Most of the new data analysis products put the burden of extracting insights from data solely on data scientists. For this reason the skill set of a data scientist, shown below, became too broad and diversified. In reality, it is a skill set of a team not an individual.
To use data analysis products customers have to hire hard to find data scientists. A hired data scientist spends more than 80% of his/her time extracting data instead of performing data analytics. In some cases customers have to pay hefty consultancy fees to install, configure and run a product. Iteru decided the best solution is to empower domain experts within an organization to perform data analysis themselves. Analytics become pervasive by making it accessible to employees. This is the essence of democratizing data.
Empowerment of domain experts is attained by the following:
1- Enable domain experts to directly access raw data and provide them with configurable automated data mining algorithms. This eliminates five of the eight components of the skill set. It is easy to find data scientists with the limited skill set. The components eliminated are shown below:
2- Provide self-service analytics & machine learning. This makes it possible to extract the insights needed without direct involvement of data scientists.
While domain experts mine the data and extract insights, data scientists can enhance the existing self-service algorithms or add new ones. They become spot problem solvers, getting involved when needed.
Why Empower Domain Experts?
Companies in different industries have their own domain knowledge, terminology and specialized terms. Even within the same industry, each company has its own way of organizing data and interpreting it. Sometimes the semantics of a language and its usage is different. Domain experts know their data and how it is organized better than anyone else. For this reason it is very important to empower them so that they can extract information hidden in that data on a daily basis. They can perform data analysis needed for developing new products, perform research, increase productivity, enhance efficiency, evaluate risks, etc.