Analytics India Magazine: What are some of the main tenets (philosophies, goals, attributes) of analytics approach and policies at your organization?
Suresh Shankar: Crayon Data aims to simplify the world of big data. Our mission is to make data simpler, more accessible and contextual for our clients. Figuratively, we add colour to data, make data sing and dance for our users. Our vision is symbolized by 7 tangrams in basic colors, which constitute Crayon logo, and our core vision is “Big Data. Made Simple.”
We recently launched a first of its kind content aggregator website in big data called BigData-MadeSimple (https://www.bigdata-madesimple.com/), this is because we want Crayon Data to become a go-to space for everything Big Data; not just creating products and applications, but nurturing and developing ideas and thought leadership. We are in the process of building a business and technology platform that democratizes the use of big data for the average business and consumer.
AIM: What is your approach to face the challenge of meeting the needs of so many clients across vast geographies with limited resources?
SS: The problem most businesses face in providing solutions across multiple verticals and geographies, is the migration and portability of vast amounts of data. We try to solve this with a multi-tiered platform based approach to store & analyze the data.
This architecture is highly scalable with the data and algorithms residing at platform level and the client facing interfaces at client/application level. This approach creates flexibility for us to jump across territories without wasting efforts in migration.
We build solutions on this underlying platform that are customized for the business needs of the enterprises. A classic example is how we are building multiple applications for various verticals above a common simpler choices platform.
AIM: What are the key differentiators in your analytical solutions?
SS: We have built a big data platform that vastly expands the data sets beyond enterprise data. By connecting the internal data of a client with the world of data outside, including social data, we are able to provide richer and more intelligent data led decisions for our clients.
Our data ingestion and curation engine (called WhiteBox) is the foundation on which we offer our unique “Choice Engine”. This engine (SimplerChoices) seeks to simplify the choices available for both clients’ internal decisions (customer lifecycle management, risk management, pricing, channel management, marketing and sales optimization) and for consumers.
Simpler Choices represents the next wave of analytics which allows a predictive, algorithm, machine-learning led approach to decision making, rather than a historical, people-led model. Over time, this improves confidence in decisions, saves time and energy for managers, and vastly improves the cost efficiency of analytics.
Additionally, we have developed an innovative visualization element that removes the clutter in analysis and presents business users ideas they can act upon immediately.
AIM: Please brief us about the size of your analytics division and what is hierarchal alignment, both depth and breadth?
SS: Crayon Data has a team of 60 professionals distributed between Singapore and Chennai in India. The team consists of data scientists, designing team and the coding team. There is also a management team and a sales and marketing team.
The team structure is based on the skill sets that each of the three founders brings in the organization. I founded the company in 2012, along with my co-founder Srikant Sastri and Vijay Kumar Ivaturi (IVK). IVK serves as the Chief technology advisor and Srikant provides strategic leadership. At Crayon, we do not follow hierarchal alignment as such; we encourage our employees to come up with innovative ideas irrespective of hierarchy.
AIM: What are the next steps/ road ahead for analytics at your organization?
SS: The primary focus for us now is to bring the power of Amazon/Netflix in terms of personalization and recommending choices to enterprises. We plan to accomplish this by aggregating and curating vast amount of external data and combining it with the internal assets at the enterprise. With this, we will be able to predict outcomes to simplify the decision making process of organizations.
AIM: What are a few things that organizations should be doing with their analytics efforts that most don’t do today?
SS: Most of today’s enterprises rely heavily on their internal data assets to make their decisions. The future of decision making depends on not only your internal data, but also on the behavior/interests of their consumers outside the business environment. They should be able to combine their customer preference with the structured internal data to make more informed decisions.
Enterprises should focus on predicting outcomes rather than constraining themselves with KPIs & SLAs. This brings a result oriented approach to their analytics efforts.
AIM: What are the most significant challenges you face being in the forefront of analytics space?
SS: There is a massive amount of structured and unstructured, online and offline data generated across various industries in the form of customer information, transactions, loyalty information, reviews and more. Most businesses today are ill-equipped to handle this tsunami of information. Data is the biggest challenge and opportunity to innovate and implement new business models that can leverage this vast amount of data to create value for businesses and consumers. Crayon believes in leveraging current technologies, challenging the existing business models of vendors and hence changing the way organizations handle the increasing complexity of using analytics in the new “big data” world. [To read the complete interview, click here]