Kathy Gibson at Saphila 2014 – Disruption can be swift and dynamic in any industry – some of the big players of yesterday don’t even exist anymore as new players usurped their markets.
The companies that are successful and have disrupted their industries are not just innovative, they provide great service.
And they provide very high value services via apps. Importantly, they are all realtime.
“This is the way moving forward, if you provide these three things, you will be successful,” says Snehanshu Shah, global vice-president SAPA/HANA.
The new inputs for the innovation factory, Shah says, are as follows:
* The Internet of things – every time a customer interacts, you need to do something meaningful with that information;
* Big data – how you work with masses of data;
* Cloud computing – none of the successful companies owns their own hardware;
* Mobile – all of these services are available to anyone anywhere.
Where we are today, there are an infinite number of things – and companies can harness them for great opportunity.
“This all boils down to big data, which means many things to many people,” he says.
“However, it is really simple. We have enormous compute power; and to this we are attaching lots of information or different types of information. But this is always rich information. Big data takes different types of information and brings it together and doing interesting things.”
Data science is also important, Shah says. So while BI tools tell us how the business did in the past, data science will tell us what we need to do in the future.
“Data science needs you will have new types of signals,” he says. This ranges from brand sentiment information from social media to predictive maintenance based on information from machines.
“Think about the new signals you will have. You need to start thinking about how loyal are your customers; how satisfied are your employees?”
What are the main big data use cases?
“Number one is about increasing sales; the next is to increase the efficiency of your supply chain,” Shah says.
“Look at your supply chain, at your sales processes, there are nuggets in there. I guarantee every company has lots of information – it’s about taking this ‘dark data’ and lighting it up.”
The companies that are seeing increased sales, more efficient supply chains and lower costs are those that are finding the gaps and operating in realtime.
The impact of big data is huge, Shah says. It offers $300-billion in potential annual value to the US healthcare system; and E250-billion in potential annual value to Europe’s public sector administration.
“There is a mind-set difference you have to get to,” he adds. “You have to think about what you don’t know; what you need to predict.”
There are “known knowns”, “known unknowns” and “unknown unknowns,” Shah points out.
In addition, there are often correlations that may not make sense – but companies should still take advantage of these despite now knowing why they occur, according to Shah.
Business intelligence reports give managers a wealth of information – but prescriptive analytics should be telling the company what is important within the information that they should be paying attention to.
“A great example of prescriptive analytics is eBay,” Shah says. With a massive inventory, eBay employs a team of 300 analysts works to keep track of what’s selling, what’s not selling and why. The company now automates a lot of this analysis so that the information is coming in in realtime and correlations can then be run.
“What they essentially did was make those 300 analysts way more production. Sales went up by 1,5%, which for eBay is significant.”
So companies need deep analytics, it must be accessible, simple and realtime.
“The challenge is that we have IT depth – complexity that has been built up over the decades; and we spend most of our time keeping the lights on, not letting IT serve us doing interesting things.
“One of my key messages it for companies to think about simplification. Big data is not about buying new technology; not buying new things.”
All companies have a relational database, Shah says. “To this you can add a few pieces like cost effective storage or massively parallel processing like Hadoop.”
A realtime engine is also required which, when put together with the structured data engine gives companies the ability to do predictive analytics.
The SAP structure has three pillars: the platform of HANA; science experts; and application to infuse insight into people and processes.
“The underpinning of all of this is our HANA in-memory platform,” says Shah. Currently, there are about 300 customers using HANA.
HANA is actually an engine for simplification, Shah says. It is the platform for all application, it supports any application and as a true platform it’s more than a database.
“I suggest that the best way to get started with HANA is in a way that you can see value almost immediately. The most successful HNA projects are about eight weeks in length,” Shah says.
He adds that the best way to get started are via three steps: an innovation sessions and use case; second to define how to implement it by looking at the company’s big data maturity; then you can figure out how to take the use case and make it live.
“Do it quickly, do it in the cloud and you can see if it has value. If it does you can bring it onsite.”
Key is to do enablement at the same time, Shah adds. “Bring the people on board. Some of the best big data scientists are business people and could become the bright stars of the company.”
Shah adds that SAP is adding value to Hadoop by automating a lot of the processes and making them simpler. “The other thing are doing it looking at what a large scale architecture looks like.”