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12 innovation with it1

It is well known that innovation with IT enables new business models (e.g., Amazon, iTunes), new products and services (e.g., tablets, mobile banking), new or improved processes (e.g., ERP, supply chain), and cost savings (e.g., self-service, offshore sourcing). Yet, such innovation is still very much a hit-or-miss proposition. For as many successful innovations as there are with technology, there are an equal or greater number of failures. Furthermore, although it is possible to do many innovative things with technology, it is much more difficult to find the ones that will deliver real and sus- tainable value to an organization.

IT organizations have always been expected to improve what is currently being done but it is much more difficult to undertake something that is different from what has traditionally been done. When innovating with technology, not only must the market be ready for the innovation (i.e., timing), but also network effects and complementary products and services must be available for it to succeed (e.g., one telephone is not very  useful; mobile banking failed before the introduction of smart phones). Finally, many innovations fail because an organization’s culture cannot sustain or exploit them (e.g., Kodak with digital imaging). In short, successful innovation is still a bit of a mystery and many IT leaders are trying to explore how best to operationalize it to deliver real business value.

This chapter explores innovation—an organization’s need to reinvent its products and services and occasionally itself—with a focus on IT-enabled innovation. We begin by examining why innovation is critical, and how/why IT is driving most innova- tion today. Following this, we examine various types of innovation. Then we present a typical innovation life cycle and examine some of the challenges encountered by orga- nizations when attempting to achieve innovation. In the final section of this chapter, we offer advice for managing IT-enabled innovation.

1 This chapter is based on the authors’ previously published article, McKeen, J. D., and H. A. Smith. “Strategic Experimentation with IT.” Communications of the Association for Information Systems 19, article 8 (January 2007): 132–41. Reproduced by permission of the Association for Information Systems.

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The Need for INNovaTIoN: aN hIsTorIcal PersPecTIve

It is well-established that the need to innovate is necessary for long-term organiza- tional survival (Christensen and Raynor 2003; Hamel and Välikangas 2003). According to Christensen (1997), there are two types of innovation: sustaining and disruptive. Sustaining innovation improves an existing product or enhances an existing service for an existing customer. In contrast, disruptive innovation targets noncustomers and deliv- ers a product or service that fundamentally differs from the current product portfolio. Sustaining innovation leaves organizations in their comfort zone of established mar- kets, known customers, and realizable business models. Disruptive technologies enjoy none of these benefits. To be successful for the initiating organization, the disruptive innovation must meet two basic requirements: it must create value as perceived by cus- tomers, and it must enact mechanisms to appropriate or capture a fair share of this new value (Henderson et al. 2003). For other organizations and particularly dominant players, disruptive innovation can be devastating. Christensen (1997) refers to this as “the innovator’s dilemma.” For an excellent discussion of disruptive technologies and a review of six leading theories of innovation, see Denning (2005).

Innovation comes about through organizational change, and here, too, we see two dominant forms: continuous change versus punctuated equilibrium. Brown and Eisenhardt (1997) describe continuous change as “frequent, relentless, and perhaps endemic to the firm,” whereas the punctuated equilibrium model of change “assumes that long periods of small, incremental change are interrupted by brief periods of discontinuous, radical change.” In this latter case, change is primarily seen as “rare, risky, and episodic.” Although it is tempting to equate sustaining innovation with continuous change and disruptive innovation with punctuated equilibrium, it is not so simple. In fact, Brown and Eisenhardt (1997) cite examples of firms that have successfully reinvented themselves through continuous change as opposed to abrupt, punctuated change. These authors suggest “in firms undergoing continuous change, innovation is intimately related to broader organization change.”

The Need for INNovaTIoN Now

Today, there is an increased sense of urgency about innovation with technology. “Our business partners now ‘get’ the importance of IT,” said one manager. “But they’re looking for IT to tell them what’s possible.” Another added, “They’re telling us ‘We don’t know what we don’t know’ and they expect IT to make new things possible.” What this means is that IT leaders are being challenged by business leaders to spear- head innovation in their organizations. This is a new mandate for IT.

