The first native data driven company to the Fortune List in 10 years

This is the list of top 10 Fortune companies: Walmart: Founded in 1962. Berkshire Hathaway: Founded in 1839. Apple: Founded in 1976. Exxon mobile: Founded in 1870. McKesson: Founded in 1833. United Health Group: Founded in 1977. CVS Health: Founded in 1963. General Motors: Founded in 1908. AT&T: Founded in 1983. Ford Motors: Founded in 1903. Information was historically expensive until the following products were lunched. Google Analytics: Was launched in 2005. It was after 2009 that it added features to convert the platform into a Corporative Solution. Amazon Web Services: Launched in 20016. Apache Hadoop: Launched in 2011. Google

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Six habits of most successful analytics professionals

Whether you are an experience analytics professional, or you are just making your first steps in Analytics these are six habits that will definitely improve your performance. Planning vs implementation time distribution: The digital industry is definitely pragmatic and due its dynamism there’s normally no time for anything. However a common mistake is going from the idea to the implementation with no, or not enough planning. If I would have to put a number, I would say that it has to be 80% planning and 20% implementation. But why almost nobody invest 80% in planning and research? Because during planning

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Are you still struggling with data? Here is why

It’s been seven years since Meta Analytics was published. In that book instead of focusing on the technical aspects I wrote mainly about two main pillars on the insights process. 1) The object: The object is what the subject (the person) will analyze. Understanding the “substance” of what we are analyzing is key to generate a proper interpretation of what is been observe. The object in our case are companies. Companies are systems, a set of things that interact together with a common objective which is making money today (ie Ebitda) and in the future (ie Purchasing intention). The parts

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All you need to know about the Google Attribution Model which is not much by now

Well, remember that in my past post I was talking about trusting your main source of value to a third party that can have a different objective/s than yours? I also mentioned the example of using Google Attribution Model. Well, if I would be Google I would try with all my might to be the one that sets the standard of the attribution model the people use. Why? Because the attribution model is the one that tells you how and where you should invest your advertising budget. So, apparently Google is aligned with that idea, because has just launched their “free Google Attribution” model.

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Here’s why you shouldn’t use someone else’s Attribution Model

The other day I was doing a presentation about machine learning applied to attribution models and the issues of standard attribution models: They are not based on a model that tries to represent a reality, with the higher level of certainty as possible, but on a completely discretionary decision (normally from a HIPPO, Highest Paid Person Opinion). Since they are not trying to represent the reality but to define a criteria to standarize the measurement, all the information that flows from them (Acquisition Cost, repayment period, etc) is wrong. So basically, you will be making your marketing decisions based on wrong premises. Meaning that

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Programmatic Buying behind the scene

Following the previous post about what computers can and can’t do, let’s go to the specifics. Fortunately during the last years it has become clear that Data Science has huge capabilities for changing the way we are doing business. One of those cases is programmatic buying. The programmatic platforms have completely changed the way brands invest their media budget and optimize their results. However it is really important to understand that’s behind the scenes in order to really understand what can and what can’t expect from Programmatic buying. What’s programmatic buying: Programmatic buying refers to the technology that allow investing the media budgets through real time

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This is what you have to know about artificial intelligence

Last week I was invited to Webcongress Colombia to talk about the future of Data and how Data can transform the world. It was more than 1,300 professionals expecting to talk about the latest advances in technology. Most of the people I was talking with was really optimistic about what a computer can, or can’t do. My normal answer is “Computers are stupid and bureaucratic at creating but great repeating things, while humans are fabulous creating but very clumsy at repeating things”. As I wrote in my book Meta Analytics, repetitive activities become transparencies for humans, so at some point we can’t identify mistakes that are not

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Landing to Webcongress Bogota with the lastest news on Big Data Analytics

Ouali did it again. He is putting together more than 1200 people from the digital marketing industry in one just one place to talk about the latest news and techniques. Webcongress had become the main tech event and I’m pleased to tell you that again, I’m part of it. I have the pleasure of joining a panel with Mónica María Zuluaga, Marketing Director at Caracol Televisión to discuss how Big Data Analytics is changing the way we market our products. I will also be presenting information on the level of evolution of the Latin American market in general and the Colombian market in

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Holistic (reductionist) vs Systemic approach of organizations and its impact in goals and metrics

We were talking a lot about the importance of considering companies as what they are, systems. Years of an holistic (Reductionist) approach of organizations generated a huge negative impact. The holistic approach, the scientific attempt to provide explanation of complex things in terms of ever smaller entities, says that if you have a company (system) and you separate it into its parts, then you improve each of its parts and put them back together you will have a better company (or system). The result of this thought was: 1. Independence between departments: If we think that we can have a

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How to develop a micro-conversions measurement environment

It’s normally easy to achieve a long term objetive if you can break it into short term goals. There’s a big different in arriving to your office with the idea you have to increase your company revenue in 20 percent and arriving to your office with a list of 5 tasks you have to perform in order to drive the company 5% closer to its objetive. Right? It’s great having a long term vision, but that’s for the C level, they are in charge of the company vision. Once that the future is clear enough it’s time to complete the

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