Bilafer’s BHAG: Agile analytics and the 3 paths to deployment
When I talk about analytics, I often use the word ‘journey’. I also like to talk about ‘getting a duck in the water’ meaning, just picking a project and getting started. I think these two metaphors best capture the experience of successful analytics deployments.
This is what I see again and again: Customers start by doing something limited in scope, usually to solve a specific business problem, and they get their analytics “sea legs.” The journey continues, and they begin to use analytics in ways that weren’t even on their radar when they started out. They only became possible because of experience gained on the journey and the ability to see things from a new vantage point.
As with any journey, your experience will depend on your starting point. If you set out for Guangzhou on the Silk Road, obviously your journey will be very different if you depart from Rome or from Java.
So, when I hear people say that companies in APJ are not really doing advanced analytics, I think that’s only partially true. What’s more true is that the evolution of how analytics have been and will be adopted and used looks different here. That means we need to think about it differently. Painting with a broad brush doesn’t create an accurate picture.
One of the things we’re seeing with analytics here is that deployments are becoming very customized and unique to each company. We also have a lot of analytics customers here who aren’t running any other SAP solutions.
Remember too, we’re talking about a region that is incredibly diverse in terms of economic development and technology infrastructure. A lot of companies here don’t have any kind of legacy infrastructure, so they can go straight to the new architecture with ERP and analytics running on HANA, and we’re seeing that—companies just buy the whole thing at once and they have the latest and greatest of everything. So, they might not be doing advanced analytics now, but in that kind of situation they can start almost overnight.
The cool thing about SAP analytics is it can be deployed on the web, behind a firewall, in the cloud or mostly mobile, so it can look a lot of different ways. So, trying to describe the journey to analytics success in APJ is lot like that story of the blind men trying to describe the elephant—it very much depends on your perspective. But those journeys are taking place.
The journey here is going to be a lot shorter than it has been elsewhere—three to five years, not ten, and at a high level, it’s going to unfold along one of three distinct paths.
The first one is, we’re selling to the business to solve very specific problems, often around things like mobility for a distributed workforce, with the goal of empowering workers to make fact-based decisions.
On this path, the business calls out the data that they need to improve the way they do their job, and they’re not worried about whether it’s applicable to marketing or HR or finance. They just need it to solve their problem.
Kimberly Clarke Asia-Pacific Japan is a great example of that. They started with a small deployment to get everything on a mobile device so their remote sales teams could have relevant information in their hands at the point of customer contact. Now their use of analytics has grown to where they’re doing analytics on their use of analytics, so they can get it so every question can be answered with 1-2 clicks.
Asian Paints is another example. At $1.7 billion in annual revenues, it is India’s largest paint company. They do both manufacturing and retail. They were already an SAP shop and have used analytics to put everything, and I mean everything on a mobile device—sales figures, product information, HR—employees have just one place to go to look for anything they want to know, which has freed up time to be more strategic and respond faster to the market.
The second path to analytics is through IT, though not exactly in the way we’ve seen it in the past. It depends on what kind of business it is and how they see information. If it really is an information-driven culture across the organization, the deployment can start with IT and grow from there.
If it’s not, and they see information as a more of a luxury, then it’s typically driven by the business. They roll out something they want, other people start wanting it too and it grows beyond their ability to scale it so IT steps in.
That happened here at SAP. When we first rolled out Explorer, it was really for the sales team. When people saw what it could do, it started to become institutionalized so now it isn’t just the sales team, it’s marketing, services and the support organization.
The moment it went from a line business type of buy to more of an enterprise deployment is when IT stepped in to take ownership and take it out across the enterprise.
When IT is leading the deployment, there are usually a couple of drivers. Obviously there is a lot of business value in having one version of the truth—one data set that everybody can access and use to make decisions, and then also making sure that there is consistent visualization available to support that.
One of our use cases that illustrates that is DuluxGroup, an Australia-based maker of paints, cleaners and other home and garden products. After breaking off from another larger company, they needed to get to one version of the truth about how the new company would look and perform, and to be able to use that information to make quicker decisions about the business. This was Gartner’s Asia-Pacific Japan BI deployment of the year last year.
Iluka Resources is another Aussie company, a mining company specializing in mineral sands. For them, analytics helped them solve critical challenges around having a single source of the truth around HR, environmental and safety information, and easy visualizations to share that information across the company.
The third path we see is companies that want analytics to empower the end customer, so it’s really being driven by the needs of the customer’s customer. PG&E, a big utility in California, has a reporting solution that lets their customers model their energy consumption by different criteria, so they can see how they can change their consumption habits to save money. The largest utility in Singapore is going to be rolling out a very similar SAP solution soon.
The American NBA (National Basketball Association) collects a lot of data, and they’ve used an SAP solution to make some of that available to their fan base to increase their engagement with the players and the teams and the game.
The same idea is being applied to fashion retail, with an app we’ve developed called My Runway. It has two parts to it. The first one is consumer app that you can download on your mobile devices to follow your favorite brands. Users can find out when there is a promotion or new product or design from their favorite brand.
The brand gets a whole new set of information about consumer sentiment, combined with demographic information that they can then use for anything from measuring the effectiveness of a promotion to deciding where to open a store.
Every single one of these is an SAP BusinessObjects deployment. It can be cloud or not, it can be mobile or desktop, it can include HANA but it doesn’t have to. It might be IT that’s driving it, but it could just as easily be marketing or sales or another business unit. It might be something they need internally, or it might be for the purpose of increasing customer intimacy. The use cases are very diverse.
So are the audiences. As I’ve described, there are really three different meta audiences—the business, IT and the end consumer, but underneath that there are as many flavors of analytics as there are of Yu Sheng or curry. Some are more mobility-oriented, some are based around visualization, some around achieving a single source of truth. Some are a combination of those three. That’s why we call it agile analytics, because it really is suited to a huge variety of purposes and there is no one path and no one journey.
The main point is, those journeys are under way.
This article previously appeared on Business Intelligence and kurtbilafer.com.