How can you design data engineering projects to meet stakeholder needs?

Recently, I led a data migration initiative where we aimed to enhance customer experience by consolidating and optimizing disparate data sources. Conducting stakeholder interviews not only revealed unique pain points but also uncovered new opportunities we hadn't initially considered. This thorough understanding paved the way for a seamless integration, ultimately exceeding our stakeholders' expectations.

The value aspect of this is the most critical. Often times engineers can take on projects that have perceived urgency, but no well thought out path to business value. Going that extra step and really understanding the WHY behind requests for data or analysis will help data teams better communicate the ROI of their efforts. This is something crucial when trying to invest in team growth or modernizing technology.

Data Engineering is a niche for Data Analytics Platform. It has evolved over the years. The requirements coming to DE are so Fluid in nature that keeping track of it through documented user stories with bounded objectives can only help. How can the requirement be aligned with clear objectives? - Only through understanding client needs. We need close connect to real problem to know how to break it in smaller chunks. DE Project is all about understanding details to most granular levels. Intergration, Frequency, Format, Security, Extraction, Governance, Storage etc... To sum it up, Convert Client needs into technical demands!

Data Engineering project can be controlled from spiraling up only if Scope and Objectives are bounded within a reasonable expectations. To add value at the end of each sprint of DE an Architect must visualize broader objective. Moving one piece at a time gives confidence and control on outcome. Restrict cost to reasonable level.

Solution Architect should visualize End to End data flow and put in on paper. Different aspect like Data Integration, Frequency of incoming Data, Format of files or relationships, Security & Classification, Extraction batch or stream, Data Governance & Linage, Storage, Encryption, Performance and Availability are some of the aspects which needs to be covered in Design. Data Engineering is very fluid. Multiple choices are available in tools and platforms, choosing right one for right use case is also key. Fail fast and rise quick to move ahead!

Along with appropriate data architecture, data models, pipelines etc. it is strongly recommended to review from a broader data consumption perspective. These additional perspectives involving subject matter experts with strong domain/ business knowledge would enable a design of a data asset deliverable that is more reusable and has a strategic perspective baked in. The goal of data engineering in a governed ecosystem is to make data as reusable as possible to minimize rework and this is the stage to create a strategic data solution.

It is recommended to see if a User Acceptance test can be done with either the original requester at the minimum or with a slightly broader group of users. This NOT only further secures user buy in but, is another location to check for usability of data assets enabled before deployment.

I agree with the pieces listed here, but ideally much of this documentation is happening as you develop and build a solution. Documentation often becomes a lower priority item after the fact. Additionally, much of the metadata aspects can hopefully be crawled and attributed to data assets automatically, reducing the burden on engineers. The sharing piece mentioned is also often overlooked in my opinion. The ability to communicate to stakeholders effectively can be the difference between “shelfware” solutions and high impact solutions.

The Data Engineering Solution is valuable if its sustainable over the period of Data Lifecycle. Many things will change, Business will evolve and requirements will be dynamic so the solution should be scalable, agile and flexible to accommodate changing business needs. Continuously evaluate the solution for the needs of Client's expectations. Seek feedbacks, hold sessions with end users, request for suggestions to improve. Being aligned with Client will provide positive outcome!