Staring at the nifty gadget coiled around his wrist, Karthik looks past the workout features of his so-called IOT device – it counts the calories burned, and rings showing how often he has stood up to take a break from sitting, how many minutes of brisk activity completed; his goal? Close each ring every day. AWS, MongoDB, Python, Google App Engine, Varnish are some if we had to say about his expertness, he is passionate about solving tough problems and building lovely user experiences. As he sips coffee from a mug labeled Mem-Cache-D, we sat down to learn more about his current project.
Tell us more about your latest project?
Currently we are developing a system to discover insights where clients can review peers, ideate promotions, observe audience reviews, etc. This technology simplifies digital marketing for local businesses; helps attract new customers and generate more revenue.
The best part you enjoy…
Data aggregation tasks (main system that collects and process the data around web) this is ahead of data collection (using Crawlers, Controllers, and Processors) and storage tasks.
Seems to be a lot of tasks! How you keep at it?
Prabha who oversees this analytics project says it is difficult to build great products without great engineers. We play with an array of analytics tools before selecting one which can do more rigorous analytics, data interrogation, and can work through “what if” scenarios more tenaciously.
Having clean master data is one of the key issues we need to tackle when embracing analytics. But he warns against becoming too obsessed with data cleansing, indicating that data is never going to be sparkling clean.
Bottom-line – Get rid of the ‘noise’; distinguish between high-level analytics and low-level business intelligence.
What have you learned working? Specific
Building a good foundation for Cognitive analytics, with three primary components of cognitive computing:
- Natural language processing to make sense of structured and unstructured data.
- Parallel processing that allows fast processing of big data – from different sources simultaneously – in real time.
- Machine learning that generates context-based hypotheses.
Now that was a knowledge pack, it was very nice talking to Karthik, analytics IQ raised.
At Congruent we focus on leveraging both structured data (survey data, syndicated data, and transactional data) and unstructured data (free flowing text, voice recordings, email and chat, social media). We have lived that working in predictive analytics tool for Healthcare CRM (Microsoft PDW, TIBCO – Spotfire), social media analytics platform (AWS, Casper JS, Node JS, Open Calias, Mongo DB, MySQL, Python), demand generation product (Apache Nutch, Apache Hadoop) and then telecom revenue distribution and churn Analysis (SAS VA).
Progressive companies are embracing recent enhancements in data mining technology and data visualization tools to deliver results more dynamically in response to risk, dive deeper into organizational data, and deliver profound fact-based insights. And they are investing in many such initiatives, innovation only to get future-ready.
Did you ever think ten year olds’ would be using smartphones? My five years old nephew tries swiping images in my QWERTY phone . These are the future knowledge workers, coming into the business world having a pre-existing relationship with technology. They are ‘want it now’, they are wired for data, wired for speed, and wired for curiosity. ‘Generation impatient’ is what they are saying, in one article read recently. Impatient is the ‘new’ new. What do you think?