Different industries are feeling different levels of pressure about innovation. At  level one, experienced by virtually every industry, new forms of technology are driving up the expectations of both business and consumers for more mobility, more usability, more customer-friendliness, and more cost-effectiveness. “There’s a market- place shift happening towards the customer,” said a manager. “We are moving from being product and process-centric to being customer-centric.” This shift is driving more horizontal views in the organization and demand for end-to-end processing, as opposed to the deeply vertical, siloed perspectives of the past.

172 Section III • IT-Enabled Innovation

Within level two industries, there is a belief that IT can be a strategic differentia- tor for an organization and that technology is a fundamental component of business strategy. “Our business sees technology as the key to new growth,” said a manager. Unfortunately, this pressure plus the greater availability of technology in the cloud is leading some in the business world to “take technology decisions into their own hands” and “do an end-run” on the IT department thinking that they don’t need IT. In response, IT is feeling new pressure to get ahead of business needs and demonstrate its innova- tive capabilities.

Level three industries experience a deep sense of unease that the fundamental assumptions upon which their business is based are changing. “We can no longer be com- placent,” said a manager. In these industries, there is growing uncertainty and fear that an upstart company could steal away huge chunks of business value by using technology to provide their products or services more cheaply or effectively. At this level, innovation is about survival and making sure that an organization is able to quickly adapt to new busi- ness models and withstand strategic challenges. Companies in these industries have seen that threats today can come from non-traditional competitors and they recognize that innovation with technology is the only way to ensure they will continue to stay viable.

UNdersTaNdINg INNovaTIoN

Innovation with technology is a complex concept. One participant defined it as, “Fresh ideas that create value.” It can include a variety of new things that are created by or enabled with technology, such as new markets, new products, new demand, new pro- cesses, new capabilities, and new practices. “It’s all about value,” he stressed. “I, like many others, am guilty of sometimes getting distracted by shiny, new gadgets rather than focusing on the value that innovation brings.” Typically, innovation is not the invention of something completely new, but its use in a new way, bringing something new to an industry for the first time, or combining it with another service to provide new value. In short, innovation in an organization lies at the intersection of the answer to three significant questions that create the strategic environment within which inno- vation with technology can deliver value (see Figure 12.1):

• What is viable in the marketplace? • What is desirable to the business? • What is possible with technology?

Ideally, innovation also refers to the process whereby a company creates new things that deliver value. There is no generally accepted methodology for innovation but we  have learned that effective, successful innovation has at least five stages. The first stage is ideation—generating innovative ideas. There are many ways of doing this— ranging from focused executive meetings to the modern online version of the sugges- tion box. This stage must address two questions: How do we get people to share their ideas? How do we respond to their ideas? In most cases, there are lots of ideas out there. In fact, managers noted that attempts to stimulate innovation in their organizations led to them being initially deluged with new ideas. However, lacking the ability to screen and prioritize or act on them, the ideas soon dried up. Research shows that the biggest reason why people do not share their ideas is that past experience has shown them that management doesn’t respond to or act on them (DeSouza 2011).

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Thus, the second stage is advocacy. Good ideas need a sponsor who firms up prom- ising innovative suggestions, seeks funding for them, and acts as a mentor to take them to the next level. One company has an advocacy process that seeks advocates from a business unit other than the one where an idea is generated, thereby encouraging broader organizational support for good ideas.

Stage three is proof of concept. This can consist of laboratory testing over a period of a few weeks to explore the viability of key technologies or ideas that are central to the success of an innovation. This part of the process is very agile and adaptive and highly dependent on business–IT collaboration. Teams are kept small and focused.

A successful proof of concept can lead to a fuller trial or pilot in stage four where the innovation is exposed to the market in a limited and measured way. A market segment is defined, and certain customers (who may be employees) are offered the chance to experiment with the new product or service. Measurements are taken to understand results, which may include marketing/branding issues, financial price points, and operational impacts. Typical pilots take about four to twelve weeks, but may be extended.

