specific needs and processes. Although this requires more effort in the analysis and design phases, it gives you greater control over the system architecture, easier future integrations, and boosts user trust by involving them in development. 

Owning the code also gives you freedom to develop the system further – on your terms, with any technology partner. You can benefit from the experience of Altkom Software, which has been building tailored software for years and has extensive expertise in integrating various systems.

Data Governance – Order in Data Starts with Principles

Another way to manage increasing data volume – often implemented alongside the deployment of a central repository – is to introduce a Data Governance policy. This includes defining data ownership, setting standards and processes, and assigning roles and responsibilities within the organization. 

Many organizations are already familiar with ISO certification, which requires structuring processes and activities into a logical, coherent system. The same applies to Data Governance – set of rules and best practices that help manage the chaos resulting from rapid business growth and data fragmentation. 

How to Avoid Mistakes and Speed Up Implementation?

While ISO standards can often be implemented internally, certification usually requires collaboration with an external accredited body. Similarly, while you can implement Data Governance on your own, working with an experienced partner can significantly accelerate the process, reduce the burden on your team, and help avoid internal habits and ineffective practices that may hinder effective data organization.

What Risks Does Ignoring Data Chaos Pose to Your Organization?

When companies are asked, “Are you considering implementing Data Governance or data cataloguing tools?” the answer is often: “We don’t see the need.” 

But is that really true? Or would a more honest answer be: 
“So far, we’ve managed – and prefer not to acknowledge that the need already exists.” 

Managing Data Quality Without Proper Tools

Realizing the need for regulation too late can lead to a long and costly implementation and adaptation process . The more systems in your organization, the harder they are to manage.

Are tools like Word, Excel, or Confluence really sufficient to document names, terms, standards, and processes effectively? Can a little IT maintenance staff really “watch over” the databases – without tools that show where data is stored, where it originates from, how it flows between systems, where and how it is transformed, how it reaches reports, and who should – or should not – have access to it?

Risk Grows Alongside Company Growth

As an organization grows and the volume of data increases, the absence of Data Governance practices begins to impact operational efficiency

Long-term neglect of the need to organize data can directly threaten your business through poor decisions, financial losses, and legal violations.

Consequences include increasing data chaos, loss of trust in your own information resources, rising maintenance costs, and declining accountability for data quality and its sources. The more fragmented your data architecture – across multiple databases and inconsistent CRM, ERP, or e-commerce systems – the greater the risks and complexity of any future remediation efforts. A strategic approach to data management is crucial.

10 Signs of Data Problems You Can Identify Yourself

So what signs should make you rethink your approach and convince your organization to take the first step toward Data Governance, a central data repository, or a Data Catalog? 

Here’s a list of symptoms that may indicate growing data management issues. If your company experiences even three of them, it’s time to seriously consider the next steps – even if this topic hasn’t been a priority so far. 

10 Warning Signs That Your Data Is in Trouble

1. Inconsistent reports from different systems

The same metric (e.g. customer count, monthly revenue) varies depending on the data source. Different departments – like sales and marketing – report different figures for the same result or process.

2. Duplicate customer or product data

Lack of unique identifiers prevents effective data linking across systems. Customers appear multiple times – often with slight differences like “John Smith” vs “Smith John”.

3. Lack of trust in data

Instead of decisions, you hear: “Let’s double-check the numbers.” Managers hesitate to make business decisions based on reports they don’t trust.

4. Conflicts between departments

Teams use different terms for the same events and disagree on definitions. The common question is: “Where did you get that data?” instead of “What does it mean?”

5. Integration and migration issues

Data from different systems don’t “fit” together, they are requiring manual adjustment. Migrations to new systems (CRM, ERP, cloud) result in errors or significant delays.

6. Trouble complying with GDPR and other regulations

The company struggles to fulfil customer requests for data access, correction, or deletion because it’s unclear where personal data are stored.

7. No accountability for data quality

When errors arise, no one takes responsibility. You hear: “That’s not our database,” or “We just pull the data,” and the issue remains unresolved.

8. Manual data processing

Excel is the main “data integrator” serving as a tool for everything. Automation is lacking, and analysis is difficult due to poor and inconsistent data quality.

9. Problems with personalization and segmentation

You can’t properly segment customers or run personalized campaigns. There’s no complete customer view or ability to tailor offerings.

10. Recurring data incidents

Data leaks, mailing errors, and incorrect invoice labels occur. There are no root-cause analysis or preventive mechanisms in place.

What Can You Do When You Notice Data Issues?

If even three of these problems sound familiar, it’s a good time to take action.

Together, we can find solutions that are optimal in terms of both cost and functionality. We can act as an advisory or implementation partner from building a custom system to deploying and integrating a ready-made tool tailored to your business needs.

_{Source: Gartner, Data Quality: Best Practices for Accurate Insights, 2023}