Data architecture & engineering

When it comes to data architecture and engineering, the challenges organizations face are extremely diverse. These include making the correct choice in technology initially, handling the skills gap for the chosen technology, managing the coherency and transition of the legacy analytics architecture, addressing the security challenges of the data flows, and disentangling any and all concerns around data governance. The list goes on.

So having a robust, scalable and progressive data platform is key, in order to provide you with integration, management, transformation, processing, exploration and visualization capabilities.

And of course, to support the business cases efficiently.

Data platforms are distinguished from BI platforms through their closer proximity to the information system’s core. Not only are they a satellite aggregating information for decision making, but they’re more fundamental than that, automating decisions and integrating operational production systems. They exist as a critical part of the chain.

Data platforms need to manage both historical & real-time data, and they must be able to support the complete development lifecycle, from exploration through to production. They must be receptive to using partners in certain use cases, while adhering to regulatory requirements at the same time (in particular when it comes to personal data or security of certain information).

Even with all this achieved, they must continue to evolve, accounting for the continuous innovation streams while enabling access to the user throughout.

Many enterprises have failed their “data lake” initiatives, ending up with complex, costly and rigid platforms that end up unused and unusable. They fail to answer the operational needs of the data and business teams, and often only deliver on a small subset of the demands.

We believe that building, maintaining and leveraging the right data platform should be done through the following principles:

• Build the architecture, a key pillar; this means using the right technology, but also ensuring the right rules and patterns exist throughout, and making certain the data is correctly structured for the processing pipelines.
• Start with the context, build a personalized route, and provide tailored technology recommendations (vendor-agnostic).
• Be driven by business cases; deploy these alongside tangible use cases, and do so to avoid the pitfalls of a technology-guided approach.
• Combine this all with a strong data governance program (see “Organization & Governance” for reference).

Because each organization’s environment is unique, we always start by assessing your existing landscape, current initiatives and the organizations’ that surround you. We would always recommend performing a maturity assessment initially, to enable a cleaner strategy from the start.

With those elements at hand we will then work to define the correct IT and data architecture, potential impacts on the organization, and the roadmap required to deploy and implement your new framework on an iterative basis. We would guide your team in construction of this architecture, train all employees in the new technologies and principles, and with that achieved we would initiate the ultimate handover and full integration to within your organization.