From invisible robots, to drones as data servers -- from virtual reality to driverless cars -- technology innovation is faster than ever, impacting us everywhere, broadening our knowledge, and newly augmenting processes and commerce. We'll now explore the ways that these technology innovations translate into business impacts, and how consumers and suppliers of services and goods can best prepare.
Listen to the podcast. Find it on iTunes. Get the mobile app. Read a full transcript or download a copy.
To learn more about the future of business innovation, we’re joined by two guests, Greg Williams, Deputy Editor of WIRED UK, and Alex Atzberger, President of SAP Ariba. The discussion is moderated by me, Dana Gardner, Principal Analyst at Interarbor Solutions.
Here are some excerpts:
Gardner: What are some of the major disrupting trends that you see in technology, and which of those do you think are going to be the most impactful for businesses?
Williams: Technology shifts tend to be almost things we don’t notice. They're not happening slowly any longer; they're happening quickly, and we're almost not seeing them.
We talk about something like robotics. Now, you can see all kinds of incredible things. You can see in Japan a robot caregiver that can lift elderly people out of their beds and can care for them in that way. You can see slightly sinister videos from Boston Dynamics of robot dogs running along that look pretty scary. But what most innovation looks like are things that we almost don’t notice are there.
For instance, and this is a boring example, an ATM is kind of a robot; a vacuum cleaner is; an elevator is. Those are things we don’t necessarily notice. They're not as dramatic as we think.
I should just caveat that and say that everything is moving very, very quickly right now. That's why it’s hard to make very clear predictions.
The other thing that’s important is this joining up of lots of different technologies. That’s the biggest trend that I see right now. We can talk about satellites and drones, which are effectively servers in the sky, or we can talk about autonomous mobility and augmented reality, but it’s all about connecting the dots.
One thing that's interesting now is the way that car manufacturers are all technology players. Every automotive manufacturer is figuring out that what they have is a computer on wheels. They have to figure out how, when people drive into a parking lot, they make an automatic payment via the vehicle. How can the vehicle know that people’s groceries are ready to be picked up at a certain point.
Although it’s nice to list robots and autonomous vehicles and other clear technological shifts, the thing that we're really seeing is the speeding up and this coming together, this joining and connecting of the dots. Basically, all are based on three things: ubiquitous computing, mobile technology, and the cloud. Those three things underpin pretty much everything that we're going to be talking about in the next 20 minutes.
Gardner: Alex, when I hear Greg, I'm thinking business networks, although people in the consumer space might not think of them as business networks. It’s the network effect, it’s intelligence shared, it’s linking things up and allowing the pace to increase and people to share knowledge and activities. What do you see as the crossover from the consumer space in the behaviors and culture of technology and then how does that translate to the business idea of a network?
Atzberger: I was recently in Dubai, and they have a Museum of the Future that they're launching this year. In the Museum of the Future, you can see what it would be like to be going to a doctor to get a new body part to jump higher or move faster. You look at these types of ideas, and the business embraces the same sort of idea. How can I augment my business to actually run smarter and be better? What are things on which I can augment myself to use data better?
Companies ask us, "Now that I'm connected to a network, how can I get data out of that network to improve my business processes and do things better?" That's what they basically call the augmented enterprise, to get augmented intelligence to that business.
Gardner: We're seeing different patterns, not only in adoption, but expectations. People are seeing a mobile device tied to a cloud that has deep learning capabilities, and feedback loops that are applying the data back and forth. People are becoming ready for the next move. They want the technology to guide them. And they also don't want to take the time to learn a process; it has to be intuitive to them.
So how do these human behavioral aspects of anticipating a proactive technological helping hand impact both us in our consumer space, as well as what we would expect in our business environment?
Simplicity is key
Williams: Simplicity is absolutely key to all technology. We have to think about the end user. The end user or the customer is always the most important thing in any kind of technology process.
Going back to what Alex was talking about in terms of artificial intelligence (AI), what it’s going to allow us to do is be a lot more predictive in terms of consumer behavior and customer behavior.
If you look at something like natural language processing now, some of the startups in that space who are working with automotive manufacturers, to go back to my previous example, they will look at trends on social media and elsewhere. They can look at import and export data maybe and they can look at those predictive trends and make predictions about General Motors, their sales in the next quarter.
From the sky, we can look at parking lots at malls like Target, Costco, and Walmart and we can make predictions about how the quarterly earnings report for Walmart or whatever is going to be pretty strong this quarter.
What we are looking at is this constant connecting of the dots, and to Alex’s point, this incredible accumulation of data. That’s the real tough thing for businesses right now. I don’t think there’s any business out there that doesn’t understand the value of data. This phrase "big data" is one that you'll hear at every single conference, but how can we possibly parse value out of that? How can we use that data in a predictive way, rather than as a lagging indicator?
