Three managers—from marketing, sales, and logistics—enter the COO’s office. Each brings a report on the number of customers for the quarter. It turns out that each manager is working with their own data: the figures don’t match, and there’s no explanation for why they aren’t synchronized. In fact, according to Gartner, up to 80% of initiatives implemented without sufficiently professional enterprise data lake consulting services for organizations with complex multi source data do not deliver the expected business results. Let’s explore why this happens.
Data Lake: What Are the Benefits of This Approach?
A traditional repository was more like a library: before placing a new book on the shelf, it had to be cataloged according to a specific system, and there was no room for deviation from the rules. The process was reliable, but expensive and very slow.
Data lakes, on the other hand, follow a different philosophy. Data can be stored in its raw form and structured only when needed for a specific query (schema-on-read). This creates a foundation for scalability and flexibility, and also allows you to work with data in any format.
But there’s a catch: when it comes to implementation, everything has to be done perfectly. Otherwise, the project is headed for disaster. This perfectly illustrates why enterprise data lake projects fail when handled by generalist consulting firms. That’s why specialized consulting is so important right from the concept stage.
Waterfall and Agile: Why Aren't Standard Frameworks Always Effective?
Unlike consulting firms that specialize in enterprise data lake governance and architecture at scale, many generalist agencies use one of two traditional methodological approaches to implement a corporate data lake.
The waterfall approach involves a linear sequence: gather all requirements, create a design, implement, and test. In theory, everything is well-organized, but this is more of a pipe dream when it comes to enterprise-level storage. There isn’t a single organization in the world capable of predicting its needs several years in advance.
The Agile approach seems like the better alternative: it is flexible and delivers greater speed. However, when it comes to “pure” Agile, it also poses risks in the context of a data platform.
This refers to decentralized teams working in sprints. Sooner or later, they begin to work with data in a way that circumvents global governance standards:
- An active marketing customer is someone who has visited the website within the last 30 days.
- For the finance department, an active customer is someone who has made a payment within the last month.
This is why different figures are cited at board meetings. And this is what erodes trust in the platform. What’s the point if the same business metrics vary so drastically?
But there is a solution, and it is a specialized approach to data architecture. It must combine governance and flexibility, federated responsibility, and uniform standards.
What Architecture Works: From Data Lake to Data Lakehouse
Modern specialized teams no longer use the ETL (Extract, Transform, Load) paradigm; instead, they use ELT (Extract, Load, Transform). The difference is clear: data can be loaded into a data warehouse in its raw form, and it is transformed within the cloud environment in response to a specific query. As a result, the original context is preserved, and different teams can interpret the same dataset in their own way.
However, it is even more important to transition to a Data Lakehouse architecture. This is a hybrid model that combines the flexibility and low cost of data lake storage with the reliability, ACID transactions, and performance of a traditional data warehouse.
Result: A data scientist trains an ML model using the same data that a business analyst uses to build real-time dashboards. This ensures a single source of truth within a single repository.
Could the data be corrupted or delayed?
Every day, the branch sends its report in a standard format. But suddenly, today’s report is different: the price column contains text instead of numbers, and there’s a new field that nobody warned us about.
To avoid such situations, modern data centers have implemented automated checks for every batch of incoming data. If something does not match the expected format, that record is placed in "quarantine" for further verification, and the processes remain unaffected.
There is another issue: data arriving late. For example, a sensor at a warehouse located in another country transmits readings a day after they were recorded. There is a solution for this as well: recording two timestamps for each entry—the actual event and the time the information was received by the system.
Learning to view data as a product
So, it is possible to set up the technical architecture. However, that is only half the battle. The other half involves a cultural shift within the organization regarding data. We need to learn how to approach data the right way.
In the traditional approach, data processing is viewed as a reactive service: a business unit submits a ticket requesting a dataset, engineers create a pipeline, and the ticket is closed. However, this results in hundreds of redundant pipelines, most of which are no longer maintained.
In a specialized approach, the Data-as-a-Product (DaaP) paradigm is changing everything. Data is no longer a passive byproduct of business operations, but an active, managed product with its own lifecycle, quality guarantees, and audience.
