I was recently asked a question so simple in the asking, but a little more complex in the response.
What is a data model?
The reason the answer isn't so simple is because I am usually consulting downstream from the data modeling. Not an enviable position but in most cases, the importance of agreeing on which metrics to include, what data to collect, and a cohesive set of data definitions is overlooked--until it is too late. And then--my phone rings.
A database is only as good as its model. I don't do the actual modeling but I prefer to understand the architecture and limits of the data I will be asked to analyze. Think of interoperability but instead of being external to your organization or data practice--it also describes pulling in data from multiple data environments. The big challenge on my end is locating disparate data and trying to determine if they are indeed measuring the same thing. Most of my familiarity is with top-down models but at this point I would have to admit--I have seen it all.
My colleagues in the data world likely share my frustration regarding the focus on debates between data scientists, statisticians, data analysts, programmers, and any other group they can successfully work into a froth. I gravitate toward data scientist but mainly because I actually tend to spend my day similarly to the HBR article description below...
...working data scientists make their daily bread and butter through data collection and data cleaning; building dashboards and reports; data visualization; statistical inference; communicating results to key stakeholders; and convincing decision makers of their results.--What Data Scientists Really Do, According to 35 Data Scientists--Hugo Bowen-Anderson
The transition and evolution is real. I don't know about you but I am constantly updating skills and modern methods of analysis. How many of us can afford a loyalty to SPSS and SAS if the best solution is R or Python? Because I access a wide variety of data registries I need to know how to work with SAS and SPSS. If a government database allows downloads of their raw data only in XML, well I better know how to harmonize these data resources for visualization in Tableau or Flourish.
One result of this rapid change is that the vast majority of my guests tell us that the key skills for data scientists are not the abilities to build and use deep-learning infrastructures. Instead they are the abilities to learn on the fly and to communicate well in order to answer business questions, explaining complex results to nontechnical stakeholders. Aspiring data scientists, then, should focus less on techniques than on questions. New techniques come and go, but critical thinking and quantitative, domain-specific skills will remain in demand.--What Data Scientists Really Do, According to 35 Data Scientists--Hugo Bowen-Anderson
I love Elena's solution below. If the industry can agree on what we specifically mean when we say we need a data scientist--we can align objectives with workable tasks and outcomes. Think about it--do we say we need engineering professionals without specificity and appreciation for the granularity of skills for the different levels of engagement? Mechanical? Chemical? Civil? Electrical? Petroleum? All have a purpose unique to a generalized definition.
Perhaps there are mythical hordes of talent that know 8 different programming languages, well-versed in statistical and predictive modeling, machine learning, clustering and classification, Python, Scala, and Java, SQL, Hive, Spark, etc. and are subject matter experts, can data source, and communicate all of the processes and deliverables clearly to all stakeholders.
Good luck finding them--I don't have time for all of that. Too much work to do...
Elena Grewal, Head of Data Science at Airbnb...
I am Analytics track most days but need to consume massive amounts of Inference or I would be lost. We need to evolve along with the deluge of data threatening to obscure our ability to operationalize the promises of AI and IoT.
Or we are just smacking ourselves in the head with fish. Listen to the podcast below from The Economist Radio. The Secret History of the Future: The Body Electric.
We’ve used electricity to treat our brains for thousands of years, from placing electric fish on our heads to cure migraines to using electroconvulsive therapy to alleviate depression. But over time, our focus has shifted from restoring health to augmenting our abilities.
Think of data like a vitamin--essential but often ignored.