Social media has been a subject of controversy ever since it first appeared in 1997, long before Facebook was born. Remember Six Degrees or Friendster? They are just a couple
Reach, impressions, impact – if you work in advertising or communications, you’ll be very familiar with these terms. Around the world, marketing departments have made these terms their mantra; in
PS: I love you. Get your free e-mail at Hotmail – Timothy C. Draper We can define Growth Hacking as a continuous process in which different sets methods and
The internet and digital media are the new mass media. This statement is almost a mantra in the world of marketing, and an unquestionable truth for companies and media buying
We recently delved into the role of the Data Scientist. In the study we highlighted the importance of making insight-based decisions, but we did not focus on the insights themselves.
2016 was not the best year for wearables. After a period of growth in 2014 and 2015 best marked by the novelty imposing on functionality, the competition with smartphones and
Once a project related to data science has developed enough within an organization, the time to measure the results of the data scientist’s work comes. How to do it? First,
Once the company has definitively embarked on the culture of Big Data, the possibilities open by the data analytics are immense. The coexistence between analysts and specialists in a company
The customer journey has long stopped being linear and has become a somewhat chaotic process. For years, we’ve known that the purchase decision gets made from multiple points and different
The data scientist must be comfortable with different types of analytics tools. Within engineering related to the construction of data processing systems, there are three basic tools to embark upon
We all can agree that measurement is one of the greatest obsessions in management. Measuring beyond the easy or the obvious (since you can count “likes,” it’s easy to think
Contrary to what many people assume, the data scientist is not necessarily a professional with a “numerical” background. While the disciplines most commonly associated with data science like mathematics, statistics,
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