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Analytics Write For Us – There is no doubt that the world of advanced data analytics requires complex techniques, and it can take a long time to reach exciting conclusions for the business. Sometimes it is forgotten that computer tools and statistics are a means of obtaining value not an end in themselves. The value for the business is obtained when, based on conclusions based on data, consequential decisions are made. You usually also have to draw up an action plan(changes to introduce) to carry them out and, later, measure if the expected value is obtained. When we talk about importance, we usually refer to a concept that can be expressed in economic figures. However, it can be extended to other value types, such as increasing life expectancy or reducing crime.

One of the keys to getting measurable value is starting with the right questions and spending some time asking and reflecting on them before getting down to business with implementation. In this article, I present specific characteristics of these questions so that the entire value return cycle can be completed and examples of of commercial activity contexts.

Questions we must ask ourselves to obtain value and its characteristics:

Are they relevant to a specific audience?

We must study whether the questions are aimed at specific recipients. Also, who has the power to make decisions and sponsor subsequent changes? Targeting investors in a startup, the sales team, or the technology team is different. For example, a sales manager may be interested in this question: How will a 5% price increase for certain products affect our sales? Meanwhile, the investor wants to know: What is the expected return on investment if the economic cycle continues to expand?

For which questions is there currently no established answer?

This is very clear from scientific studies. Let’s ask ourselves about the correlation between CO2 levels in the atmosphere and the incidence of certain respiratory diseases. We will be able to find many rigorous studies with evidence in this regard. And if necessary, we can continue to deepen without starting from scratch, for example, restricting the analysis to a specific city where there are still no reliable statistics.

Taken to the business world, a question like “Who are our ten best customers?” indeed is a list that someone has already made in the past. It is about knowing under what hypothesis it was carried out even if there is already an automated procedure to dispose of the information. In addition, it is a question that perhaps can be answered without resorting to advanced analytical techniques and, starting from it, calculating the value of the complete customer life cycle, which does require making future estimates.

Are they plausible questions?

This is about understanding how the business works. For example, raise hypotheses about cause-effect relationships that can be justified. To put an extreme case, absurd correlations are found online, such as the Norwegian crude oil import data vs train accidents. Basically, this has to do with putting the correct focus on what data is relevant a priori to make decisions based on it.

Although it makes sense to implement a “data lake” in specific contexts, we must not fall into the so-called “Diogenes syndrome of data”. If we start doing random statistical calculations with all the available data, we will find correlations irrelevant to business. Let us remember here a fundamental maxim; correlation does not imply causation. For this reason, it is essential that data scientist profiles have some business knowledge or work closely with other mixed profiles.

What questions can be answered?

This seems obvious, but the truth is that projects have at least cost and time restrictions. Once exceeded, it is normal for them to be considered failures. It is also possible that we do not have the relevant data to answer those questions and it is not feasible to collect it.

Again, an extreme case may be looking for a method to travel back in time. Although it is an interesting question, and where I understand that the laws of relativity could allow it, it is almost certainly not going to be implemented in 50 years, starting with the lack of suitable materials. It could be something worse, which proves unfeasible in the end. Going back to more everyday examples, a question of the type “what is the probability of flight for each of my clients in one month’s time?” it is not something that we are going to answer in two weeks by hiring a Data Scientist and a Big Data Engineer, if it is the first time that we tackle a project of this type. Perhaps we can establish a proof of concept with the help of cloud technologies, but don’t expect record accuracy.

Are they concrete and specific questions?:

It’s quite different to ask “How is customer sentiment about our brand?” to ask “What percentage of twitter opinions are negative regarding our new product model?” Both questions are legitimate in the business context. But, in the second case, it is something that we will be able to start implementing earlier, since it better delimits the technologies to be used and the relevant data . That is, we have narrowed the scope. And it is to be expected that in the second case we can also make faster decisions , in the event that there are excessive negative opinions, detecting if they are associated with aspects of price, quality, support, even if there are particular interests or not, etc.

What questions allow for actionable responses?

Let’s imagine that we are interested in knowing the “cluster” segments of our clients, in order to direct a specific commercial campaign to some of them. Through unsupervised learning we discovered that there is a segment that mainly buys from us when discounts are available, and rarely does so at normal prices. However, it is not technically possible for the company to send a discount coupon only to these customers, because it does not have their updated email addresses, but instead advertises discount coupons through banner ads on third-party sites.

Although knowing the segments is interesting, and perhaps you can try a partial delivery or that the banner is displayed only in certain circumstances, in the end much of the potential value is lost by not being able to contact many of the customers directly. On this occasion we want to use advanced techniques but we have not yet solved other more basic aspects such as having quality data.

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