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Data presents an invaluable opportunity for firms to innovate, but only if they know what to do with it. In her latest research, Wharton professor of operations, information and decisions Lynn Wu looks at how different organizational structures influence the use of data analytics to spur innovation. Her paper, “Data Analytics Supports Decentralized Innovation,” is forthcoming in the journal Management Science and was co-authored by Wharton operations, information and decisions professor Lorin Hitt and Wharton doctoral candidate Bowen Lou. Wu spoke with Knowledge@Wharton about the research. (Listen to the podcast at the top of this page.)
An edited transcript of the conversation follows.
Knowledge@Wharton: Let’s start by talking more generally about the ways in which data analytics can help fuel innovation.
Lynn Wu: There are so many great examples. [There are] lots of analytics technologies, especially fueled by recent advances in machine learning and the vast amount of digitalized data. For the first time, I think a year or two ago, a machine powered by analytics was able to win against the best human player in the world in the game “Go.” We also have self-driving cars [that rely on] the large number of digitized images that improve vision recognition systems dramatically. We have seen IBM’s Watson culling through lots and lots of [research] literature in digital records and find six new cancer suppressors in two months. That would have taken researchers years to find. Even in the realms of art and music, for first time AI analytics is creating art that people are willing to buy and music that people are willing to hear. So, we do see lots of data-driven analytics creating a lot of really cool innovations around us.
Knowledge@Wharton: In terms of how companies can use data analytics to spur innovation, what was the central question or problem that you were trying to address with this research?
Wu: I just gave you some really good examples of [analytics-based] innovations that we are all aware of. But if you look at innovation statistics, economists have documented that we have been spending more and more money per capita on research, but we are actually having a decline in the rate of innovation we’re generating. We’re spending more money, but we’re getting much less in return. That seems like a paradox, right? We’ve seen lots and lots of cool, data-driven innovation, yet we don’t see any innovation statistics.
“Analytics is really great at finding these linkages or hidden patterns we may not easily observe by mining through a ton of data.”
That makes me think about a famous quote by Nobel laureate Bob Solow from three decades ago. He said that we see computers everywhere, but not in productivity statistics. If you replace “computers” with analytics, and you replace “productivity” with innovation, we have the same paradox. What I’m trying to do in my research is to see whether we can use the same set of frameworks to explain the analytics innovation paradox that we are observing today.
Knowledge@Wharton: In your paper, you note that there are many factors in an organization that could affect innovation, but you specifically focus on two ways that firms handle innovation: decentralized and centralized. What is the difference between those two?
Wu: There are definitely tons of factors that might mediate the relationship. I purposely picked decentralized and centralized innovation because there’s lots of work that’s been done on [the questions] what is the advantage of decentralization, and what is the advantage of centralization? What I define as decentralized innovation structures is based on their collaboration networks. You can think of inventors creating the same patent and working together, so there’s a link between them. When I describe a decentralized innovation network, it’s really about how concentrated the innovators [are when they collaborate]. You may see a big cluster with lots of inventors all working together, or you can see a very much decentralized or dispersed — the opposite of a concentrated — structure where there are lots of small clusters of individuals and they are loosely connected together.
The reason I picked those two structures is that neither one is absolutely better than the other. We’ve seen throughout many industries that very innovative firms have both types of structures. If you look at Apple, it’s very much a centralized, concentrated cluster with a small group of people responsible for a vast majority of innovation. But if you look at Google, you see a small group of clusters, and they are loosely connected. They are also very, very productive in terms of their innovation. You see that in pharma as well. You see Sanofi and you see Roche. Sanofi has a much more decentralized or dispersed structure, and Roche has a more concentrated structure. My question was, do these structures play a role in how they use analytics to innovate?
Knowledge@Wharton: You say that one’s not better than the other, but what did you find in your research in terms of how these structures impact innovation?
Wu: That’s getting into the key of what analytics can do for innovation. What I find is that analytics can really drive the creation of recombinations, or combining a diverse set of existing technologies in a new way. Each individual technology already exists, but how do we recombine them in some ways to create a new innovation? Or reuse something that we know solved one problem, but apply it to a different domain? Analytics is really great at finding these linkages or hidden patterns we may not easily observe by mining through a ton of data. That is really the key to driving decentralized innovation for several reasons.
“Analytics can really drive the creation of recombinations, or combining a diverse set of existing technologies in a new way.”
