Data Science: why you need it
“Without data, you’re just another person with an opinion.”
That’s a line from Edward Deming, whose aim was to stress the importance of using data to solve real business problems. As a matter of fact, the focus should not be on how advanced the analytical tools are, but on the value they can bring to the business.
Therefore, when talking about Data Science, we must not confuse it with its tools: it is first of all a scientific method and as such, its task is giving a realistic, predictive and prescriptive description of reality and of the laws governing it.
That said, data must be interpreted according to the specific organization: only in this way can they guide decision-making processes in a meaningful way.
The history of Data Science
But how was Data Science born? You may be familiar with the term “data mining“. It indicates the ability to extract useful information from data.
In 2001 William S. Cleveland, professor of Statistics at Purdue University, published an article on the International Statistics Review. He had the brilliant intuition of combining data mining with computer science. From that moment on, statistics can rely on incredible computing power.
But the point of no return was passed with the rise of the web 2.0: since any user got the opportunity to interact and publish content online, the number of digital “fingerprints” has grown exponentially. That’s the so-called Big Data: just consider that from the dawn of mankind until 2003, 5 exabytes of content have been produced; now we make that same amount every two days.
The skills of a Data Scientist
The need for insights out of this huge amount of data was the right terrain for the rise of new professional roles employing Data Science: Data Scientists.
Someone refers at it as the sexiest profession of the 21st century. Let’s see what it takes to be one.
Drew Convay, an American data scientist, has created a Venn diagram about Data Science, where three different areas of expertise intersect. In addition, an adaptive curiosity can’t be ignored, as we’re talking about a sector that evolves at a very high speed.
Depending on the prevalence of one of these skills above the other, Alessandro Giaume, author of “Data Scientist. Between competitiveness and innovation” has traced four profiles of Data Scientists:
Data Business People
Their main abilities and interest reside in the business development and management, they have executive responsibilities but also the technical knowledge to guide Data Science projects (prevalence of Substantive Expertise)
They have total autonomy over the entire development of analysis functions, they create custom tools and proprietary problem-solving methods, they have a deep knowledge of open source tools and open data (mainly hacking skills).
They focus on data extraction and management, especially in the Big Data area, and they have strong expertise in the design and development of Machine Learning systems (strong presence of Hacking skills, but also good at Math&Stats).
It’s the most complete profile. They understand complex and articulated phenomena and they study the cognitive paths characterizing customers and prospects. They apply a scientific method and have a strong aptitude for action involving the end users, thanks to a holistic view of processes (prevalence of Math&Stats skills, but also very good at the other two).
The application of Data Science must be adequate to the data-drivenness of each company: this term indicates how much the data culture has entered an organization. The more data-driven is a company, the greater the skills required for the Data Scientist.
Why apply it to Marketing?
Most marketers stick with a descriptive approach to data (Analitycs, Business Intelligence etc.), someone applies a predictive approach (identifying trends through metrics and making predictions), but only few go beyond.
When analyzed with a diagnostic and semantic approach (the context and its meaning are reconstructed) thanks to Artificial Intelligence, data can guide us in creating knowledge and even autonomously identify the areas of intervention, ending up suggesting prescriptions (what’s the best thing to do).
This data-driven rigor in decision-making enable the marketing teams handling campaigns to say with certainty what works and what doesn’t, and we know that firm evidence – figures in hand – can melt the heart of the most stubborn managers. What really matters is the ROI (Return-on-investment).
What is Content Intelligence?
These insights help marketers through the creative process itself. This is an age where each client is a segment on their own, so we must provide them with personalized communication, answering very specific needs.
That’s why Content Intelligence is the ideal solution: a tool to get data about user interests out of their behavior with the content published by the brand. By measuring content performance, it gives us an understanding of how a given message is influencing (and thus converting) the recipient.
But how does it work? Technologies that apply Content Intelligence (e.g. THRON, an Italian DAM Saas) work on two steps:
Application of AI on company contents
Machine Learning features such as speech-to-text, image recognition, semantic analysis, when applied to corporate assets can understand and categorize them autonomously, describing their topics with metadata.
Matching: tag topics = customer interests
When a user looks at them, AI algorithms associate these tags with the user’s profile: it’s like having a fingerprint scanner that tells you in real time what a particular person is interested in, at that precise moment.
The benefits of adopting a data-driven strategy like Content Intelligence
It eliminates data silos
With the rationalization brought by AI you’ll have a single hub where every piece of content is tagged, and can be easily retrieved through search strings.
It is updated in real time
User data must be as up to date as possible: combining the data collected “live” from Content Intelligence with the one stored on other platforms like the CRM provides a truly complete overview of the user’s viewing path, and allows a precise, dynamic segmentation.
It suggests you the most performing editorial choices
It is possible to know, from the analysis of content performance, which are the topics, formats, channels etc. preferred by the audience: with this data set, the creative process is guided and you can automate marketing initiatives, letting the system suggest the most relevant content. Tools like KPI6 stand out when it comes to support similar strategies: our AI-powered insights will tell you granular, detailed, reliable information about what your audience likes (and what they like to talk and read about).
In the company
However, not all companies can, for economical and organizational reasons, resort to a team of Data Science specialists or programmers for Big Data platforms, as they are expensive professionals requiring specific training.
There are Saas (Software-as-service) platforms though, that only by paying a monthly/annual fee, will allow direct access to the results carried out by the AI, without having to resort to specialists and without having to bear the development and maintenance costs of a physical IT infrastructure.
Applying Data Science will let you know in a scientific way what is influencing the corporate ROI, starting from brand communication, so it is a business opportunity not to be missed.
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