Big Data or Fat Data?

Has it ever happened to you that when you call a company you are a customer of, the operator asks you for all your details?


David Coral

Forbes

September 16, 2021

https://forbes.es/opinion/116964/big-data-o-fat-data/



Has it ever happened to you that when you call a company you are a customer of, the operator asks you for all your details? And what's worse, every time you are transferred to another person in another department, sometimes up to five times in the same call, you have to repeat those same details? Isn't it frustrating? Surely we are all thinking the same thing: if I am a customer and, therefore, they have all my details, why do they ask for them again and again when they have already checked my identity? It is curious, to say the least, that in a world where the intensive use of big data has become the mantra of every self-respecting company, these situations are repeated so often.


But let's add a little data to this big data paradox to make it even more surprising. If, according to a recent study by Capgemini, companies that use data to make decisions are 22% more profitable, their employees generate 70% more revenue and customer satisfaction increases by 19%, why do only 30% of Spanish companies use data?


What are the causes? Here are three possible answers:


  1. The 'lone ranger' syndrome: all too often the data expert, if there is one, is a lone hero who does not always have the backing of the entire workforce. Despite being a key player in the much-vaunted digital transformation, if they are not resourced and, above all, supported by the whole organisation, their task will be mission impossible. It is shocking that many companies spend huge resources on acquiring platform licences and do not invest enough to train their teams on how to get the most out of them.

  2. The "incommunicating vessels" syndrome: an intensive and intelligent use of data always involves breaking down silos within the company. Data means transversality, integration and collaboration between people and departments. And it is not always easy to break down the walls because some people misunderstand that sharing their data means losing power when it turns out to be the opposite.

  3. The "cyber paleolithic" syndrome: most companies do not know what to do with so much data. They capture more data than they are able to analyse because they have increasingly sophisticated tools at their disposal. However, the treatment and processing for decision making is still very poor. In other words, they are 21st century data collectors but Palaeolithic hunters.


If, as we have seen, it is complex how to manage data internally, as it requires a completely renewed organisational structure and culture, it is no less complex how to obtain it. Faced with such a challenging environment, which will soon be aggravated by the elimination of third-party cookies, here are three recommendations for each company to collect its own data (First Party Data):


  1. Go into Quid pro quo mode. Create a fair value exchange with your customers and potential customers. They will give their data when they perceive that they receive some value in return. It can be a content, product, service, offer, application, support, ...

  2. Driving the data virtuous circle. Data must be used responsibly and transparently to build loyalty and trust over time. This is a virtuous circle with the following sequence: the more data you collect from people, the more detailed you get to know them, which in turn allows you to create better user experiences that lead to a stronger bond with the brand.

  3. Think Pull and not just Push. Going out to capture data by investing in media advertising (Push strategy) is vital, but it is no less important to leave crumbs for them to approach and share their data proactively through a Pull approach. In other words, it is as important to go out and hunt for data as it is to set a good hook to catch it.


In short, if less than 5% of companies around the world use data science to gain a competitive advantage - as analysts as important as Thomas C. Redman, president of Data Quality Solutions, or Thomas H. Davenport, professor at Babson College and MIT, point out - is it not really because we are still in Fat mode and not Big? In other words, we have enough technology to stuff ourselves with data, but then we do not turn it into active muscle but into inert fat.




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