Most businesses have used data as a historical indicator. So, it's looking at sales reports or whatever other data is important within your organization. How we can use all those external factors is going to become increasingly important for businesses. Can we see how our competitors are doing by looking at the job postings that they have maybe? How can we see what their next move is in terms of manufacturing by looking at their import/export data? Can we look at the amount of money they're spending on Google AdWords and see what keywords they're spending money on?
As I said previously, it’s about connecting of the dots and bringing this information together, and also figuring it out, having someone within your organization who's not going to get overwhelmed by this data, but is curating it, and knows what’s important and what’s not important to the enterprise, because a lot of it isn’t.
Gardner: User experience plays a huge role in how we can consume and make good on this technology, on this data, on this analysis. What Greg said about simplicity can be deceiving. It might seem simple to the end user, but an awful lot has to happen in order for that effect to take place.
So Alex, one of the interesting things I've seen with SAP Ariba recently is this notion of Guided Buying. I love that word "guided," because you're anticipating the user, heading them off on complexity, but what does it take behind-the-scenes to actually make that happen?
Atzberger: There’s a whole lot that it takes to get this going. The idea of Guided Buying was always that simplicity that all customers are asking us for. It’s really about how I make the user feel empowered and give the power to the user, but at the same time, embed intelligence in the software.
In our cloud applications, we thought through every step of the process, starting with monitoring how users were behaving with the system. So it’s a design thinking approach, and it starts off with deep empathy with the user. That’s the first point.
The second point is understanding what the business actually wants to accomplish, because the business actually runs a business. They have rules, methodologies, things that they want to achieve.
I was with one CPO who told me, "Alex, I look at this beautiful software, but you're making it too easy to buy. I don’t want people to just go out and buy stuff." That’s absolutely a good point, but what we're doing is embedding the logic of the buying in the enterprise into Guided Buying. That’s the difference between B2C and B2B.
In B2C you can have that beautiful experience. You just want to make the experience so seamless that you drive commerce. In B2B, you want to guide the commerce, to be more relevant and fit your company goals. That requires a slightly different approach to how you solve that problem. We're obviously deeply committed to solving that problem in the context of giving users as much freedom and choice as possible while enabling the business to achieve their goals.
Williams: Alex used a really great phrase and it’s one that we actually had a discussion about in the office, which is the importance of design thinking within organizations. When you think about software or any technology, the user experience is your brand. So, it’s the people experiencing it.
Pretty much in every organization now, the "design brief" is a really important part of the organization. Maybe designers need to be brought in, whether they're software designers or in the B2C space, UX designers. They need to have a seat at the top table these days, because they're such an integral part of defining any kind of brand.
Atzberger: We hire a lot of designers into SAP Ariba, but interestingly, a lot of the engineers come and say they need to think about design as well. So, it’s not like design is still a separate department. At one point, design becomes part of what we call a scrum team that basically builds the software, and an engineer should have a point of view as well in terms of what is good design.
You could argue that there are some sites that don’t necessarily look pretty, but they're really easy to use. So, it’s not just about the visualization and the fonts, etc.; it’s about also how many clicks and the logic behind it. That’s where product people want to be product people. They don’t want to just be engineers or just designers.
Gardner: I suppose another important element to this is not only that user experience where one-size-fits-all, but a user experience where customization is brought to bear, and because of the technology, because of the intelligence, access to a cloud infrastructure, we can do that. There are examples of customization at the individual worker level, where role-based and policy-based approaches can do that.
We're also seeing with the SAP Ariba cloud, you're bringing master data, vendor data, for example, into the cloud, cleansing it, making it usable, but still keeping it germane to that particular company, so that this isn’t just a business app for everyone. Let’s delve a little bit into this idea of customization specifically to a company and then even down to the individual user. How is that so important now in business applications, Alex?
Atzberger: The premise of the cloud was always speed. What you gave up for the speed was the ability to customize, especially in enterprise systems. What we're now saying is that you can have a level of individuality and things that are important to you, either through configuration or through extending the platform that you're on.
That’s the power of the technology that comes to bear when you look at platforms today. If you look at Amazon Web Services or what SAP is doing with the HANA Cloud Platform, it’s essential, because it gives the capabilities to companies to actually customize further.
At the same time, we have a concept of the private and the public persona, because at the end of the day, there is some data that’s private to a company and then there's data that's publicly shared. We need to be very sensitive of what data is relevant and in what context.
Gardner: Greg, one of the areas where business can get out in front of the technology curve is this idea of customization and anticipatory or predictive analytics’ benefits. It seems that we're only scratching the surface here. When I go on Netflix, they still can’t pick shows that I really want to watch. When I go to Amazon and they have My Box or My Stuff, it's really just things I already bought with a little bit of augmentation.
