Why Full Stack Data Science?
(Note: While I am still the President and CEO of AvalonBridge and remain committed to helping companies grow through strategic consulting and targeted investing, in March of 2016 I decided to go in-house with ATA and take over as CEO to help pursue and shape opportunities in the filed of data science for reasons, at least in part, that may become evident in this blog post.)
Some would tell you that data science and full stack developer talent are the two toughest types of people to find right now (and I would agree). That is a big part of the reason why my company, ATA, is in this space. The demand is clear, the need for support is great, and we have something significant to bring to market with our Full Stack Data Science offering. That is why we are looking to "create our own" and why we are always recruiting (even when we're not hiring and you're not looking for a job). We aim for right fit (culture, attitude, competency) and timing (for you and us). To accomplish this, we should be willing to invest in people and why you and I should always be talking.
(Although, right now, I am hiring a Full Stack Developer to support the development and deployment of a DevOps capability -- email me... firstname.lastname@example.org)
What is drastically needed is the integration of data science disciplines ("defragging" data science, so to speak). We need to bring an end to the disorganized, fragmented and sub-optimized approach to data science we see broadly employed today, often weighting activity towards analytics and visualization technologies leaving vital elements of the stack untouched or receiving slight treatment (and then we wonder why data analytics is falling short of expectations).
This is what Full Stack Data Science does!
To bring Full Stack Data Science to market, it demands a stellar team of data scientists, software engineers, cloud engineers, subject matter experts, and analysts. We find that great talent in these areas like working with people like themselves; each bringing a highly valued knowledge and experience base with them while also seeking to learn from their peers. There should be no "mushrooms" relegated to a dark corner so a firm can claim to be "doing data science" (what a waste of talent).
At ATA, we are focused in this area and our core team works collaboratively to address some tough (and surprisingly common) challenges around data management; collection and ingestion; data architecture; exploratory, advanced, and secondary analytics; knowledge presentation and visualization; and access and security. This team also engages to help shape an organization's data science strategy, understand capability gaps, design and build a "right fit" data science capability, leverage DevOps to accomplish continuous delivery, and determine the appropriate measurement and validation techniques to ensure outcome meets intent (otherwise, what's the point).
Today, we are all competing, regardless of whether or not you are in the public or private sector, and we find that what is often needed the most is a partner behind the scenes providing the edge needed to win at the never ending game of needing to do more and more with less and less. We're all experiencing the often mentioned data explosion and hungry to identify new relationships and opportunities in our data. We also know the ability to derive new business understanding demands new thinking, infrastructure "glue-code", and gap analytics often not found in-house (even at some of the most data-driven organizations we could think of).
In the end, the goal of Full Stack Data Science is help organizations build a sustainable capability to improve performance, optimize investments, and gain competitive advantage.
Full Stack Data Science is the capability for the data-driven future we all know is coming. The world needs more data scientists and full stack developers. In the meantime, while you look to build your own, call ATA to leverage our team to help you get it done today.
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