It’s hard to keep up with the pace of innovation in the world of analytic deployment. Whether you are a statistician, mathematician, data scientist, actuarial, quant, or analytic professional of one sort or another – your star is rising!, Take recent news from Microsoft’s Ignite conference, IBM Watson’s announcement, or research from the Business-Higher Education Forum (BHEF) that by 2021, nearly 70% of all business leaders within the U.S. will prefer candidates who are qualified in advanced data skills. Pick your stat du jour – but it’s clear we are fully transitioned from the era of big data management to the era of big data activation where analytic and predictive models are transforming the business world in every vertical.
Currently there are innumerable data languages that can be used for a wide range of analytic projects, and this amount will surely increase as new languages are being developed.
Before models can be placed into scoring engines and then into production, custom code has to be written for each model. This process is labor-intensive and often error-prone. After the model’s custom code is written, Data Scientists have to transport the model to IT.
We are excited to introduce Rehgan Avon as the newest addition to the Open Data Group (ODG) team. Rehgan comes on board as a Product Manager, with a background in integrated systems engineering and a strong focus on analytical technology. She has easily made the transition from data and systems engineering to product strategy.
Modern businesses leverage analytics to gain insights in a multitude of areas, from evaluating business performance to predicting future behaviors. In many industries, these insights are quantified numerically as “scores,” and the process of applying an analytic model to transform a collection of data into scores is called “scoring.”
Imagine that you created a model that runs without any errors. There were no miscalculations the first time Data Science tested it, IT could easily read and replicate it, and it was deployed within a few hours of being trained and approved. In a perfect world, this process may be a bit more realistic. However, this ease of creating a well-performing model is not always seen.
Software engineers are always looking for new, fast ways to update their models once deployed into production. Whether this involves running a new system, creating a new code, or utilizing a new software, programmers need to find fast and accurate ways to update their models that are already in production in order to maintain efficiency.