Products vs Services

Recently a few of us were discussing about what should be the focus of a new company in analytics space - products or services? The question was not purely theoretical as some members of the group are in process of launching a new analytics company. In this group, we had 3 kinds of people, technology experts, predictive analytics experts and someone who has built and sold analytics companies before. Naturally, I expected that we would have difference of opinion given the varied nature of experience and roles in the team. Surprisingly, though, we all felt strongly that for an analytics company to succeed pure services model will not work. Our reasons for same were as follows:

• Services only model is not easily scalable as its very people dependent. Requires lot of hiring, training etc. as new deals get signed.
• Analytics in today's world is not used by a privileged few. In fact, its power can be truly unleashed only when everyone in the enterprise can access it and use it to make more informed, timely decisions. And products that can be used by business teams are the only way to democratize analytics
• The sheer size and kind of data available today makes it dependent on technology (hence products) to not just store it but to even use it.
• Last, but not the least, even the services side of analytics has evolved over the years. I remember, when I joined analytics as a profession, predictive modelers were held in very high esteem. Statistical modeling was as much about art as it was about science. Over the last 15 years I have seen that high end human modelers are being replaced by model building software by FICO or KXEN or SAS. A few months back I was introduced to SAS Forecast Server and its forecasting module. I was impressed at how hundreds of good quality Time Series forecasts can be generated by a team of only one or two statisticians. I am not saying that we don't need modelers but the fact is that we don't need as many and that has been made possible only by analytic products.

One additional reason that was discussed was about long sales life cycle of services. I, personally, do not agree with this statement. I have seen instances of equally long lifecycle for selling products. In my experience there are two kinds of audiences and they both react very differently to products and services.

First is the IT organization of the enterprise - IT organization is actually very used to services model. They work on projects and it's a practice in IT world to hire contractors against project dollars. In addition, the coding portion of IT work renders itself a bit more easily to decoupling and hence the offshoring to cheaper locations like India becomes feasible.

Business teams on the other hand are very wary of services model. First, they usually get payroll dollars and not project dollars. Using payroll dollars they can only hire company staff and hiring contractors requires additional approvals. The project dollars that the business has are usually for one time needs (for instance, customer segmentation) and these get outsourced as a project to an established vendor in that niche space.

Even when the enterprise signs up for using offshore teams through captive centers or vendors, they are very concerned about lack of business knowledge of these teams. And they are usually right about it - it does take months to transfer the specifics of the business that are so important for providing appropriate analytic solutions.

For these very reasons, I have found business teams more open to buying a product to meet their specific need instead of investing time and effort in transferring business knowledge to another team. IT teams on the other hand are vary of buying analytic products unless they are sure that business would use these - else what's the point in adding another product to their list of things to maintain and support?

In summary, if your services or solutions are targeted at business users, its better to productize them to a certain extent and complement the same with service offerings for higher end predictive analytics that requires higher degree of customization.
 

Invest in change management, not just data and IT systems

During my analytics focused career I have had the fortune of working with multibillion dollar organizations looking to set up an analytics capability. While I served them in different capacities, a full time employee, a third party consultant, an independent consultant and while they all had very different business models, I found that they all had one thing in common – the need for change management as it is accompanied by anything as paradigm changing as Analytics. 

Analytics is viewed by most organizations in similar ways – top leadership recognizes this as the competitive advantage, as a tool to survive the ever changing economic realities and as something that has become part of any forward looking company that expects to hold its own in next 5-10 years. The analysts who have been MIS or financial analyses focused all their careers are much closer to reality when it comes to data availability and IT systems and process changes needed to implement analytics on a large scale and hence more than hesitant when they hear CXOs talking about analytics as need of the hour. Middle management is the one worst hit – one side they are under pressure from executive leadership to deliver results and other side they have a team that is worried about how analytics focus will change their lives at work.

While this scenario is true for most large scale changes, the challenge with analytics is that it is not successful unless it becomes part of day to day working style. Unless it’s adopted atall levels of the enterprise, one can never unlock its full potential and deliver returns on the large investments made in data and IT systems. And hence it becomes even more imperative that as organizations gear up to set up an enterprise wide analytics capability, they must recognize the change management needs that would accompany it. 

Just to put the need for change management in perspective, I would like to share an example from my experience. A multi-billion dollar MNC that uses innovation in its products as the competitive edge, decided that it needs to adopt analytics in order to stay relevant as newer younger companies were coming up with equally innovative product range. Over last few years, significant investments were done in areas of data infrastructure and BI tools. There were monthly and quarterly reviews presided over by an executive leader with a highly analytical mindset. However, process owners across this business function chain kept saying that they didn’t have enough information to make right decisions at the right time. All this when there was a dedicated team churning and providing numbers to all levels of this business function at a regular frequency. So where exactly was the issue? 

The key issue was that no one was converting data into insights, the analysts were not analyzing data to provide information, they were primarily completing a number churning task using latest BI tools and sending across reams of printed excel reports to the process owners. While the organization had spent millions getting the data and tools in place, no one realized that there was no common vision or even common definition of analytics across the organization. And as a result, in spite of all investments and top leadership buy in, the organization was still in its infancy when measured on analytics maturity curve. 

In my personal experience, the toughest part has always been convincing the front line managers and existing analysts to not look at analytics as an unknown devil but as something that would make their lives easier and add tremendous value to business. And what it requires is nothing else but lot of dialogue and some patience and some time. As it happens with any new product or idea, there will be early adopters and once they are on board and they adopt analytics and deliver better results, sooner or later rest of the organization will follow suit.