The fifth and final stage is transition or “go to market,” where the innovation enters into the mainstream IT production process to ensure the idea is “industrial strength.” Many shortcuts, which served well enough for the pilot, must now be engineered to meet production standards. For example, in one proof of concept, a financial organi- zation developed a mobile application without privacy or security protections. These were then added in at this final stage.

Unlike other types of IT projects, the goals of innovation projects can be fuzzy. Focus group participants stressed that innovation projects should not have to meet the same ROI or defined benefits as other IT projects. “Enforcing traditional stage gate cri- teria too early in our innovation process killed off a lot of good ideas,” said one man- ager whose company has now changed this practice. Furthermore, the full value of some innovations may not be immediately apparent. “We are innovating to develop a platform for direct customer interaction,” said a manager, “but we are not telling our sales staff this right now.” The results of this process can be both “‘big I’ innovation that refers to substantial and significant changes and ‘little i’ innovation that refers to smaller ongoing improvements,” explained one participant.

What Is Possible with Technology

What Is Desirable to the Business

What Is Viable in the

Marketplace

Innovation

fIgUre 12.1 The Organization’s Strategic Environment for Innovation with Technology

174 Section III • IT-Enabled Innovation

A major difference between innovation projects and more traditional IT proj- ects is that failure at any stage is anticipated for many ideas and should be expected. Participants stressed that the learning gained from unsuccessful ideas is an asset that is valuable. “We need to document our failures so that we can learn from them in the future,” said one. “Innovation is not a binary process,” said another. “We need to recog- nize that we can leverage many elements in different ways over time to build successful innovations.” Thus, feedback from all participants and at all stages of innovation is an especially crucial component of the innovation process (see Figure 12.2).

The valUe of INNovaTIoN

Increased business value is the goal of innovation, but sometimes it is not always clear what that value is. Many innovations do not deliver results in terms of ROI or other measureable metrics. “You can’t use traditional metrics, like revenue generation, when it comes to innovation,” said a manager. “Value can’t always be quantified,” said another. Yet, it is important to understand where and how value is delivered by innova- tion or this effort will soon lose out to more measurable initiatives that have a clearer short-term value.

Communication of value is therefore essential to ensuring innovation is sus- tainable in the organization over the long term. From this perspective, value has two components:

1. Is it desirable? “Our business users and customers can’t always articulate a clear value proposition,” said a manager “but they can tell you if they want it and like it.” Therefore, customer testimonials and social media comments can be

Ideation

Advocacy

Proof of concept

Pilot

Transition

Results

Vendors, Employees, Others

Business Leaders

Support

Top Ideas

First Users

Successful POCs

Stage Gate

Valuable Innovations

The Strategic Environment

fIgUre 12.2 The Technology Innovation Process

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good mechanisms for companies to tell if they are on the right track with innova- tion and user perceptions of value should be carefully monitored. “Even a simple change can go viral if users perceive its value,” said a manager. “And customers also know when the value is not there,” added another.

2. Does it build our innovative capabilities? Innovations in many industries rapidly become table stakes. “The real value of innovation is the ability to innovate continu- ously and consistently,” said a manager. The capability to rapidly scan the environ- ment, incorporate new ideas and technologies into an evolving business strategy, make the associated organizational and technological adaptations, and execute quickly, is the real prize. “Innovation isn’t a one-time project, but the ability to deliver over and over again,” said a manager.

INNovaTIoN esseNTIals: MoTIvaTIoN, sUPPorT, aNd dIrecTIoN

Three conditions are necessary for innovation to be successful: (1) motivation, (2) sup- port, and (3) direction. As one manager stated, “Without motivation, little will happen; without support, little can happen; and without direction, anything can happen.” The focus group’s recommendations to others seeking to improve their innovation practices include the following:

1. Motivate: Establish rewards for innovation. Although many individuals are naturally drawn to innovation, the demands of everyday work often drive this interest and inclination into remission. Furthermore, innovation is risky, and not all people are willing to jeopardize their reputations. As a result, innovation does not flourish without intervention. According to focus group members, the way to create an innovation-enabled organization is twofold: provide incentives and rewards to support innovation and risk taking, and make it everyone’s job. Good ideas are good ideas, and experience shows that they are as apt to originate at the customer interface as they are within the laboratory or the executive ranks.

