Why and when you need a Big Data Architect
Big Data Architects are one of the most demanded professional profiles in the IT sector. Their main goal: turn data into information that facilitates decision-making. However, when facing any project involving big amounts of data, stakeholders will face an overwhelming amount of questions:
- What strategy should we adopt for collecting and storing data?
- what should we do with entries which miss some chunks of data?
- What software technologies should we use to manage and analyze our data? (Hadoop, Elasticsearch, Storm, Spark, pyspark, python, R, Tensorflow….)
- What hardware? Should we run our software on our machines or should we externalize the service? And in the latter case, all of it or just a part?
- How can we extract the most information from our data?
- What algorithms do we need? What features do we need to extract from it? How can we reduce dimensionality?
- What models should we use to make predictions/recommendations?
- And the list goes on and on and on...
It is really overwhelming. You are an expert on your field/department, a really good one, but you have opened pandora’s box. These questions are too technical and have a too deep impact on the project to be taken lightly. In addition, there is no correct answer to them. Answers depend on many things, like your objectives, the data you already have, the system and workflow you already have in place, the money you want to spend…
Big Data holds all answers
You need a big data architect with whom to work and design a strategy and a roadmap so your project has the best outcome possible within budget and deadlines.
The Big Data architect you need, in addition to extensive technical knowledge on data management, programming, modelling, machine learning algorithms etc… has high analytical, organizational, decision making, social and communicative skills and is capable of understanding your business and organisation.
Let us once more highlight the importance of the intrapersonal and communicative skills for him/her to deal with technical staff on one side and non-technical staff like managers, CEOs, business analysts etc on the other side.
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