The team at Aunalytics saw a need for Big Data software that could deliver an end-to-end approach to data science. To discover what this approach was all about, we caught up with Nitesh Chawla, Co-Founder of the company.
insideBIGDATA: Nitesh, please tell me a little bit about the services that the Aunalytics platform provides.
Nitesh Chawla: Our data science platform, Aunsight, helps enable the entire data science pipeline, beginning with the integration of data from disparate sources, and finishing with the delivery of rich visualizations. It takes away the worries of data aggregation and integration,computational constructs, scheduling, monitoring, etc. It provides a platform for data scientists to develop a workflow – integrate relevant data, collaborate, bring business insight, develop and implement algorithms, experiment and validate, and deliver results via rich visualizations or even something as simple as a CSV file.
insideBIGDATA: What aspect of data science do you see the software being best used?
Nitesh Chawla: Data scientists need to be able to focus on answering the big questions and uncovering insights that impact day-to-day businesses. The platform provides an environment for data scientists to explore all types of data, integrate relevant data, develop new algorithms or implementing existing algorithms, validate or discover hypotheses, conduct experiments, interpret results, and deliver actionable insights. It allows data scientists to be curious and do what they do best – explore big questions and deliver actionable insights, and not be weighed down by anything else.
insideBIGDATA: So much of the attention on Big Data is centered on enterprise, why ultimately does the scientific community need Big Data analytics?
Nitesh Chawla: The scientific community has been using “Big Data” for research for many decades. It was not called Big Data, it was just data, tons of it, in disparate forms. There was “big data” in scientific disciplines such as astronomy, bioinformatics, etc. In fact, my PhD dissertation in 2002 focused on developing learning algorithms for massive data. Recently, the business community has realized the competitive edge available from appropriately leveraging data, whether transactional, social, or unstructured, and introducing tools and a mindset to leverage data for competitive edge.
insideBIGDATA: What does the future hold for science-based analytics and what role will Aunalytics play in this?
Nitesh Chawla: Aunalytics’ driving philosophy is that the creation of data-evidence based decision making is a collaborative process between business intuition and data science. There is an interplay of generating and validating hypotheses, discovering new models and testable hypotheses, and conducting experiments by asking “What if?”. Aunalytics is also developing algorithms (and delivering them as Apps) that can learn models from disparate data, develop an individual-centric profile, and deliver predictive analytics, such as predicting the propensity of an individual to churn. Aunalytics will continue to work with clients to achieve more data-driven and evidence-based decision-making. Aunsight provides the foundation for such analytics.
insideBIGDATA: What will be your goal at the 2014 Strata Conference in Santa Clara?
Nitesh Chawla: Our goal at the 2014 Strata Conference is to unveil Aunsight, network with the data science community, and learn from our colleagues in the industry.
insideBIGDATA: Do you have any recent success stories that you can tell us about?
Nitesh Chawla: We have been working with several clients to deliver analytics through Aunsight. As case studies become available we will keep you informed. Industries in which we are currently working include: banking; financial / investment; oncology; and blood donation.