Taking this a step further, one company has made innovation a compo- nent of everyone’s annual performance assessment. This organization offers specific types of formal rewards for different types of innovation that range from patentable ideas to emerging business opportunities. Not all rewards need be formal though. One firm uses a system of frequent informal rewards (e.g., books, tickets, cards, recognition days, and executive citations) to recognize innovative ideas and encourage and reward innovation with IT. Another company discovered that the best reward for IT personnel is simply the opportunity to work and play with new technology! In this company, enterprising IT personnel win the right to experiment with new technology without the need for champions or sponsors. According to the manager involved, this activity is funded by “skunkworks” and “beg and grovel.”

2. Support: Create infrastructure to sustain innovation. Offering rewards for innovation sends employees the signal that innovation is encouraged and will be recognized and valued. This provides the motivation for individuals to experiment, but organizations need to provide support for such experimentation if they want it to happen. Over time, the combination of recognition and support builds a culture of innovation.

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Many firms believe it is also necessary to build some infrastructure around IT innovation. One company, for instance, created the position of “chief scientist” and provided that office with a budget and resources. This was the organization’s “way to signal to everyone that the lifeblood of the organization is discovery . . . not just innovation,” said the manager involved. At this company, “innovation is a given” and expected in all parts of the business. “Discovery,” however, conveys a sense of urgency as well as the notion that the company needs to continually reinvent itself to survive in the marketplace.

Many companies have formal centers (or laboratories) to support innovation. Depending on the firm, the roles of these centers vary from “new product introduction” to “new technology introduction” to “business venturing” to “incubation centers.” Where IT is considered a key business driver, they usually focus almost exclusively on strategic IT innovation. The critical aspect of their creation is the provision of support and infrastructure to enable idea review and experimentation. Most centers are formally entrenched within the organization with ongoing funding, permanent staffing, and well-developed procedures and pro- cesses to encourage, guide, and support innovation. According to one manager, the key element is “to link sponsorship to innovation,” reflecting the fact that “good ideas don’t make it on their own.”

Companies in the group reached consensus on the mandate for innova- tion centers, but they disagreed about their governance. Two distinct strategies surfaced:

• Insulate. This strategy creates innovation centers as places where “all lines of business can come together to address common problems.” According to pro- ponents, the key benefit of this approach is the ability to foster synergies across the business in the belief that innovation is best “nurtured away from the main- stream business.”

• Incubate. Those following this strategy place their innovation centers within specific lines of business (LOBs). Proponents suggested that forcing innovation to be housed within a single LOB focuses innovation on “real” problems and opportunities with committed local ownership.

The innovation infrastructure that was common to virtually all organi- zations in the group was the maintenance of an intranet for launching ideas. These sites are considered to be effective for soliciting, vetting, and sharing ideas and/or opportunities. According to one manager, an intranet’s chief value is that “anyone can input and everyone gets access” to build on ideas. In firms with innovation centers, intranets are effective “feeder” systems. In organiza- tions lacking the formal support of an innovation center, ideas identified on the intranet require a sponsor to marshal support to turn them into realizable products and/or services.

A common form of financial support is the establishment of internal venture funds. In about half of the participating organizations, funding mechanisms had been set up to support IT innovation. Typically, such funds are made available on a competitive basis with an oversight committee in place to award resources and to monitor progress and completion.

