Business intelligence, Big Data and machine learning

  • A short time ago, I was talking to a friend about the huge potential of the digital transformation to become much more disruptive than the simple elimination of the paperwork involved in the conventional management of organisations.
  • It’s true that the digital transformation is here to stay and businesses that do not invest economic and human capital in going digital and automatizing their processes are destined to suffer a quite considerable loss of competitiveness.
  • Now these terms have been defined, in future posts we’ll talk about them in greater detail and explain why all these technologies provide key value to businesses.

A short time ago, I was talking to a friend about the huge potential of the digital transformation to become much more disruptive than the simple elimination of the paperwork involved in the conventional management of organisations. It’s true that the digital transformation is here to stay and businesses that do not invest economic and human capital in going digital and automatizing their processes are destined to suffer a quite considerable loss of competitiveness. However, the digitalisation of businesses processes also holds the germ of a much more transformative technology. This digitalisation is key to adding all kinds of data to business processes and, in that way, open the door to really efficient continuous improvement. As Lord Kelvin said, what is not defined cannot be measured, what is not measured cannot be improved, and what is not improved is fated to disappear.

However, within this aggregation of data we come up against a mishmash of buzzwords and grandiloquent phrases that are meaningless and which we are tired of hearing. Some of these are the oft-repeated terms machine learningbusiness intelligence and big data. However, instead of what many people have come to think due to the insistence of the buzzwordiansthese expressions are not meaningless and entail a powerful arsenal of technology and scientific developments. In fact, they were all coined many years ago, long before the buzzwordians made them lose their meaning. Yet, we often hear these terms associated with exactly the same meaning, which is something like: “to do things with data”.

Although some of my colleagues will probably disagree with me, I’m going to try to define the three terms in a way that’s easy to understand:

  • Business intelligence refers to the process of extracting knowledge and wisdom out of data. Obtaining information through the digitalisation of business processes can add a great deal of value to an organisation but, to do that, it is necessary to analyse the data and be able to see beyond the numbers and graphs. Many readers will have worked on some occasion with a business intelligence tool: Google Analytics.
  • Big data: perhaps one of the most-used terms recently. However, behind the expression we can find a large number of technological and scientific developments with a common objective: to respond to the barrage of data we are currently experiencing. To do this, data structures and ways of managing computer resources efficiently have been developed. I won’t go into what is considered “big”, because it’s hard to reach an agreement about that, but we can say that it’s a volume of data that is so large that classic algorithms and data structures are no longer useful. Here, we could go into structured data and non-structured data, but we’ll leave that for another post.
  • Machine learning. Finally, this is perhaps the term that’s least in fashion at the moment, although there seems to be a consensus that it’s going to be and very soon. The discipline of machine learning is responsible for designing intelligent systems that, based on the data they are given, are able to predict the future or automatize certain tasks which only people were capable of doing previously (such as transcribing text or detecting patterns). They are not a crystal ball, but can be useful in circumstances in which the human eye (or brain) is unable to reach conclusions fast enough simply because there is too much information to be analysed manually or because the machine system has access to more data sources than the human expert.

Now these terms have been defined, in future posts we’ll talk about them in greater detail and explain why all these technologies provide key value to businesses.

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