RPRuban Phukan: Our philosophy is that all business decisions in an organization should be based on data. Experiences, intuition and gut feel should always be validated against data.
The goal of DataRPM is to enable any individual in any organization, irrespective of their technical expertise, to have the power of a data scientist and be able to analyze data themselves and leverage the same for decision-making in the fastest, cheapest, scalable and the most natural way.
The key driver in achieving this goal is our patent-pending Natural Language Data Discovery technology that can automatically model data from disparate data sources and provides organizations with a simple Search bar where anyone can ask a question in English and the platform delivers answers from data with intuitive and interactive visualization automatically and instantly.
AIM: What is your approach to face the challenge of meeting the needs of so many clients across vast geographies with limited resources?
RP: We believe that the only way to enable businesses across the globe to leverage their data in the easiest and affordable way is through the use of Smart Machines that automates the time consuming manual processes in data discovery.
Smart Machine uses cognitive computing technologies to deliver algorithms that can think and act like humans. This helps automate the otherwise time, resource and cost intensive processes like modeling data from disparate data sources to deliver a simple natural language question-answering interface that requires no learning curve for end users. This makes it extremely simple for us to deliver data discovery solutions at global scale in a near self-service manner.
Any business can now connect DataRPM to their data sources, our data connectors crawls and indexes the data and makes it query-able or search-able, thus democratizing enterprise data discovery in a similar way that Google had democratized information discovery on the web.
AIM: What are the key differentiators in your analytical solutions?
RP: We use the futuristic cognitive computing technologies to deliver smart machine for data discovery for which we have patent pending. There are three pioneering differentiators in our platform that puts us generations ahead of any other solution available in the market today. These are:
i) Natural Language Question Answering: We empower our customers to analyze data by just asking questions in English. The smart machine figures out how to translate that question into a data query and return back the results with appropriate visualizations. This enables any individual to analyze data like a data scientist without requiring any technical training or software learning curve.
ii) Ad-hoc Real-time Big Data Computation Search: This component enables data search and computation based on the user queries in real-time. It supports any ad-hoc queries, dynamic slice-dice and drill-downs without requiring any cubes setup or pre-aggregation. This is designed to run on cheap commodity hardware that scales-out seamlessly for big data.
iii) Auto Data Modeling: We leverage sophisticated algorithms to automatically create data models from disparate data sources and dynamically update the same based on changing sources. This replaces the huge cost and time spent in creating and managing data warehouses manually.
DataRPM can get any organization live with data discovery the fastest way and most cost-effective way.
AIM: Please brief us about the size of your analytics division and what is hierarchal alignment, both depth and breadth.
RP: We don’t have a separate analytics division but everyone in our organization does data analysis using our product, DataRPM, on a regular basis. We are a company that delivers analytics platform and we believe that effectiveness with analytics can only be achieved when everyone in the organization has a data-first mindset and uses analytics naturally like using email for their work.
AIM: What are the next steps/ road ahead for analytics at your organizations?
RP: The next step for analytics at our organization is predictive analytics. Currently we have mastered data discovery and descriptive analytics. The next frontier is the ability to leverage data to predictive the future course of action and the ability to do that naturally by everyone without requiring one to be a trained data scientist.
AIM: What are a few things that organizations should be doing with their analytics efforts that most don’t do today?
RP: In many organizations analytics generally exists as a silo – a separate department with only a few people who analyze data. This is an ineffective approach and one of the reasons that most analytics efforts of organizations fail to yield desired results.
The true power of analytics can only be unlocked if data-first mindset is baked into the entire organization. There shouldn’t be silos but rather every individual in an organization should be analyzing data on a regular basis to support their work and decisions.
Analytics should become as ubiquitous as email and shouldn’t just be an after-thought.
AIM: What are the most significant challenges you face being in the forefront of analytics space?
RP: The biggest problem I would say is the Data-Waste. The world is generating tens of petabytes of data on a daily basis. Every organization has access to this wealth of data today both in proprietary systems, 3rd party services and the web. But unfortunately only 10% or less of this data is even analyzed, because most organizations are still struggling with the age-old manual approaches to data warehousing, canned reports and dashboards. This means that most organizations are missing out big time on the opportunity that proper data analysis, by looking at the entire spectrum of data and not just canned things, can deliver in business growth and efficiency. Also data analysis is an after-thought and silo-ed into a department.
Preventing this Data-Waste and changing the analytics-silo mindset to a data-first one is a significant challenge.
AIM: How did you start your career in analytics?
RP: I started my career in analytics as a data scientist in Yahoo, analyzing the big data sets of Yahoo and coming up with strategic insights. This was even before “big data” and “data science” were globally recognized terminologies. These projects influenced several products & business strategies and led to tens of millions of dollars of positive revenue impact.
AIM: What do you suggest to new graduates aspiring to get into analytics space?
RP: The key traits to be successful with analytics are to always question everything, hypothesize and try to validate with data. Persistence in pursuit of multiple analysis paths, ability to look at the same problem from different angles and corroborate conclusions goes a long way to powerful insights. Also one should stay abreast of new technologies and methodologies, as the analytics space is moving really fast into amazing new frontiers. Clinging on to aging technologies or methodologies causes one to become obsolete fast.
AIM: What kind of knowledge worker do you recruit and what is the selection methodology? What skill sets do you look at while recruiting in analytics?
RP: DataRPM is at the forefront of innovation in the big data discovery space. We are pioneering a lot of technologies like the natural language question answering, computation search and auto data modeling for data discovery. As such we look for people who are passionate about inventing the future.
We evaluate on the ability to question and challenge the norm, structured and analytical thought process, data-first mindset, creativity and entrepreneurial spirit. In terms of technology we look for experts in machine learning, scalability and performance, big data, visualization among other areas.
AIM: How do you see Analytics evolving today in the industry as a whole? What are the most important contemporary trends that you see emerging in the Analytics space across the globe?
RP: The next huge revolution that is coming to the industry of analytics is the emergence of Smart Machines. We have already seen smart machines making the world better in consumer tech like Siri in iPhone, Google Now and others. Now smart machines are poised to revolutionize the enterprise tech world with IBM Watson already making big waves in healthcare knowledge discovery and startups like DataRPM in enterprise data discovery.
Smart machines changes the way analytics have been used in organizations. It removes the time and cost intensive manual efforts, learning curve and usability issues with Analytics making it available to all organizations globally and not just the top of the Fortune list. This is even more relevant in the world of big data. Ability to just ask questions in natural language to a virtual data analyst anytime and anywhere makes data analysis available to all individuals and not just the technical experts. Organizations don’t have to make significant parallel resource investment anymore to get started with analytics.
Cognitive computing technology enables the smart machines to do the heavy lifting, allowing the users to spend more time interpreting, understanding and finding insights within their data, the way it should have always been!
AIM: Anything else you wish to add?
RP: The importance of data analysis is not just relevant for only large organizations or tech companies. Any organization or any individual can repeat huge benefits by analyzing data the right way. DataRPM is committed to help bring this data democracy at a global level and make data analysis available at the fingertips of everyone. We are more than happy to talk, help, share and partner with organizations and individuals who want to participate in this data revolution.
Reach out to us at: http://datarpm.com