If we can take this to the full potential of customization, and I think businesses can because they have access to the data and they can be policy-based and in probably a better way than a mass consumer environment could, what’s the potential here, when the machines can really start getting us customization, predictive analytics, and apply that to how we get productive in our business sense? It strikes me as something quite significant?
Williams: Yeah, it is. I was talking to someone in a California startup who is developing a sales tool. This person worked for many years in a very large enterprise that builds CRM software. His new business is very interesting because he's trying to do what you described. He's trying to do it almost being a search engine for the entire business Internet. I know this has to be verified, but their claim is that they are much more efficient than regular salespeople.
Say you're trying to sell your software product into a telco. You'll spend a lot of time learning about the person who purchases, those services. You'll go to conferences, read blogs, develop networks, and put a lot of effort into this process.
His startup suggests that they'll be able to not only identify the companies that you're able to sell into, but they'll be able to identify the actual individuals. It will become a lot more detailed in terms of this is what they're interested in and this is what they're not interested in. This is the conference that they've been to. Increasingly, we'll have more-and-more intelligence on people, their habits, their preferences, their interests, and their connections.
Take your Netflix example. Netflix moves simply from being a content delivery service to being a creative business by looking at this kind of Venn diagram of its users interests. They saw that there was a sweet spot that overlapped with Kevin Spacey, David Fincher, and the original House of Cards from the UK. They saw that there’s this huge amount of people who love those three things. They said, "Great. Let's commission this series."
Every time that users interact with the service, it's helping to improve it. Netflix knows what you watch, when you watch it, where you stop, where you don’t finish, where you fast-forward, and where you rewind. So, they're collecting huge amounts of data that can be used not just to understand consumer behavior, but also to get insights that can be used for decisions around content.
Gardner: So, Alex, translate this to the business environment, the business network that your company is aligned with can be the determiner of how effective this new trend towards customization, anticipation and being more of a science than an art for sales for example that Greg mentioned is. This to me says the right network with the right information is a crucial decision for you. How does that work in terms of companies differentiating themselves based on who they work with in their ecosystem?
Atzberger: First of all, any company that engages in a network and then captures the data to make better business decisions is already on that journey. If you look at the social networks today, if you like three things on Facebook, Facebook knows more about you than your best friend. If you like more than 10 things, Facebook knows more about you than your spouse. That’s the logic, and the same happens in business networks as well.
What we see a lot is that businesses are connecting to networks to conduct global business, to find new market opportunities, and become much better at actually mining and understanding that data to become more pointed in terms of what solutions they actually want to provide to the market.
But we're still at the very beginning of this trend. We're working with companies on enabling Data as a Service, where they leverage the data itself to create more insight into their business, pursue better business opportunities, change their product offering actually and innovate with their supplier base. If we do that, we're impacting real change, and that's absolutely feasible today, but we're still early on.
Gardner: Any examples, Alex, of companies that really get this and that are showing some demonstrable benefits, that are really tagging on innovation to what their businesses were traditionally, but taking it in a new direction based on some of these technological benefits that we’ve talked about -- poster children for innovation perhaps.
Atzberger: When I think about poster children for innovation, I think about companies that are really looking to the network as infrastructure. What are the other things I can do through this network in order to change my business or add new capabilities?
What I love is when we have customers who talk about the fact that they can actually change their industry. Or their entire supply chain. We have a one high-tech manufacturer who thinks about how they can get demand signals much faster to their supplier base so they can actually impact the end customer. I like that thinking a lot.
Gardner: Greg, last thoughts on what's to come, how technology and business combine to transform how we get things done and perhaps even improve our quality of life?
Solving big problems
Williams: That’s obviously the fundamental end result, one hopes, of all technological change -- that people have better lives and we solve big problems. Looking forward, we're going to see, as Alex has been describing, a real joining of the dots. There aren’t necessarily going to be things that are dramatic, but we're going to see increasing amounts of AI, for instance, offering us insights in industries such as healthcare that only machines are capable of determining because of the sheer volume of data that they can analyze.
I was talking to a guy who worked in the security industry recently. They do a lot of work for the Pentagon. He was telling me that they did an analysis of tweets about ISIL during one week in August last year and they noticed that most of them were about security or the security situation in various parts of Northern Iraq and Syria, account promotion, religion, and strategic updates, but then they came across an outlier that they never noticed before.
The official ISIS accounts were re-tweeting any mention of female fighters or women in ISIL -- there was clearly a big push by ISIL to recruit women. What happens? Six weeks later, we had the first female suicide bomber in Europe in Paris. Now, those things probably are not linked, but I think we're able to see things in the data now that we have never been able to see before and I think they increasingly will be putting those things to use.
Listen to the podcast. Find it on iTunes. Get the mobile app. Read a full transcript or download a copy. Sponsor: SAP Ariba.
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