Data Analytics Integration in Organizations
- 2. Challenges organizations face in adopting advanced data analytics
- 3. The biggest challenge of making the transition from an information culture to a learning culture— from a culture that primarily relies on heuristics in decision making to a culture that is much more analytical and data-based and accepts the power of data and technology — is not the cost. Initially, it ends up being just imagination and inertia. What I’ve discovered in my last few years is that the power of fear to grow to think and act differently today is immense, and to ask questions today that we weren’t thinking about our positions beforehand. And it’s that shift in mindset — from an expert-based mindset to one that is much more fluid and learning-oriented, as compared to a fixed mindset— that I think is essential to every company’s sustainable health, big, small or medium. Murli Buluswar, Chief Technology Officer, AIG:
- 4. What we found challenging, and what I find challenging in my discussions with many of my peers, is finding the collection of tools that enable organizations to generate value efficiently through the process. I hear of individual wins in some projects, but creating a more unified environment where it’s completely incorporated is something I think we’re all grappling with, partially because it’s still very early days. We have been thinking about it in the past few years quite a bit. The technology is always changing. Also, the sources are still evolving. Ruben Sigala, Chief Analytics Officer, Caesars Entertainment
- 5. Data protection is one of the most significant problems and what’s shared and what isn’t shared. And my view on that is customers willing to share if it returns interest. Single way sharing will no longer float. So, how do we secure this knowledge? How do we leverage it and become a partner for our customers rather than just a seller for them? Zoher Karu, Vice President, Global Customer Optimization and Software, eBay:
- 6. Capturing impact from advanced data analytics
- 7. Ruben Sigala: That helps better inform the correct structure, the forums, and finally, it sets the more granular operational levels such as training, recruiting, and so on. It is essential to align yourself with how you are going to run the company and how you are going to communicate with the larger organization. And there it will fall in line with everything else. It is how we set out on our journey. Vince Campisi, Chief Information Officer, GE Software: One of the things we found was when we began and concentrated on a goal, it was a perfect way to generate value and get people excited about the opportunity rapidly. And it took us to places we didn’t plan to be going before. Then we can go after a given outcome and seek to coordinate a collection of data to achieve that outcome. When you do so, people start bringing in other data sources and items they want to link. So it’s just getting you to a point where you’re going after the next outcome you didn’t foresee going after before. You’ve got to be able to be a little flexible and versatile about how you think things. But if you start with and deliver one result, you’ll be shocked as to where it takes you next.
- 8. Vince Campisi, Chief Information Officer, GE Software: That helps better inform the correct structure, the forums, and finally, it sets the more granular operational levels such as training, recruiting, and so on. It is essential to align yourself with how you are going to run the company and how you are going to communicate with the larger organization. And there it will fall in line with everything else. It is how we set out on our journey. Vince Campisi, Chief Information Officer, GE Software: One of the things we found was when we began and concentrated on a goal, it was a perfect way to generate value and get people excited about the opportunity rapidly. And it took us to places we didn’t plan to be going before. Then we can go after a given outcome and seek to coordinate a collection of data to achieve that outcome. When you do so, people start bringing in other data sources and items they want to link. So it’s just getting you to a point where you’re going after the next outcome you didn’t foresee going after before. You’ve got to be able to be a little flexible and versatile about how you think things. But if you start with and deliver one result, you’ll be shocked as to where it takes you next.
- 9. Ash Gupta, Chief Risk Officer, American Express: The first change we had to make was just to increase the quality of our results. We’ve got a lot of data, so sometimes we just didn’t use the data, so we didn’t pay as much attention to its accuracy as we do now. It was one, to ensure the data had the correct history, that the data had the right intent to represent the customers. It is a road, in my opinion. We have made strong progress, and we expect this progress to continue in our program. The second field is working with our people and making sure we centralize all aspects of our business analytics. We centralize our resources, and we democratize their use. The other thing, I believe, is that we understand as a team and as an organization that we do not have enough resources ourselves, so we need cooperation from all kinds of organizations beyond American Express. This partnership comes from innovators in technology, and it comes from data providers and analytical firms. We need to put together a full package for our business colleagues and partners so that it is a compelling case that we are collectively improving stuff, that we are co-learning, and that we are building on each other.
- 10. Examples of the impact of data and advanced analysis
- 11. Victor Nilson, senior vice president, big data, AT&T: The consumer experience also begins with us. That’s what matters the most. We now have a growing range of very complex items at our customer service centers. Only the simple products often have potentially very complicated problems or solutions, because the workflow is very complicated. And, how can we improve the process at the same time for both the customer-care agent and the client, if there is an interaction? We’ve used big data strategies to evaluate all the various permutations and improve the knowledge and solve or strengthen a specific situation more quickly. We take out the uncertainty and make it a smooth and actionable task. At the same time, we should evaluate the data and then go back and say that, in a particular case, whether we are proactive or not for optimizing the network. So, not just for customer service, but also for the network, we take the optimization and then tie it together.
- 12. Vince Campisi: I’ll give you a personal viewpoint and an outer viewpoint. One is that we do a lot of what we call creating a digital thread— how you can link innovation to a product through engineering, manufacturing, and out. And that’s where we focus on the brilliant factory. Take the driving optimization of the supply chain as an example. We’ve been able to take over 60 separate silos of direct-material sourcing knowledge, exploit analytics to look at new relationships, and use machine learning to find enormous amounts of flexibility in how we source direct materials that go into our product. An external example is how we use analytics to enhance the efficiency of properties fully. We call it the administration of asset results. And we are beginning to allow digital industries, like a digital wind farm, where analytics can be leveraged to help the machines optimize themselves. And, you can help a power-generating company use the same wind that comes through, and by making the turbines pitch themselves correctly and knowing how they can maximize the wind speed. We have also shown the potential to generate up to 10 percent more energy out of the same amount of wind. It’s an example of using analytics to help a company achieve higher yield and productivity.
- 14. Victor Nilson: Talent is all about that. You need to have the data, and, naturally, AT&T has a wealth of data. But this is meaningless, without talent. The differentiator is talent. The best talent will go and discover the best technologies; the right talent will go out there and solve problems. We also helped, in part, contribute to the growth of many of the innovative innovations emerging in the open-source culture. We have the tradition of sophisticated lab methods, and we have the Silicon Valley that is evolving. But we do have mainstream talent around the world, where we have very advanced engineers, we have all-level managers, and we want to grow their abilities further. So just this year alone, we have delivered over 50,000 Big Data related training courses. And we continue to make progress on that. It is just a continuum. It could be either a one-week boot camp or advanced data science at the PhD-level. But for those who have the aptitude and interest in it, we want to continue cultivating the talent. We want to make sure that they can improve their skills and then tie it to enhance their output along with the instruments.
- 15. Zoher Karu: Talent is crucial in every path through data and analytics. And analytics talent alone, in my opinion, is no longer enough. We can’t have singularly competent men. So the way I develop my organization, I’m searching for people with a minor and a major. You may be significant in analytics, but in marketing strategy, you may be minor. Even if you don’t have a child, how can you interact with other parts of the organization? Otherwise, the mere data scientist won’t be able to speak to the database administrator, who won’t be able to talk to the market analysis guy, for example, who won’t be able to speak to the owner of the email channel.
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