Data Science in a Modern SME – Part 2!


In my previous post I explained how data science has been used by organisations to gain competitive advantage. In this post I will explain how I see the company I work for, a fairly typical modern software company, gaining competitive advantage from data science. Like the last post this is an adaptation of a posting that I shared internally with my colleagues.


Where I work we do ‘digital governance’. The major part of our value proposition is a platform which monitors the quality of clients’ websites and allows them to easily manage what are usually (in our clients’ cases) massive, sprawling things.

Every day there are 500 million tweets sent. Some non-trivial portion of those tweets are related to issues of website quality and governance. It is possible to perform something called sentiment analysis on these tweets to determine the attitude of the tweeter towards those issues. Are they frustrated with their current governance framework? Are they frustrated by the lack of any framework at all? If we could narrow down the set of all tweets to those that would be promising sales prospects for us, then that would be a valuable thing. And we can do that, using the aforementioned sentiment analysis along with textual data analysis.

Improved system uptime

Despite the best efforts of our dev team and especially our ops team, our systems break down sometimes, affecting the end user’s experience. This is bad. Fortunately, there exist techniques called predictive modelling and cluster analysis which can help us to predict and avoid future breakdowns. Essentially, we can collect the system data from around the time of the problem and feed it into a model which will look for, and hopefully find, patterns. These patterns can help us diagnose the root cause of problems, as well as give us advance warnings of when they are about to happen again.

Business problem optimisation

The set of techniques called optimisation involves translating a real-world business problem into its mathematical formulation, and then solving the mathematical representation for the best solution. This is an application of data science that far predates the current hype around the field. It has applications for all types of business problems, e.g. managing workforce churn, purchasing, and cash management.

Pleasant surprises

As I mentioned at the end of my previous post, data science can give us insights that we were not specifically looking for. There are a group of techniques which involve feeding a data set into a model, running the model, and seeing what insights the model throws up. These techniques are collectively called unsupervised data mining, and they can throw up insights such as clusters of similar objects, outliers, local influencers among groups, and bridges between different groups. I think it is obvious how such insights might help our sales efforts, for example.

Other opportunities

I hope that the opportunities that I have outlined above seem interesting at the very least. I am not an expert on data science, and it is certain that with more knowledge I could find more and more opportunities in the business I work in for the profitable application of data science. Were others in the business to acquire only some data science knowledge, new ideas would start sprouting from all directions. This leads me to my next point…

Develop in-house capabilities

Data science capability is rare. There just aren’t that many qualified data scientists out there. This will change in the next 5-10 years as universities adapt to market forces, but right now it is difficult for a firm to acquire external data science capability. Any firm that does acquire data science capability will therefore have a large competitive advantage. In my company we don’t have data scientists, but we do have many skilled software developers of an analytic bent. It is very feasible that they can quickly learn the more basic of the techniques that I mentioned in earlier paragraphs. All tech companies are the same.

Look outside?

While it is very difficult and expensive, currently, to bring in a data scientist as a permanent member of staff, there are ways that an organisation can acquire capability without developing it internally, and without breaking the bank. These are all practices recommended in the book Data Science for Business:

  • Fund academic research. PhD students work for peanuts, and if an organisation can exploit them encourage them to work on its problems, it can get them solved very cheaply. Of course, the results will be shared with the academic community and therefore may be available to competitors.
  • Take on a data scientist as a ‘scientific advisor’.
  • Hire a third party firm to do your data science. The book notes the caveat that the interests of data science firms are not always well aligned with the interests of their customers. The authors don’t go into detail on what they mean by that.
  • Post a competition on, a site which allows organisations to post problems along with a prize. The competitions are open to the public, and often attract excellent data scientists. NASA has used the site in the past to gather solutions to its data science problems.

Now for the threats…

The flip side of everything that I’ve said in this post is that our competitors may profitably do data science, and may use it to gain a competitive advantage over us. We should be aware of this threat. What data do our competitors collect that we don’t, and how may it be used by them to gain a competitive advantage? What data science capability do they have that we don’t? Is there a way that a purely data-driven firm may enter our field and do through data what we do through consultancy, thereby eroding one of our key competitive advantages?

Data science is an equal opportunities tool 🙂

Data Science in a Modern SME – Part 2!

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