In “The Corporate Challenge of Big Data” I attempt to shine a light on the successes, problems, topography and future of Big Data which is becoming a major player in the IT industry. The challenge is how do companies particularly large corporations transform to embrace Big Data without getting a bloody nose. In a recent survey Deloittes recognised CFO’s are returning or starting to return Cash to the top of the Corporate agenda. “Therefore, the efficient use of resources, especially working capital, is front of mind for most CFOs.” (source: Deloittes working capital report May 2012, Andrew Harris). So investment in Big Data has to have a return on investment and quickly.
Business concerns itself with process and for as long as I can remember treats data as an “IT thing” and IT in turn are equally happy to take data off the agenda of Business. So Big Data’s name suggests a bigger problem so corporate Business in an attempt to reduce costs puts off the challenge hoping it is a fad or offloads the challenge to their overstretched IT services division. None of this is helpful, the name Big Data has hidden depth. I argue here until Big Data just becomes data and is part of the business process, companies are going to struggle. It could also go the other way if companies set up a Big Data department as part of IT and let it sink or swim with costly and dire consequences. There needs to be a joining up of the Business and IT dots to galvanise data into the fabric of the business.
Many large corporations have invested heavily in IT Architecture, Infrastructure and Governance over the years which is now becoming costly to support and less agile than younger alternatives. It is a brave CIO that stands in front of the Board and says “forget all you know about IT services, we need to move into EaaS (everything as a service) ditch the IT department team that supports Architecture and the one that supports Procurement and the one that supports Software installation and upgrades. We need to bring in third-party suppliers to look after our data and store it. We also need to invest in PhD Data Scientists whose services are costly because there are so few of them.” However it is an even braver one that says, “we need to do all this in parallel with the IT services we are currently paying for until we can switch off our old IT.” In truth it isn’t clear whether traditional IT should persist in parallel. Therein lies The Corporate Challenge of Big Data.
Start-ups on the other hand now live in an age where they can start small with Big Data and Cloud services which scale as the company grows. This is hugely important. There is a case study of a snacks company in the UK that started small in the 90’s recession and tried entering the market through Pubs and Bars hoping to sell in Autumn when (as they thought) these outlets would be stocking up for Christmas. In fact these prospects had ordered their stock in July. Undaunted and so sure of their refined snack proposition the company took orders from a few clients for the post-Christmas and New year period. Success followed and the order book started to grow, orders came flooding in, they embarked on a TV advertising campaign which was costly but they felt this could be their next step to success as the orders really started to pick up. However now, their IT processing capacity was rapidly hitting its limit and they were hit by a double whammy increased orders and having to pay the TV production company for their advert that generated more orders. They literally did not know if they had the cash to pay the TV company and if they did how would it affect their cashflow, they took a chance and paid the bill. They then had to pay for new IT systems. Luckily for them they surmounted all these problems and survived.
This case study is all about process not data or Big data. Knowing the market and having back office processes in place to know instantly status of orders and cashflow. Today thanks to the likes of Salesforce, Amazon Web Services and Hadoop, a teenager in their bedroom could operate a company that could do all the above and do their own market research and grow to a multi-billion multinational household name within years not tens of years. Here we have another corporate challenge – agile competition that doesn’t exist yet.
Traditionally transactional behaviour has been the key indicator of the value of a customer. If they pay on time and buy multiple products regularly then these prime customers get the prime service. Corporations can “watch” introspectively their customers and try to keep the good ones. Companies do this by watching their churn figures customers jumping ship. If someone complains directly to a company against one of their products or a service then there is an opportunity to evaluate the complaint and compensate that customer also they can adjust the product or service accordingly. If someone complains on Twitter, Facebook or other sites and starts to trend then it is the brand that takes the hit and that can be damaging. However, the promise of Big Data allows anyone to “listen” to what is being said in the public domain, good or bad.
So how do corporations bridge that gap transform without boiling the ocean. Here I offer my 10 point starter plan:-
1. Acknowledge change is inevitable and employ a brave CIO who can get the message across of how that change can be made to happen. Board members need to be knowledgeable about their accountability, to formulate strategy delivery and to explain delivery roadmaps backed up with empirical evidence that is current, accurate and up to date. All this to be at their fingertips where ever they happen to be. C-level buy in to this is crucial.
2. Gain confidence in using Cloud computing services where they can add value but not increase risk. Consider archiving, email and CRM.
3. Employ a young entrepreneurial company or interns to find out what is being said about the Corporation and the competition in the Social Media world (good and bad), ask them to present their findings and recommendations.
4. Find a way to measure and benchmark every current process to implement a continual improvement programme.
5. Use a set percentage of any increased revenue or savings to fund more business improvement initiatives, so these become self funding.
6. Train displaced business and IT people with the aptitude to become Data Scientists.
7. Look to build on the benchmarking work to satisfy regulatory reporting, become Service oriented and work toward ISO 20000.
8. Expect and plan for a convergence of Big Data to become the norm and design or pay for Operational systems that lend themselves to Big Data analysis.
9. Explore the opportunities of API protocol as a revenue stream. Making available your aggregated data (therefore anonymous) to drive further business initiatives or for industry comparison and benchmarking.
10. Reduce to the minimum any silos of operational systems and analytical systems. Share and collaborate on as many IT systems design and resource usage as possible.