Decentralization’s advantage is that there are small clusters working on a problem, so they really know what the problem is in that domain. They see what exactly they can do to solve that problem more closely than a centralized structure, which is bigger but much more coordinated. A decentralized structure lacks the coordination. They know very well what they do, but they don’t know what other people are doing. Centralized structures know what everyone is doing, but they don’t know details for each individual problem in the domain, unless they have the capability to comb through lots and lots of data finding hidden patterns. That’s exactly the disadvantage that decentralized structures have. In that sense, decentralization does not easily find other people’s work. Analytics finds a way to cull through that and find you a new combination, a new way of solving your problem that you may not have easily found without analytics. That capability, of course, can also help a centralized structure. A centralized structure already has that search and coordination mechanism built in; it just doesn’t have as much higher marginal benefit as a decentralized structure would have.
Knowledge@Wharton: As data analytics becomes more ubiquitous, will it pay off for firms to move towards a more decentralized innovation structure?
Wu: That’s a great question. I think that depends on what your innovation goals are. If you were a decentralized structure and you really want to do innovation that combines existing technologies in a new way, or reuses technology applied to a different domain to solve a different problem, analytics is great for helping because you get both advantages. You deepen the problem domain and get diverse knowledge from outside.
But centralization is great at looking at bigger pictures and creating novel or de novo technologies that could act like a building block for future recombinations. They’re foundational technologies. That’s difficult to create with big data. [Centralized structures] don’t necessarily need to have big data to create that kind of technology.
Knowledge@Wharton: Like Apple, for example?
Wu: Exactly. Lots of it is creativity or human intuition that’s kind of hard to digitize. If you were in that type of innovation work, then having analytics or having a decentralized infrastructure wouldn’t necessarily help you. It depends on what your goals are. Because analytics is making it so much easier to recombine new technologies in a new way, that raises the value of the new foundational technology. Once I create it, it can be quickly exploited to make new combinations. There’s a trade-off between the two.
Knowledge@Wharton: Are there any contexts where you found that data analytics might impede innovation?
“We can use lessons learned from past generations of IT and analytics technologies to inform us about what the future could look like.”
Wu: We didn’t have any conclusive evidence that it impedes, but we definitely find that analytics does not help with building or creating de novo innovation that is foundational and can act like a future building block for future combinations. That is something that analysts are not great at. If you think about it, if something’s so new, it probably didn’t exist in data yet. So, there’s not much you can do with data analytics to help you find that pattern.
Of course, recombinations can be radical innovations; they can have a lot of profound impacts. Lots of innovations are recombinational, right? In that sense, I think analytics are really moving forward in what we could do to speed up the innovation process.
Knowledge@Wharton: What’s next for your research on innovation?
Wu: We are on the cusp of a really great change in technology, especially with the rise of AI and machine learning that is dramatically changing employment and how work is organized. I’m looking at analytics, including AI and the subfield of deep machine learning, to examine how we can use them more effectively to innovate. That is an emerging problem. We can use lessons learned from past generations of IT and analytics technologies to inform us about what the future could look like.
I only mentioned centralized and decentralized innovation structures, but there are lots of different ways of innovating. If we think of analytics or the large field of artificial intelligence, it’s a general-purpose technology. It will take decades for us to really understand how to apply it. Think about electricity. It took a long time for us to learn to use electricity effectively…. This really provides a ripe opportunity for us to examine what cutting-edge firms are doing with these technologies and how they innovate. And that can provide some key lessons for the future about what other firms can do to leverage analytics better to innovate.
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Anumakonda Jagadeesh
Excellent.
Analytics is the discovery, interpretation, and communication of meaningful patterns in data. It also entails applying data patterns towards effective decision making. In other words, analytics can be understood as the connective tissue between data and effective decision making within an organization. Especially valuable in areas rich with recorded information, analytics relies on the simultaneous application of statistics, computer programming and operations research to quantify performance.
Organizations may apply analytics to business data to describe, predict, and improve business performance. Specifically, areas within analytics include predictive analytics, prescriptive analytics, enterprise decision management, descriptive analytics, cognitive analytics, Big Data Analytics, retail analytics, supply chain analytics, store assortment and stock-keeping unit optimization, marketing optimization and marketing mix modeling, web analytics, call analytics, speech analytics, sales force sizing and optimization, price and promotion modeling, predictive science, credit risk analysis, and fraud analytics. Since analytics can require extensive computation (see big data), the algorithms and software used for analytics harness the most current methods in computer science, statistics, and mathematics.
Innovation in its modern meaning is “a new idea, creative thoughts, new imaginations in form of device or method”. Innovation is often also viewed as the application of better solutions that meet new requirements, unarticulated needs, or existing market needs. Such innovation takes place through the provision of more-effective products, processes, services, technologies, or business models that are made available to markets, governments and society. An innovation is something original and more effective and, as a consequence, new, that “breaks into” the market or society. Innovation is related to, but not the same as, invention, as innovation is more apt to involve the practical implementation of an invention (ie new / improved ability) to make a meaningful impact in the market or society, and not all innovations require an invention. Innovation oftenmanifests itself via the engineering process, when the problem being solved is of a technical or scientific nature. The opposite of innovation is exnovation.
