Zacharias 馃悵 Voulgaris

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Key Characteristics of a Data Science Ready Organization

Key Characteristics of a Data Science Ready Organization

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Throughout my career, I've worked with various organizations, from one that I'm not at liberty to talk about (it was via Georgia Tech) to others that I'd rather not talk about since their presence is an insult to data science itself! Of course, many of the organizations I worked with (or for) weren't in these extremes. Some of them were reputable companies that were what many of us would refer to as "data science ready." But what does that mean exactly? Is it something only the data science adepts understand or something that everyone has access to in some way?

Being data science ready is not as simple as ticking certain boxes. However, all data science-ready companies have a few characteristics in common. So, if your organization has these characteristics (or at least most of them), it's quite likely to be data science ready (DSR).

For starters, a DSR organization has lots of data. They don鈥檛 need to have petabytes of data, but if they do, they are readier than a company that has just a hard drive worth of data. As a rule of thumb, if your data can fit in a modern Excel spreadsheet, you don鈥檛 have enough to bother with a data scientist. If your company can handle its data using a data architect or two, it may or may not need a data scientist (sometimes a data analyst would be ample). If your organization finds itself having more data than it knows what to do with, it鈥檚 probably high time its HR team puts together a data scientist spec.

DSR organizations may also have specific needs that relate to Analytics. For example, their stakeholders may need reports that are difficult if not impossible to produce with the conventional slice-and-dice approach, which can be undertaken by any junior-level analyst or even a good DBA. If you need to have dynamic dashboards, predictive analytics, or prescriptive analytics, you are likely to need a data scientist. Also, if you have to process data in real-time, no conventional analyst will be able to do the trick since that's well within data science territory.

A DSR organization may derive its niche from deep techs, such as A.I. If that's the case, it is bound to need a data scientist or even a data science team. Many brilliant professionals can handle the A.I. part (e.g., the Robotic Process Automation or even the Deep Neural Networks involved), and having one or two such professionals around can be useful. If that organization needs to engage with this technology on a deeper level, however, it would require a data scientist, particularly one with knowledge of A.I. models.


Cloud computing is another technology that a DSR organization is bound to delve into, to some extent. Although many organizations leverage the cloud for storage purposes as well as virtual servers for their websites, it's not a big leap to leverage other services of it, such as analytics. If an organization is appreciative of the merits of the cloud (without having its head in the clouds!) it's likely data science ready, to some extent, depending on the problems it tackles. At the very least, some data science consultation would be useful as it could pinpoint potential projects that could yield value, be it monetary or marketing-related.

Beyond these characteristics, there are plenty more that are more industry-specific. In any case, if an organization is somewhat data-driven or is tech-savvy enough to see the value data science has to offer, it's something to consider since how DSR an organization is, also depends on its mindset regarding these matters. What's your organization like?


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