Often people ask me, Why should a large company trust startups with their data and pay them for it? and I usually retort Why not? The way I see it, the entire start-up ecosystem allows companies to experiment with their data at a much lower cost. Let me elaborate.
Before the entire big data wave hit us, most companies didnt know how much data they could capture and retain and the power of this data. In the past month itself, our team at Sigmoid has come across more and more customers willing to experiment in big data. To improve upon their omni-channel experience, one retailer wanted to understand the products customers are searching for online and in a hyper personalized manner guide the customers to these products on the shelves.
To optimize their marketing budget, one e-commerce portal wanted to understand how their ad-words were performing in terms of reach to various demographics and how could they optimize their monthly spend while still focusing on their target segments. A major travel portal wanted to recognize in real time if a particular segment was under-performing or over-performing and why, instead of looking at daily aggregated statistics.
Any major company is capable enough to setup a team and go after any of the above-mentioned initiatives and it is usually the route of choice. However the risk is too high and time is of a premium. Firstly they will have to hire a team, set up a department, allocate a budget, buy additional infrastructure, and go through a bunch of processes for something that might look too ambitious to succeed.
Secondly even if this succeeds, the time spent in working and re-working on the implementation can mean that the opportunity is already lost. Contrast this to a big data start-up who would have an expertise in implementing similar projects, cost you much lower and give you a tangible result in the form of a Proof of Concept (POC) within a few weeks. Post this you are free to evaluate whether you want to continue, develop in-house or want to hire an external vendor.
To illustrate, a major American retailer was concerned their promotions were cannibalizing their own sales and wanted to see if they can create customer P&Ls instead of category P&Ls. Their current set-up consisted of a legacy data warehouse. The upgrade required for storing additional data and carrying out such analytics was turning out to be too expensive and they wanted to try out open source technologies.
Like all companies they faced the dilemma of make vs. buy. Initially they started off in-house but they soon ran into roadblocks including internal resource availability. Hiring full time employees for an experiment didnt seem a viable idea. After evaluating various options they finally decided to work on a small POC with Sigmoid to first validate the idea. This gave them two advantages; firstly their current team was not burdened with new technology; secondly they could now validate the idea before jumping on to a bigger investment.
We have been having similar discussions in the recent past with more and more companies who are willing to explore the potential of Big Data and open-source technologies. Often companies become too single minded in their operations, to think of new possibilities. A young motivated team is enough to start the ball rolling for something bigger. As more and more CIOs and leaders are looking to enforce a culture of data-driven decision making, we are excited about the times ahead.
