Businesses supported by analytical decision making have many similarities to tandem biking. I assume most folks have a good sense of solo biking but a few would have tried tandem biking. Tandem Biking is a mutual experience. The team becomes more than the sum of its parts. However, tandem biking requires coordination between the stoker and the captain to get into a cadence of speed. To get optimal output, both need to communicate and synchronize on several fronts. The captain has the responsibility to control the bike – which includes balancing (whether stopped or in motion), steering, shifting, and braking it. Since the stoker cannot see the road directly ahead with the same clarity, the captain has a special responsibility to warn the stoker of the bumps ahead so that the stoker can brace for them.
The stoker is an equal participant – He serves as the engine. The stoker should be able to generate more power than he/she would on a single bike. The stoker needs to ensure smooth power especially when starting up and until the team gets into a manoeuvring speed. The stoker should keep in line with the centreline of the bicycle, and lean with it as it leans through corners.
Similarly, for companies, the speed at which the business (read: captain) needs to act and react to the market conditions and the speed at which insights come out of data (stokers ability to generate power) need to work in tandem. When insights are aligned to the changes in the market conditions, we have an ideal situation and likely a position of competitive advantage – whether it is about delivering new products into the market, targeting the customer base more effectively, pricing the products in the right band without losing market share or taking a hit on profitability. Over time, the speed at which business needs to act and react has gone up, primarily driven by increasing competition and changing customer demand. At the same time, the speed at which insights come out of data have also gone up, primarily driven by availability of richer data and better data mining tools and technologies. However, the speed at which business needs to act and react is higher than the speed of insights. At TEG, we view this gap as a position of competitive disadvantage. When you look under the covers, the inhibitors can be grouped into three broad buckets: data proliferation, shortage of good analytical talent, and constant dependence on analytics teams for insights.
We, at TEG, see it as an opportunity to help our clients increase the velocity of insights and bridge the gap to be in a position of competitive advantage. For this, TEG has invested in three key areas:
Meaningful ingestion of data from commonly used data sources – Ingestion of data is not a technology exercise where you connect to a source and perform a few data mapping operations to deliver the final data. Ingested data needs to be understood from a business context and aligned to the unique internal business operations – be it is the alignment of the reporting weeks, the alignment of calendars or whatever else is needed. Data may need to be rolled up or broken down. Each of these operations is not a simple arithmetic operation. A high level of proficiency in the domain is required to keep the business meaning intact
Global delivery – tapping into the unique capabilities of the resource pool available across the globe be it quantitative talent from India or Russia or domain expertise from countries like the US. It is an art to convert raw quantitative talent into analytical talent that understands the business and speaks only in the context of the business.
Self-serve analytics – on any analytics project, one ends up spending a good portion of their time collecting, cleansing, integrating and harmonizing data. In our own estimate, the effort can be upwards of 50-60% of the total effort. The harmonized data assets can be re-used for several last mile analysis which is nirvana for a business user if it can be done. At TEG, we have built a cloud based analytics platform enabled by a combination of best of breed technologies to deliver the last mile experience.
The goal is to allow a business team aka the captain in tandem bilking, to be able to communicate effectively with their stoker, the analytics service provider, become agile in handling varying track conditions and reach a state of cadence to take on market challenges, seize new opportunities, and continue to improve
Imagine having a primary equipment break down at the peak of your work schedule. Imagine, if you had known it would breakdown and had got it fixed well before peak time. Imagine……….
Well, this is what one of our clients came to us with. It was peak season for their clients across the world. Every year, they would have to deal with breakdowns and part replacements, for their vehicles which were intensively used during the harvest season. Though they were able to capture real time data (which was captured via telemetry systems and stored in a SAP HANA big data system), they needed to figure out a way to inform the customers about predictive maintenance. The client wanted to give an advance warning of critical breakdowns. This way, our clients too would be prepared with spare parts at the right time. And if they could provide this information to their customers, they were sure to exceed vehicle sales in the next year.
Easy isn’t it? Except that our customer has over a million vehicles and a lot of data around vehicle details like the make, mileage, intensity of usage etc. Current mechanical data is sent real-time using telemetry systems . Our challenge? For one, weather data such as temperature and rainfall could impact breakdowns. And we had to find out which vehicle was going to breakdown or which one was going to need a spare part before the actual breakdown happened during harvest season. All the data was stored in a state of the art SAP HANA big data server. Our client has been in business for over 100 years and has been capturing data for quite some time.
When we sat down as a team for our first meeting, we had very little idea what we were going to do. But we, the data fanatics (as we like to call ourselves) ate, slept and dreamt data for the next few months. A couple of weeks later, and after dozens of empty coffee cups, we were ready to showcase our solution.
We built a decision tree model in R , integrated it with the predictive analytics library of SAP HANA , and developed a scoring equation , which spit out the probability of breakdown for each vehicle in real time. Voila! We had hit the bull’s eye! Our prediction was 90% accurate (it was about 35% before we started).
This was the first time ever that anyone had been able to take a humongous data for the farming sector and turn it into something that would eventually benefit the client in a big way (by that we mean increase sales $$$). The project was a Big success for our clients, but more importantly we felt really proud at having done something that no one else had attempted before.