While a novel device is often describedas an innovation, in economics, management science, and other fields of practice and analysis, innovation is generally considered to be the result of a process that brings together various novel ideas in such a way that they affect society. In industrial economics, innovations are created and found empirically from services to meet growing consumer demand.
Marketing has evolved from a creative process into a highly data-driven process. Marketing organizations use analytics to determine the outcomes of campaigns or efforts and to guide decisions for investment and consumer targeting. Demographic studies, customer segmentation, conjoint analysis and other techniques allow marketers to use large amounts of consumer purchase, survey and panel data to understand and communicate marketing strategy.
Marketing analytics consists of both qualitative and quantitative, structured and unstructured data used to drive strategic decisions in relation to brand and revenue outcomes. The process involves predictive modelling, marketing experimentation, automation and real-time sales communications. The data enables companies to make predictions and alter strategic execution to maximize performance results.
Web analytics allows marketers to collect session-level information about interactions on a website using an operation called sessionization. Google Analytics is an example of a popular free analytics tool that marketers use for this purpose. Those interactions provide web analytics information systems with the information necessary to track the referrer, search keywords, identify IP address, and track activities of the visitor. With this information, a marketer can improve marketing campaigns, website creative content, and information architecture.
Analysis techniques frequently used in marketing include marketing mix modeling, pricing and promotion analyses, sales force optimization and customer analytics e.g.: segmentation. Web analytics and optimization of web sites and online campaigns now frequently work hand in hand with the more traditional marketing analysis techniques. A focus on digital media has slightly changed the vocabulary so that marketing mix modeling is commonly referred to as attribution modeling in the digital or marketing mix modeling context.
These tools and techniques support both strategic marketing decisions (such as how much overall to spend on marketing, how to allocate budgets across a portfolio of brands and the marketing mix) and more tactical campaign support, in terms of targeting the best potential customer with the optimal message in the most cost effective medium at the ideal time.
People Analytics is using behavioral data to understand how people work and change how companies are managed.
People analytics is also known as workforce analytics, HR analytics, talent analytics, people insights, talent insights, colleague insights, human capital analytics, and HRIS analytics. HR analytics is the application of analytics to help companies manage human resources. The aim is to discern which employees to hire, which to reward or promote, what responsibilities to assign, and similar human resource problems. HR analytics is becoming increasingly important to understand what kind of behavioral profiles would succeed and fail. For example, an analysis may find that individuals that fit a certain type of profile are those most likely to succeed at a particular role, making them the best employees to hire.
However, there are key differences between people analytics and HR analytics. “People Analytics solves business problems. HR Analytics solves HR problems. People Analytics looks at the work and its social organization. HR Analytics measures and integrates data about HR administrative processes,” says Ben Waber, MIT Media Lab Ph.D. and CEO of Humanyze. Josh Bersin, founder and principal at Bersin by Deloitte agrees that people analytics is a larger industry than HR Analytics, explaining, “… over time, I believe it doesn’t even belong within HR. While it may reside in HR to begin with, over time this team takes responsible for analysis of sales productivity, turnover, retention, accidents, fraud, and even the people-issues that drive customer retention and customer satisfaction… These are all real-world business problems, not HR problems.”
A common application of business analytics is portfolio analysis. In this, a bank or lending agency has a collection of accounts of varying value and risk. The accounts may differ by the social status (wealthy, middle-class, poor, etc.) of the holder, the geographical location, its net value, and many other factors. The lender must balance the return on the loan with the risk of default for each loan. The question is then how to evaluate the portfolio as a whole.
The least risk loan may be to the very wealthy, but there are a very limited number of wealthy people. On the other hand, there are many poor that can be lent to, but at greater risk. Some balance must be struck that maximizes return and minimizes risk. The analytics solution may combine time series analysis with many other issues in order to make decisions on when to lend money to these different borrower segments, or decisions on the interest rate charged to members of a portfolio segment to cover any losses among members in that segment(Wikipedia).
Predictive models in the banking industry are developed to bring certainty across the risk scores for individual customers. Credit scores are built to predict individual’s delinquency behavior and widely used to evaluate the credit worthiness of each applicant. Furthermore, risk analyses are carried out in the scientific world and the insurance industry. It is also extensively used in financial institutions like Online Payment Gateway companies to analyse if a transaction was genuine or fraud. For this purpose they use the transaction history of the customer. This is more commonly used in Credit Card purchase, when there is a sudden spike in the customer transaction volume the customer gets a call of confirmation if the transaction was initiated by him/her. This helps in reducing loss due to such circumstances.
Dr.A.Jagadeesh Nellore(AP),India