Autora: Lorena Sánchez Osorio

I know, you're going to hate me, but let me develop my idea first, and then the data scientists can hate me if they want to

We are in the midst of a very interesting phenomenon; we have mathematicians, statisticians, engineers, and other professions studying data science. All are aiming for people to make decisions based on data because this way we minimize many risks or manage processes better. However, what is the average result?  

 

Perfect algorithms with incredible results that nobody cares to review. It generates frustration and a feeling of unachieved success, and it's there, right there, where I come to intervene.

We forget that the foundation for a successful data management lies with the individual.

The person generating data or from whom we want data is one person, the one analyzing data is another person, and the one making decisions with data is yet another person. What's the common denominator? Exactly, the person. Are we managing it correctly?

Mckinsey in its article: “Breaking away: The secrets to scaling analytics” tells us about the biggest challenge that analytics projects must overcome. They refer to it as 'crossing the last mile,' and it relates to everything the project should do to provide the right knowledge to the right people for proper use of the data. It sounds very logical, but at least in Colombia, this isn't common. In Colombia, we have many data gurus telling companies how they should analyze their data and how they should consume it. They expect that once the analytics project is completed, the result will be adopted by the company, and that's it – everything worked perfectly. However, reality shows us that there are more discarded dashboards or dashboards used as data exporters to Excel than those that are genuinely used.

So, how do we build valuable solutions? Simple: with the user!  Mr. Data Requirements Officer, try to understand what they want, what they like, what they need, and find ways to bring together their preferences, desires, and needs in a solution. Enchant your customer and repeat the process. 

Here's a disclaimer: In very large companies, decision-makers are kept happy, but it's impossible to keep all analysts happy. Thus, you can create a culture where analysts agree with decision-makers and their way of interpreting data, potentially freeing them from late-night work during every closing (You enchant the analysts differently).

If a person agrees with their dashboard, wants it, it meets their needs, and they like it, there's a very high probability of adoption and data growth within the company. Now, the company is one step closer to becoming a data-driven organization. We can say that there is no data project that doesn't involve people, so why talk about technology?  

We're asking for a stronger change within individuals and the organization to include data. So, why talk about technology? Typically, our gurus in exact sciences don't have very good social skills. Are they the ones who will understand people?

So, in conclusion:

Analytics projects are magical because they have an interdisciplinary team working on the best way to generate value for the organization with data, combining human sciences, exact sciences, and managerial skills.

Successful analytics projects are on average 40% technology and 60% adoption of that technology (Data based on experience with Wadua). If we ignore the 60%, we only have a 40% chance of success, and I don't even know what the data project is about.

Our slogan: 'Technological solutions from people to people' didn't come overnight. It was the result of understanding that analytics goes far beyond what a good algorithm can achieve. At Wadua Analytics, we handle analytics projects from start to finish, positively impacting profitability, and that is NOT achieved SOLELY with technology.

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