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3. Direct: Manage innovation strategically. One manager pointed out that “experimentation never fails as long as there has been learning.” Strictly speak- ing, the focus group agreed but felt that “any such learning would have to be strategically important for the organization” for it to be considered successful. According to the group, learning for the sake of learning was “an activity enjoyed by academics”—much to our chagrin! They suggested that providing motivation and support for individuals to experiment freely would be a recipe for disaster. Organizations must provide direction for these activities. Strategic IT innova- tion does not occur by happenstance. Some participant suggestions for direct- ing IT innovation in order to ensure that it was strategically relevant include the following:

a. Link innovation to customer value. A simple yet effective way to accomplish this is to focus on emerging pain points. At one company, all new ideas had to articulate the specific customer pain point (CPP) that would be addressed. This requirement, in and of itself, produced results. As the manager involved related, “The identification and surfacing of CPPs stimulated considerable and sometimes heated discussion. Many people were surprised to learn of CPPs, and many potential solutions emerged. It was a case of ‘if only I had known.’” Unfortunately, failure to articulate business value to the customer is a common phenomenon.

b. Link experimentation to core business processes. The opposite approach focuses IT experimentation internally on core business functions. One participant, whose organization is “currently reluctant to experiment in the market,” focuses all its experiments on core business activities. “Our belief is that innovation is strategic only if it produces significant efficiencies for internal operations in a way that can be captured on the bottom line,” she said.

c. Use venture funds to guide strategic initiatives. Although establishing venture funding for innovation is a form of support (as already noted), the governance of such funds can be instrumental in achieving strategic alignment. Venture funds are typically given for initiatives that do the following:

• Make greater use of innovation resources • Focus on new business models • Explore new/disruptive technologies • Focus on penetrating new markets • Leverage cross-organizational capabilities • Streamline decision making • Focus on opportunities that can be scaled.

challeNges for IT leaders

Although all of the managers in the focus group felt strongly that innovation is essential both to the future of their organizations and IT, they expressed a number of caveats and concerns about how innovation and an innovation process would be implemented in their organizations. These fell into four major themes:

178 Section III • IT-Enabled Innovation

1. Strike the correct balance. IT managers are acutely aware that they have the responsibility to ensure that their organization’s data and systems are kept safe, secure, and private. Furthermore, many of the so-called “bureaucratic IT processes” were put in place for good reason, such as to ensure quality, interoperability, and cost-efficacy. “We don’t want to go back to the days when cowboys ran IT,” said one manager. “There’s a risk to throwing out all our rules for the sake of rapid innova- tion.” In fact, in many highly regulated industries, such as finance and health care, laws and risk-aversion prohibit much innovation. “We need to balance urgency and quality, and not forget architecture and integration,” said another manager. “These ‘innovations’ can turn into a legacy nightmare very quickly.” Nevertheless, they recognize that there is a need to reconcile these competing priorities and rethink IT processes to facilitate innovation, although at present, there is no accepted way of doing this.

2. Create a sustainable process. One focus group company was on its third innovation process. During the first one, they had lots of input from employees but a lack of interest from executives in taking action on their ideas. The second process, designed to rectify this problem, gave funding to the CIO to implement innovative ideas, but executives flooded the pipeline with ideas to get the “free” IT funding. Now, in its third iteration, the process is focused on innovation in busi- ness intelligence and how this will improve the way work gets done. “Whatever process is put in place, it must be collaborative and include a process for flesh- ing out ideas,” said the manager involved. “There are too many half-baked ideas out there.” In addition, there must be recognition from executives that innovation requires risk. Thus, the innovation process must enable rapid proof of concept and trial development, and link into traditional development procedures during the transition stage.

3. Provide adequate resources. IT staff often become too busy “fixing messes” and doing other types of IT work to undertake innovation. In fact, many companies have had to address this resource gap by carving out specific resources or time peri- ods dedicated for innovation. This is not ideal and most managers would rather see innovation integrated into everyone’s job. Similarly, many executives are simply “too busy” to focus on work with such a vague return. As a result, “there is no real alignment in either IT or our organization about how to undertake and resource innovation,” said a manager. Thus, many IT functions are waiting for senior man- agement to say “go” before implementing a serious innovation process.

