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Data & Intelligence

Data Architecture and Design Thinking

Simplicity is the ultimate sophistication. – Leonardo da Vinci

Simplicity is a very important strategy as people are thinking of designing their modern data platforms. Design is often a complex task, so I recommend applying strong design thinking to simple goals as you lead your data architecture teams. I have stood before many architecture review boards (ARB) for organizations and found that many teams over complicate things. Here are three common issues that ARBs run into and how to simplify data platform design:

ONE: “Let us release the well versed, fully thought through, over-complicated version of this architecture in 3 months to support our use case.” How to improve?

  • Break down this architecture into smaller components and release frequently
  • Prioritize the design and release appropriately
  • Know the goal of this design and what business purpose it serves
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TWO: “I have my AI component which is trained and set for business requirements. Let me release into production.” How to improve? 

  • Have you thought through the operations components such as meeting SLA, cost of support, cost of hardware and software?
  • Have you thought about the data that is required for this AI component and the data model that will support it?
  • What are the phases of improvement for this AI?

THREE: “I have a meeting with my business counterparts to discuss these new cool tools that my team has implemented.” How to improve? 

  • Business teams have many things to worry about and technology is not one of them. So stop harassing them with new technologies to prove a point.
  • Business teams do care about their data and how it’s served to them to solve their problem. So think data governance when you’re presenting a solution to them.
  • Start pulling the business team into the solution when you start the design and build phase.

Final Recommendations

My recommendations on a simplistic data architecture focused on design thinking with best practices are as follows:

  • Consistently think synergy between data architecture and business outcome
  • Create an architecture review board (ARB) that will align capability models to business strategy
  • Consider utilizing people who know and listen to what they can offer before you can be the hero of your organization (This is a very common problem I see in the industry which needs to be managed using organizational change management)
  • Focus on simple, purposeful, lightweight architecture while leveraging stable systems of record for baseline data
  • Empower citizen developers within your team to build more and agile

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Arvind Murali, Chief Data Strategist

Arvind Murali is the Chief Data Strategist for Data Governance with Perficient. His role includes defining data strategy and governance to deliver transformative data platforms. Arvind has served as an executive advisor for data strategy and governance to organizations across several industries. Arvind’s dedication to solving challenges and identifying new opportunities has provided valuable business-focused results for clients, such as providing self-service access to data for global sales teams; helping physicians create informed wellness plans; and delivering insights about current supply chain inventories. He is a passionate Vlogger on YouTube and discusses real-world insights, data platform trends, and the importance of governance as big data continues its exponential growth.

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