Everyone talks about data science these days, as well as A.I., since the value these disciplines can add to an organization is being verified more and more. However, there are organizations out there that are not ready yet to make use of data science, even if they have ads for data scientists in various job forums. Before applying to places like that, you may want to answer this question for yourself: is this organization I’m interested in data science ready?
Just because an organization has seen value in a data science proof-of-concept (PoC) project, it doesn't make it ready to employ and utilize data science professionals. First of all, it has to have a solid leadership team, one that at the very least has a CTO who has worked with data scientists, though additional roles like that of a CIO and a CDO, would also be useful. If the C-level team of an organization hasn't worked with data scientists and doesn't have a clear idea of what data science can and what it cannot do, then this is a red flag.
In addition, an organization that has access to a variety of data streams, even if these don’t qualify for “big data” status, is essential for making it data science ready. If all its data is in Excel spreadsheets and SQL data bases, perhaps they need a data analyst, a business intelligence professional, or a statistician. If they do get a data scientist, they won’t be able to do much more with her, since she will not have enough to work with and provide sufficient value, that can translate to a positive ROI for her group. That data scientist is better off working somewhere else where they make better use of her skills and her mindset.
Moreover, a data science ready organization has realistic expectations and a good plan about how to utilize its data resources. Just because it has access to good data, it doesn’t mean that it can get value from it, even if it employs a group of very talented data scientists. It also need to know what it is going to do with it, what data products it can create, how it is going to leverage the insights the data science team provides, etc. All that is not going to take place in the next quarter necessarily, especially if the organization is new to data science. So, expecting some ground-breaking results within the next 3 months would be naive and financially irresponsible. An investment like this is bound to take some time before it yields dividends and if the organization is not aware of this, then it may not be ready just yet.
Beyond these signs, there are other, more specialized ones that are more domain-specific or data-specific. However, mentioning them here would make the article so long that you’ll need to run some text analytics system on it to derive all the information from it! So, let’s just say that there are other thing that can be good predictors as to whether an organization is worth your time as a data scientist, or in the case you are a hiring manager of such an organization, whether you should start recruiting data scientists at this point. After all, data science is a long game, so there is no point rushing into it. It’s more beneficial if it is conducted in an environment that is conducive to it, and capable of fostering a congruent and efficient team, poised to add value to whatever data it utilizes.
Zacharias Voulgaris, PhD
Passionate data scientist with a foxy approach to technology, particularly related to A.I.