Currently there are some 2.5 quintillion data per day, a figure that is increasing from the use of the Internet of Things; and not only that, 90% of global data was generated in the last two years. As if this were not enough, more than half of web searches are done from a mobile phone, more than 3.7 billion humans use the internet and, on average, Google processes more than 40,000 searches per second equivalent to 3.5 billion searches per day.
The ability of companies to successfully adapt to this information revolution (known as “digital Darwinism”), should lead them to prioritize data analysis and interpretation in their strategies (present and future), as well as to recruit and train qualified personnel for this purpose.
Data Culture or Data Driven approach in companies
A recent Gartner survey showed that in 2020 most organizations lacked sufficient AI and data literacy skills to achieve business value. The results of this survey showed that 75% of employees felt “uncomfortable” when working with data.
Although many digital companies already have departments of Business Intelligence or are surrounded by experts in Big Data Y Machine Learning, the question would be: should this knowledge remain in watertight compartments of the company o It should be a transversal competence for all the employees of the organization?
There would be the difference between a company that is Data Driven and one that isn’t: when the majority of human resources have a data literacy and culture that allows them to hold reasoned conversations based on quantifiable evidence, it is an approach Data Driven. Otherwise would be an organization that has a lot of data and technology to capture it but knowledge remains in specific silos of the company, such as the IT sector.
It is not just about knowing how to read data, but about asking good questions. And once done, design and build the mechanisms for obtaining that data.
There is the imaginary of believing that those who have studied computer science or engineering, have the skills to interpret and analyze data. They may indeed have the ability to manipulate, transform, mold them to create information and visualization systems. But -in my experience- asking good questions, establishing the action plan to get the data and making conclusions, are skills that are more frequently found in professionals of other types of training. The sciences that study natural phenomena (such as physics or biology) or the social ones (such as economics or medicine) have a greater capacity in the design of these processes
The art of knowing how to measure
Not because it is trite, the phrase “measure and then improve” (or what is not measured cannot be improved), is no longer true. In Big Data scenarios, where there are many observations, from almost any point of view and with different levels of certainty and confidence, measurement is a key process. First wanting to measure, then knowing how to measure.
Co Founder of 7Puentes.
Source: Ambito
