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Few would object Organizations have more data at their disposal than ever before. But actually deriving meaningful insights from that data and turning knowledge into action is easier said than done. We spoke with her six senior he leaders from leading organizations and asked them about the challenges and opportunities associated with adopting advanced analytics. Vince Campisi, Chief Information Officer, GE Software. Ash Gupta, Chief Risk Officer, American Express. Zoher Karu, Vice President of Global Customer Optimization and Data at eBay. Victor Nilson, Senior Vice President of Big Data at AT&T, said: Ruben Sigala, Chief Analytics Officer at Caesars Entertainment, said: An edited transcript of their comments follows.
interview record
Challenges faced by organizations in adopting analytics
AIG’s chief scientific officer, Murli Burthwal, said: The biggest challenge in evolving from a knowledgeable culture to a learning culture, from one that relies heavily on heuristics in decision-making to one that is more objective, data-driven, and embraces the power of data and technology, is actually is not. cost. At first, it often ends with imagination and inertia.
What I’ve learned over the years is that the power of fear is that today we can evolve ourselves to think and act differently and to ask questions we didn’t previously ask about our role. And a shift in mindset from a professional mindset to a more dynamic and more learning-oriented mindset, as opposed to a fixed mindset, is fundamental to the sustainable health of any business. I believe it is. , large, small, or medium.
Ruben Sigala, Chief Analytics Officer at Caesars Entertainment said: What we found difficult, and what we still find difficult in our discussions with many of our counterparts, is to develop a set of tools that enable organizations to create value efficiently through their processes. to find out. We hear about individual wins in certain applications but it’s still very early days for this to have a fully integrated and more cohesive ecosystem so we’re all struggling I think there is. It seems like it’s been talked about quite a bit over the past few years, but the technology is still changing. The source is still evolving.
Zoher Karu, vice president of global customer optimization and data at eBay, said: One of the biggest challenges concerns data privacy and what is shared and what is not. And my take on it is that consumers are willing to share if value is returned. So how do we protect that information and how do we use it to become a consumer’s partner, not just a vendor?
Capturing impact through analytics
Ruben Sigala: You have to start with the charter of your organization. You need to be very specific about the purpose of the function within your organization and how it is intended to interact with the wider business. Some organizations start with a fairly focused view of supporting traditional functions such as marketing, pricing, and other specific areas. And there are other organizations that are taking a broader view of business. I think you need to define that element first.
This helps to provide the right structure, the best information to the forum, and ultimately sets a more detailed level of operations such as training, recruitment, etc. But aligning how you drive your business and how you interact with the wider organization is absolutely critical. From there, everything else should match. That’s how our path started.
Vince Campisi, Chief Information Officer, GE Software, said: One of the things we’ve learned is when you start and focus on results. This is a great way to deliver value quickly and get people excited about your opportunity. And it took us to places we never thought we’d go. Therefore, you may want a particular result and try to organize your data set to achieve that result. Once you can do that, people will start bringing in other data sources and other things they want to connect to. And it really takes you to a place where you seek the next result that you didn’t expect before. But when you start with one result and make it happen, you’ll be amazed at where you go next.
American Express Chief Risk Officer Ash Gupta said: The only change that had to be made initially was to improve the quality of the data. We have a lot of data, but sometimes we just didn’t use it, and we didn’t pay as much attention to the quality of the data as we do now. was to make sure the data had the right purpose to serve the customer. In my opinion, this is a journey. We are making good progress and hope to continue this progress throughout the system.
The second area is working with employees to centralize some aspects of the business. We centralize functionality and democratize its use. Another aspect is that as a team, as a company, we recognize that we don’t have enough skills ourselves and need collaboration with all kinds of entities other than American Express. This collaboration comes from technology innovators, data providers and analytics firms. I need to put together a complete package for my business colleagues and partners. This makes for a compelling argument that we are developing things together, learning together, and building on each other.