The role of the Data Product Manager—a specialist who identifies the operational needs of data consumers—is growing in importance. This role also involves designing analytical assets that seamlessly integrate into workflows and iteratively improving the product based on feedback.
The main tool of the DaaP approach is Data Contracts. This is a kind of API agreement between operational system engineers—who are data producers—and analytics teams—who are data consumers. This agreement specifies:
- What schemas and formats are required?
- How often and with what delay should the data be received?
- What standards and business rules must they comply with?
Contracts are built directly into CI/CD pipelines. If the operational system attempts to pass data that violates the contract—for example, a string instead of an integer, or a missing required identifier—the pipeline automatically stops.
Even the best data storage system is useless if an ordinary administrator has to make sense of tables with names like tx_vol_098_usd. It’s like giving someone a city map where the streets are labeled with coordinates instead of names.
That is precisely why we need a semantic layer to act as a bridge between the technical side of the data warehouse and the decision-makers. It translates technical terms into familiar business language: that same column, tx_vol_098_usd, becomes “Annual Recurring Revenue (ARR)”—and everyone understands what it means.
The result is a single version of the truth for everyone:
- The manager is creating a report in Power BI
- The analyst is loading data into the model
- The CEO asks the AI assistant
And all three get the same, verified figures.
How to Avoid a $1 Billion Fine?
While in many business sectors the cost of a mistake is primarily technical debt, in finance, healthcare, and telecommunications it also entails enormous financial risks. A single medical data breach costs an average of $10+ million, and penalties for GDPR violations can reach $1 billion (not to mention the reputational damage).
In most cases, the cause of the error is adding security and regulatory compliance features at the end of the project. The correct approach, championed by enterprise data lake consulting services trusted by regulated industries for complex environments, is to integrate them into the architecture from day one.
If everything is done correctly, the system can automatically track the path of each data field—from its source through all transformations to the final report. Is the regulator asking for proof that the calculation is correct? A complete audit trail is available in seconds.
The system simultaneously takes into account the user’s country, their current project, and the sensitivity level of specific data. A data scientist sees anonymized medical records for training the model, while an HR specialist with the appropriate access permissions sees full personal data. It’s a single architecture, but everyone has access only to what they are authorized to see.
What to do after the system has started
In traditional logic, it seems reasonable that a systems integrator would deploy the infrastructure, set up the initial pipelines, hand over the keys to the in-house IT team, and then exit the project.
However, Data Lake is not a static application. If the internal team lacks in-depth cloud-native expertise, it will be unable to support the rapidly growing ecosystem. Without continuous optimization, queries will slow down, data quality will decline, and cloud computing costs will rise.
That is why specialized consulting firms use the Build-Operate-Transfer (BOT) model to ensure an enterprise data lake consulting engagement that delivers beyond initial implementation and build:
- Build — architectural design, cloud infrastructure deployment, and building pipeline frameworks with built-in governance and CI/CD.
- Operate — A consultant monitors performance, resolves incidents, and supports the client’s analytics and ML teams in generating real value.
- Transfer — the gradual and structured handover of knowledge, documentation, and operational responsibilities to internal teams.
What to Look for in a Consulting Partner
If you are planning to build or modernize a data lake, you need a data lake consulting engagement that includes governance lineage and post delivery support. To achieve this, the experts at Cobit Solutions recommend:
- Choose Lakehouse over legacy approaches. In your architecture, you should combine storage flexibility with the performance and reliability of a data store.
- Avoid ticketing systems. Insist on software contracts that automatically safeguard data quality and a unified semantic layer that ensures metric consistency.
- Right from the start, emphasize the importance of setting up data lineage tracking, flexible access management, and ongoing compliance monitoring. This should under no circumstances be put off until later.
- Insist on clear commitments regarding system availability, data quality, recovery time after incidents, and, most importantly, business value metrics: the speed of insight generation, the platform’s adoption rate, and the volume of data-driven decisions.
Final thought
Building a corporate data lake is a strategic transformation of processes. A poor approach is to treat the process as simply purchasing cloud infrastructure. The right approach is to build a product: one that involves continuous development, quality assurance, and a focus on the end-users. As a result, the company gains a significant competitive advantage and control over its processes.
