Democratization Of Data
putting data directly into the hands of employees

Data Democratization Is Key To Analytics Success
key-2To create competitive advantage, it is becoming more and more important for companies to use data analytics. For data analytics to be pervasive in an enterprise, it must be democratized, so that domain experts and employees can access data and perform analytics on a daily basis.

Impediments to Data Democratization
One of the biggest impediment to the democratization of data analytics is the complexity of existing products and the need to hire hard-to-find data scientists. The products put the burden of data miningimpediment-a and analysis solely on data scientists. The skill set of a data scientist has become too broad and diversified. In addition to statistics, math, machine learning and AI, it includes proficiency in programming languages, understanding complex query languages and subject matter expertise which only the end user posses. It is hard to find a data scientist with such a broad skill set. Moreover, hired data scientists spend more than 80% of their time extracting data instead of performing data analytics. In many cases customers have to pay hefty consultancy fees to install, configure and run a product.

Why Empower Employees?
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 empower-2interpreting it. Sometimes the semantics of a language and its usage is different. Employees know their data and how it is organized better than anyone else. In many cases, because of complexity of data, transferring domain knowledge to a data scientist is virtually impossible. For this reason it is very important to empower domain experts to be able to interact directly with the raw data, perform data mining and analytics. They can use data analysis to develop new products, increase productivity, enhance efficiency, evaluate risks, tighten security and discover new drugs.

How to Empower Employees
Iteru's empowerment of employees involves the following:

  1. Provide employees with automated data mining algorithms. The algorithms are configurable toimpediment-a1ensure that extracted data satisfies the objective of analysis.
  2. Sometimes it is very difficult to know what to do with the data because of its ambiguity and entanglements. Iteru provides interactive tools to explore and understand the data.
  3. Provide self-service analytics, machine learning and AI tools.

With Iteru's empowerment of employees the burden of data mining and analytics is no longer solely on data scientists. This decreases their inflated skill set by eliminating proficiency in programming languages, understanding of complex query languages and subject matter expertise. Data scientists with the limited skill set of statistics, math, machine learning and AI are easy to find. While employees mine the data and extract insights, data scientists become spot problem solvers, getting involved when needed. Moreover, they are always needed to improve the machine learning and AI tools and add new ones to the pool of self-service analytics.

Iteru Enables Data Scientists To Focus On What They Do Best
By separating data extraction and mining from analytics and by empowering domain experts there is data_scientist-2no need for data scientists to spend most of their time preparing and mining data they do not understand. Domain experts can accurately mine the data fast. The separation allows data scientists to focus on what they do best, which is providing a solutions based on statistics, math, machine learning and AI. Optimizing a solution takes time as it is an evolving rather than a 'one shot' process. On the other hand when data scientists are familiar with the data, they can use Iteru to mine the data in a short time and again enabling them to focus on what they do best.