Data is one of the fundamental pillars of decision-making, operations and competitiveness. At the same time, managing data has become increasingly complex. Forecasting, operational optimisation, and a growing range of automation and AI solutions are all built around it.
Many organisations run into the same core questions: how much of the underlying infrastructure should be built in-house, and when does it make more sense to rely on off-the-shelf solutions? What kind of architecture best supports growth and changing needs well into the future?
In this article, we outline the key considerations for decision-making and present our Lakehouse implementation model as one viable option.
Questions around data architecture are both practical and strategic. Data pipelines, storage models and processing solutions have a direct impact on critical areas such as operational efficiency and security, the ability to respond to change, and the reliability of decision-making.
In many cases, data infrastructure grows incrementally: one system is built for a specific need, another for a different one, and a third to patch the shortcomings of the previous solutions. The result is often a difficult-to-manage tangle that becomes harder to develop year after year.
At the same time, expectations for data utilisation continue to rise. Solutions are expected to be efficient, real-time, and ready to support advanced analytics and AI. This often conflicts with the reality that data processing has not been designed from the outset with such a broad perspective in mind. Data frequently flows through multiple pipelines, is transformed along the way, and ends up in different views based on differing logic.
Broadly speaking, there are three approaches to organising and using data: the traditional data warehouse, the data lake, and the data lakehouse, which combines elements of both.
Simply put:
A data warehouse is, as the name suggests, a carefully organised store. Only fully processed data in a consistent format is loaded into it. It works best in use cases where a “one-stop shop” solution from a major provider (such as Amazon Redshift, Google BigQuery or Snowflake) fits the need. Warehouses typically excel when data is used mainly for reporting and standardised analyses. Their strengths are reliability and a clear structure; their drawbacks are that changes can be slow and expensive to implement.
A data lake, by contrast, is more like a deep pool into which data can be poured in its raw form, without prior transformation. This makes it flexible, particularly when data is to be used in many different ways, such as for exploratory or research purposes. The challenge is that without a clear structural model, finding the right information can be difficult, and the whole can quickly become disorganised.
A data lakehouse aims to combine the best of both approaches. Data can be stored flexibly in varying formats, as in a data lake, while also benefiting from the strengths of a warehouse: structure, governance and ease of processing. In practice, this makes it possible to use the same, consistent data source for reporting, analytics and AI solutions, without unnecessary duplication or data movement. For this reason, we use a lakehouse approach in our own projects when it makes sense to build the infrastructure in-house.
When building data infrastructure solutions, we always start from real business challenges – how well the solution supports everyday decision-making, analytics and forecasting. The data foundation must be clear, manageable and reliable, while remaining flexible enough to accommodate changing needs and evolving technology.
Freedom of choice is also a key reason why we have opted for the lakehouse model in our own solutions. It does not lock the architecture into a single mould or operating model, but instead allows, for example, new AI applications to be built on top of it in a straightforward way. This aligns with the realities of modern business, where organisations need to respond to new opportunities as quickly and flexibly as possible.
For many companies, data architecture is no longer just a technical support structure but rather a core part of business strategy. That is why it is worth implementing it together with a technology partner who understands how the solution fits into the wider digital ecosystem – now and in the future.
Are questions around data architecture currently relevant in your organisation? Get in touch – our experts will help you identify the best next steps.