4. Reassess IT processes and practices. The IT function needs to be characterized by disciplined thinking, rapid action, agile development, and supported by new technologies that facilitate this. We need to transition from “IT control” to “IT coor- dination,” explained a manager. “Our structures need to be changed to enable us to get us 80% of the way in a project and then to pivot and change direction, if necessary,” said another. A third noted that IT and organizational rewards need to be restructured to motivate more innovation. Finally, existing structures and gov- ernance mechanisms need to be changed to accommodate innovative practices. For example, as already noted, traditional stage gates are not appropriate for early- stage innovation projects. As well, roles such as relationship manager, which serve as gatekeepers into the business, may prevent the learning and collaboration that is needed to promote innovation.

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facIlITaTINg INNovaTIoN

In spite of these challenges and reservations, the focus group agreed that IT’s goal should be to develop an organization with the capability to change and adapt in order to deliver value with technology. Focus group organizations were trying a variety of practices to facilitate innovation. From these, a number of guiding principles for effec- tive innovation may be inferred:

• Focus on achievable targets. Innovation should be manageable and targeted but, at the same time, built so they can scale up easily. According to one manager, “It is far easier to ramp up a proven venture than to plan, build, and deliver a winner.” At one company an innovation involved a “proof of concept” for a new technology involv- ing six sites. Management then rapidly decided to expand the innovation to three hundred sites! This action literally ended experimentation, and the task immediately became large-scale implementation.

• Don’t rush to market. Positive results from an experiment should be viewed as justification for further experimentation, not as a “license to launch.” At one com- pany, a decision to go to market based on very favorable pilot results quickly ran into difficulty. The customers involved in the initial innovation turned out to be unrepresentative of the overall customer base, and the uptake in the market plum- meted as the rollout broadened its base.

• Be careful with “cool” technology. Because innovation deals with technology, it is sometimes easy to be misled by cool technology. The buying public may not under- stand what the technology does (e.g., it’s a browser pen), may have no need for the things that the technology does (e.g., it tracks unvisited sites), and/or may not find the technology appealing (e.g., it’s a mouse with arms and hands). On the other hand, this same technology may become the item that every teenager on the planet must have!

• Learn by design. The goal of innovation is to learn. The group provided several examples of innovation attempts where nothing was learned. In these cases insuf- ficient controls were designed into the process to enable the organization to ascer- tain after the fact what had actually happened. Was failure due to product features and/or functioning? A lack of effective marketing? The price point? Thus, the first step with innovation should be to identify the critical questions that need to be answered, then to design these into the process.

• Link innovation to business strategy. It has already been noted, but bears repeat- ing, that valuable innovation is closely linked to an organization’s strategy and long-term vision. This must be the first level of screening for all new ideas. A close second is business sponsorship to ensure that the idea will be funded and protected during its early stages of development.

• Incubate innovation. Until innovation is fully incorporated into daily work, it is important to provide a safe time/place/manner to promote it. Focus group compa- nies were doing this in different ways. Some have appointed an innovation team; others host focused innovation meetings; still others use innovation labs and “safe” environments. Each of these enables rapid idea generation and screening and places a spotlight on innovation outside of normal practices.

• Collaborate with vendors. “It’s important for IT to get out of its bubble and expand its boundaries,” said one manager. The focus group agreed that IT must

180 Section III • IT-Enabled Innovation

now proactively move out of its comfort zone to enable innovation and innova- tive processes. Working with vendors who can bring a broader perspective into the organization was the most common way of doing this. In some cases, organizations are inviting vendors to present to both IT and the business community in planned “innovation summits,” where new possibilities can be brainstormed and screened for potential value in a very short period of time.

• Integrate business and IT. This is table stakes for effective innovation. “IT must be plugged into the business, able to speak business language, and articulate poten- tial benefits,” said a manager. One good way of doing this is for IT to participate in regular “huddles” with the business to better understand their pain points and what their interests and challenges are.

