This Client is one of the fastest growing retail clothing chains in India. The Client has more than 50 stores located in 18 cities, and offering more than 8 in-house brands of apparel. The Client business is experiencing consistent growth with new stores opening in numerous cities at prime locations.
Predictive analytics is more refined, more dependable and more comprehensive than ever. The foundation for predictive analysis is a great predictive analytics tool, and features and function that include assisted predictive modeling.
This Client is a pioneer speciality retail chain with pharmacy and wellness stores in India. The Client owns and manages a chain of stores located across Ahmedabad, Gandhinagar and Vadodara and offers pharmacy products sourced from manufacturers or channel partners.
As one of its Strategic Assumptions, Gartner predicted that ‘By 2020, more than 40% of data science tasks will be automated, resulting in increased productivity and broader usage by citizen data scientists.’ Gartner also predicts that the number of Citizen Data Scientists will grow five times faster than the number of expert data scientists through 2020, and that by 2019 the amount of advanced analytics produced by citizen data scientists will surpass that produced by data scientists.
Smart data discovery is not dependent on a few geniuses sitting in a science lab somewhere! With appropriate data discovery tools your team members can leverage their knowledge of their role and domain, and the needs of their division, team or business unit to find the data and insight that is most meaningful to them.
When we use terms like ‘predictive analytics’, it sometimes puts off the general business population. Team members might envision everything from a crystal ball to complex charts and graphs with unreadable numbers and conclusions. While predictive analytics techniques and predictive modeling does include complicated algorithms.
This Client is one of the fastest growing life insurance companies in India and offers a diverse range of wealth management, protection and retirement solutions.
Let’s talk about data preparation! That often cumbersome, always complex process by which data scientists and IT professionals gather and prepare data for analysis. Data prep involves the extraction and transformation of raw data to prepare that data for analysis. It includes cleaning and reducing that data by removing outliers and standardizing data formats, among other things. Without data preparation, data analysis can often be misleading and incorrect – garbage in/garbage out, if you will.
Sometimes we are so focused on perfection that we do not see the benefit of agility. Consider the emergence of the brainstorming concept as an example. We learned quite some time ago that if used the brainstorming concept of freewheeling, non-judgmental discussion we could bounce ideas off one another and often come up with innovative ideas that would not have resulted from a discussion that was more restrictive.
We all understand the value and innovation inherent in natural language processing (NLP). Think about the ease of searching for the answer to a question on Google. Users with average skills can ask a question and get an answer, a list of search results that address their interests and support and assistance to take the next step.
