Data science is a disciplinary blend of data inference, algorithms development, and technology in order to solve relative complex problems.
At the core is data. Stacks of raw information, streaming in and stored in enterprise data warehouses. Much to learn by mining it. Advanced capabilities we can build with it. Data science is ultimately about using this data in creative ways to generate business value and eventually make something out of it.
Data science and discovery of data insight
This aspect of data science is all about uncovering findings from data. Diving in at a raw level to mine and understand complex behaviours, trends, and inferences. It’s about surfacing hidden insight that can help enable companies to make smarter business decisions to increase their profit. For example:
Netflix data mines movie viewing patterns to understand what drives user interest, and uses that to make decisions on which Netflix original series to produce and make sequel to.
Target identifies what are major customer segments within it’s base and the unique shopping behaviours within those segments, which helps to guide messaging to different market audiences.
Proctor & Gamble utilises time series models to more clearly understand future demand, which help plan for production levels more optimally.
How do data scientists mine out insights? It starts with data exploration. When given a challenging question, data scientists become detectives. They investigate leads and try to understand pattern or characteristics within the data. This requires a big dose of analytical creativity.
Then as needed, data scientists may apply quantitative technique in order to get a level deeper – e.g. inferential models, segmentation analysis, time series forecasting, synthetic control experiments, etc. The intent is to scientifically piece together a forensic view of what the data is really saying.
This data-driven insight is central to providing strategic guidance. In this sense, data scientists act as consultants, guiding business stakeholders on how to act on findings.
How data mining and sorting algorithms finds and engineer your decisions
Amazon’s recommendation engines suggest items for you to buy, determined by their complex algorithms. Netflix recommends movies to you. Spotify recommends music to you and so on.
Gmail’s spam filter is data product – an algorithm behind the scenes processes incoming mail and whether decides if a message is junk or not.
Computer vision used for self-driving cars is also data product – machine learning algorithms are able to recognise traffic lights, other cars on the road, pedestrians, or any obstacle etc.
Data scientists play a central role in developing data product. This involves building out algorithms, as well as testing, refinement, and technical deployment into production systems. In this sense, data scientists serve as technical developers, building assets that can be leveraged at wide scale.