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Modern businesses are forced to process terabytes of data. Information comes from various systems — websites, CRM, ERP, marketing platforms, or even production sensors. To be able to store and use data, companies either build their own data lakes or connect to ready-made cloud solutions. Such storage allows data to be stored in its original form ‘as is’ so that important details are not lost for further analysis.
Despite its potential, Data Lake often fails to live up to expectations. Without well-thought-out processes for collection, structuring, and quality control, it quickly becomes unmanageable. As a result, companies accumulate data but do not benefit from it. Sometimes they are even unable to find the necessary and essential information.
Only experienced specialists can solve systemic problems. Today, our guests from Cobit Solutions will talk about the most common challenges.
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The most common reason why Data Lake loses its value is the lack of a well-thought-out architecture. As a result, over time, the structure of this repository collapses, and instead of an orderly system, chaos ensues. This is what experts call a ‘data swamp.’
Data Lake architecture is a model that defines:
Without these rules, any storage quickly loses its logic. And the lack of consistency between formats means that analytical models begin to produce conflicting results.
At first, Data Lake works as intended — clearly, structurally, predictably. After all, the company creates a repository for a specific task. For example, to collect marketing data, generate reports, or prepare a database for modelling. But gradually, the needs grow.
Data from accounting systems, CRM, internal portals, and integrations with marketing platforms is added to the repository. Tables for calculations, reports, and temporary experiments appear. Teams copy files for their own analytics, change structures to suit their needs, and leave intermediate versions in working directories.
What initially looked like a logical system begins to lose its shape. The line between ‘raw’ and processed data becomes blurred, and confidence in which sets can be used in analytics disappears. With each new update, the risk increases — of changing or deleting something important, breaking links, or getting conflicting results in reports.
Signs of system degradation accumulate gradually. If the storage has already lost transparency, this will manifest itself in daily processes.
If you recognize at least two of these symptoms, your data lake has lost its function as an analytical platform and has turned into a file storage facility. In such a situation, it is advisable to order Data Lake consulting. This allows you to assess the actual state of the storage, identify sources of errors, and determine whether it is worth restoring the current architecture or whether it is more effective to build a new one.
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How is it that the system functions, people work with it, but no one actually manages what happens in it? There are several reasons, and they are all organizational, not technical.
Can technical specialists really help with organizational issues? Yes, if they understand that data management is not just about technology, but also about the structure of interaction between people, processes, and systems. An external review is needed when data begins to contradict reports and indicators between departments do not match.
Consultants approach the problem from a different perspective:
After consulting, the company usually gains a new way of thinking about working with information flows. There is an understanding that data is a shared asset that has owners, a life cycle, and measurable value. And it is from this moment that Data Lake begins to function not as a technical project, but as the basis for managed analytics.
Sometimes the repository is formally updated, but does so with errors. Data arrives in the wrong sequence, is partially duplicated, or does not match the update times of the source systems. As a result, indicators that should reflect the same processes differ between reports. The business sees conflicting figures, and analysts draw false conclusions due to the distorted picture.
The problem occurs more often than it seems. The reason lies in the way synchronisation is configured, of which there are many, but each has its advantages and disadvantages. If the update schedules are not coordinated or the conversion is done incorrectly, the Data Lake receives some old and some new records at the same time.
For example, CRM has already recorded the sale, but the financial system has not yet updated the payment status. The same customer may appear twice in the reports. This may seem like a minor technical issue, but it is precisely because of such discrepancies that analytics begins to ‘lie.’
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Specialists begin with an inventory to determine what sources are available, what types of data they generate (registers, transactions, reference books), and how often such changes occur. This allows them to immediately weed out inappropriate synchronization methods.
Then they proceed step by step:
When experts are at work, you forget about ‘black magic’ and incidents like, ‘I pressed something, and everything disappeared.’ Data Lake begins to work in sync with real business, rather than ‘on its own schedule.’
Even if the storage is built correctly and the sources are synchronized, the most insidious factor remains: data quality. It is this factor that determines whether analytics can be trusted at all.
It may seem like a minor inaccuracy — an error in the date, a duplicate customer, or a missing field — but on the scale of a data lake, it turns into an avalanche. ‘Dirty’ data ends up in BI reports, predictive models, and demand or risk assessment systems. And everything built on such foundations begins to give false results. And the company receives beautifully visualized misinformation.
The other side of the issue is analytics performance. As the repository grows, the number of queries and data volumes increase exponentially. If the table structure, caching, or monitoring are not well-thought-out, even a simple query can take 10 minutes to execute. This reduces the efficiency of analytics teams and forces them to look for shortcuts — intuitive ones.
Experts approach the problem systematically:
In many companies, Data Lakes appear as technological projects. The team sets up storage, connects sources, automates uploads — but no one clearly articulates what business difficulty this is supposed to solve. As a result, the system works, costs increase, and the benefits are almost invisible.
Without a specific goal, a data lake becomes an unlimited file repository: information accumulates, but no solutions are found. Managers see no effect, analysts do not understand which indicators are a priority, and technical teams simply maintain the process out of inertia.
External experts help restore the connection between data and business value. They formulate what exactly the company should get from the data lake — faster report generation, cost reduction, the ability to forecast demand, warehouse optimization, or new revenue models. After that, they determine which data is really needed and which only creates noise and overhead.
A separate task is to measure the effectiveness of the initiative. Experts establish clear metrics: report update time, accuracy of indicators, savings, speed of decision-making, frequency of analytics use.
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When a data lake has clear business objectives and transparent success metrics, it ceases to be a costly technical project. It becomes what it should be — a tool that gives the company a competitive advantage. Therefore, in situations where complexity grows faster than benefits, it is worth turning to experts. They will help bring the data lake back into the realm of real business value.
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