Examples of impact
Victor Nilsson, senior vice president of big data at AT&T, said: We always start with the customer experience. That’s the most important thing. Our Customer Care Center currently has a number of very complex products. Workflows are very complex as even simple products can have very complex potential problems and solutions. So how do you simplify the process for both customer care agents and customers at the same time every interaction?
We used big data techniques to analyze all the different permutations and enhance that experience to resolve or enhance specific situations more quickly. Remove complexity and turn it into something simple and practical. At the same time, you can analyze that data and then decide, “Are we actively optimizing the network for this particular case?” So we not only do customer care but also network optimization and tie it all together.
Vince Campisi: Introduce one internal point of view and one external point of view. One, we’ve been doing a lot of what we call enabling the Digital Thread, how we can connect innovation across engineering, manufacturing, and even servicing a product. is doing [For more on the company’s “digital thread” approach, see “GE’s Jeff Immelt on digitizing in the industrial space.”] Among them, we focus on the Brilliant Factory. For example, we drive supply chain optimization. Getting information on over 60 different silos related to direct materials purchases, leveraging analytics to explore new relationships, and using machine learning to find out how to source direct materials used in products is very important. I was able to identify it as efficient.
An external example is how you can leverage analytics to really improve the performance of your assets. We call it Asset Performance Management. It is also beginning to enable digital industries, such as digital wind farms, where analytics can help optimize machines. So we can help power generators that use the same wind that blows them. We have also demonstrated up to 10% more power generation capacity by properly pitching the turbine and understanding how to optimize that level of wind. Generates energy from the same amount of wind. This is one example of using analytics to help customers generate more revenue and productivity from their existing capital investments.
win the talent war
Ruben Sigala: The competition for analytical capabilities is fierce. Also, maintaining and maintaining a talent base within an organization is difficult, especially when considering this as a core competency. Our primary focus is developing a platform that communicates what we believe to be a key value proposition for individuals seeking to start or sustain a career in this field. .
When we talk about the value proposition, we use terms like having the opportunity to truly impact business results, doing a broad range of analytical exercises that are regularly challenged. But by and large, be part of an organization that sees this as an important part of competing in the marketplace, and do it regularly. Partly, doing it well requires a good training program and very specific interactions with senior teams. You also need to be part of an organization that actually drives the company’s strategy.
Murli Bruthwal: Focusing on the fundamentals of why science was born, what our aspirations are, and how being part of this team shapes the professional evolution of our team members helps us It has proven to be very important in attracting talented talent who are interested.
Yes, money matters. My philosophy with money is that I want to be in the 75th percentile range. I don’t want to be in the 99th percentile. Because wherever you are, most people (especially those in the data science department) can raise their rewards by 20-30% if they choose to take action. My intention is not to try to close that gap. My intention is to create an environment and culture where they can see what they are learning. They understand that they are addressing issues that have a broader impact on the company, the industry and, through it, society. They are part of a vibrant team inspired by why the team exists and how success is defined. For me, focusing on that is an absolutely vital enabler for attracting the kind of talent that I need and, for that matter, that everyone else needs.
Developing appropriate expertise
Victor Nilsson: Talent is everything, right? AT&T has a wealth of data. But without talent, it means nothing. Talent is a differentiator. The right people go find the right technology. The right talent solves problems out there.
We have partially contributed to the development of many new technologies emerging in the open source community. We have traditional advanced technology from the lab and we have the emerging Silicon Valley. But we also have highly advanced engineers, managers at all levels, and key talent from across the country who want to further develop their talents.
That’s why we’ve delivered over 50,000 big data-related training courses this year alone. And we continue to push it forward. It’s a whole continuum. It can be a one-week boot camp, or it can be advanced PhD-level data science. But we want to continue to develop that talent for those who have the aptitude and interest in it. I would like to be able to maximize the
Zohel Kar: Talent is key in any data and analytics journey. In my opinion, analytical talent alone is no longer enough. No one has unique skills. And the way I build my organization is by looking for people with majors and minors. How do you communicate with the rest of your organization if you don’t have minors? and the market researcher cannot speak to the owner of the email channel. You need to make sound, scalable business decisions based on your analytics.
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