Data Challenges

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Data architecture

The volume and velocity of data that is being created is evolving at enormous speed. At the same time, technology and architectural patterns and insights are evolving all the time. Do you consider it a challenge to define the right, future proof data architecture, fit for the evolving needs of your organization?

Situation: Continuous cycle of novel insights

The world around us is changing so rapidly, so is business. Novel insights are continuously needed at a fast pace to keep up with competition and the changes around us.

Staying competitive means understanding your business and the needs of customers in perhaps near real-time. Your business managers are asking for faster data insights to make the best decisions.

Challenge: Define the right future proof architecture

In order to deliver the business insights managers need, a solid data architecture needs to be put in place.

On top of that it is an organizational struggle to keep up with the high pace of novel technologies in data. In the last decade, the amount of new architecture terminology seems to pop up at high speed. The term Data Warehouse got outdated and is replaced by data platform or data hub. New concepts seem to rise every year. New technology often leads to new concepts. Big data technology has led to the rise of the Data Lake. Nowadays, terms like Data Mesh, Data Fabric and Data Lakehouse are the new kids on the block.

In terms of data modelling, it is also crucial to choose the right methodology. In line with your current and expected future needs. And in line with the technological choices that were made. When is it for example a good idea to use Data Vault? And when should you avoid it? When is it better to go straight to Kimball?

And the number of technology and architectural choices to make are ample.

The impact of these choices can be significant. As there is no single source of truth, multiple perspectives are to be considered, and multiple experts are typically involved. And budgetary constraints are also put into the equation.

Solution: Fasten your seatbelts with a modern data platform, starting from a proven reference architecture

A modern data platform with a layered architecture is much more performant and can fulfill the current and future (+/- 5 years) data needs. It is often cloud native, integrates data from multiple systems, and is cost efficient to setup and maintain. The ability from a modern data platform to support more advanced analytics is equally as attractive as the speed and efficiency gained.

In terms of approach, Sparkle typically starts with a solution assessment that defines a target architecture in line with your business needs and expected evolutions. On top, we define a roadmap on how to gradually evolve to this target architecture. We are no fans of a big bang approach, and rather start small (but think big). This allows to learn and improve.

Once the technical architecture is defined, a POC is often still preferred to test the combination of technologies (and infrastructure) is living up to the expectations.

The implementation of the chosen architecture is usually done incrementally. We start by ingesting data source(s) needed for a first set of reports. Learning from that exercise will allow more efficient setup of the platform, source by source, layer by layer. What we typically see is that business needs are changing rapidly. This agile approach will also allow us to incorporate those changes. 

Please contact our experts to discuss and solve your data challenges. 

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