• Send clear messages. If an organization really wants innovation, it must send clear messages about its importance from the top down. This means that leaders must promote it, resource it, reward it, and most important of all, agree to take on the risks involved. Failure is a given in innovation, but unless management explic- itly acknowledges and accepts this, it is unlikely that an organization’s culture will change to become innovative.

• Manage the process. Innovation will not just happen without attention. It requires active and intentional management to design and monitor a process and determine what works and what doesn’t in a particular organizational culture. Innovation ini- tiatives can sometimes have unforeseen side effects, such as demotivation of nonin- novation staff, disappointment and cynicism when management doesn’t respond to ideas, resistance to poorly communicated changes, and confusion over the pro- cess itself. In addition, existing budgets, structures, processes, and governance can work against innovation, unless there is proper attention to these factors.

• Promote learning agility. Because innovation is still not a factory-like process and is continuing to evolve, IT leaders must cultivate capabilities that promote innova- tion, rather than specific skills. Chief among these, said the focus group, is learn- ing agility or the ability to be flexible to learn new things and new ways of doing things. Whether it’s business language, agile development, a new techno logy, or working with new partners, the best IT staff will be able to take on new challenges, learn, and thrive.

Organizations are just beginning to grasp the scope of the new world of continuous change that is being ushered in by technology, and to grapple with how it will affect their tradi- tional processes of implementing technology for value. This new world is faster paced, with change taking place in smaller, more frequent increments that create and enable flexibility for the organization. Today, we are just at the cusp of this transition, which will result in a

transformation of how both organizations and IT functions operate. Although business as usual can continue for the short term, IT leaders are well aware that their current struc- tures and processes must adapt rapidly to this new world of change. “Innovation” is thus merely the vanguard of what is to come; but addressing it thoughtfully and intentionally is the best way to ensure that an organization is prepared for the future.

Conclusion

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Brown, S. L., and K. M. Eisenhardt. “The Art of Con tinuous Change: Linking Complexity Theory and Time-Paced Evolution in Relentlessly Shifting Organizations.” Administrative Science Quarterly 42, no. 1 (March 1997): 1–34.

Christensen, C. The Innovator’s Dilemma: When New Technologies Cause Great Firms to Fail. Boston: Harvard Business School Press, 1997.

Christensen, C., and M. Raynor. The Innovator’s Solution: Creating and Sustaining Successful Growth. Boston: Harvard Business School Press, 2003.

Denning, S. “Why the Best and Brightest App- roaches Don’t Solve the Innovation Dilemma.” Strategy & Leadership 33, no. 1 (2005): 4–11.

DeSouza, Kevin. Intrapreneurship. Toronto, Canada: University of Toronto Press, 2011.

Hamel, G., and L. Välikangas. “The Quest for Resilience.” Harvard Business Review (September 2003).

Henderson, J. C., N. Kulatilaka, N. Venkatraman, and J. Freedman. “Riding the Wave of Emerging Technologies: Opportunities and Challenges for the CIO.” Working paper, Boston University, School of Management, 2003.

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13 Big Data and Social Computing

It’s a time of significant change for organizations and for IT. Tools for implementing social business (i.e., social media) are being rapidly adopted by the population as a whole and, at a slower pace, by businesses (Kiron et al. 2013; Maoz et al. 2013). At the same time, tools associated with huge amounts of data they gener- ate are facilitating new ways of understanding business through insights, analytics, and predictions (Davenport et al. 2012). These tools enable organizations to engage customers, suppliers, partners, and potential customers in real time and in a multitude of different ways. And they make it possible to incorporate a wide variety of data into organizational processes, enable decision making, and offer new products, services, and delivery channels.

It’s a substantial extension of the trend to move computing to new parts and levels of the organization and beyond traditional corporate boundaries. Whereas big data and social media have been seen as separate organizational challenges in the past, these two fields are now converging in numerous ways, depending on the industry and a company’s needs. Social media is becoming the organization’s front line data collection point, while big data tools use it to drive information and analytics insights that in turn will guide business strategy development. And this is just the beginning. Underlying all of these initiatives are more improved data, whether from customers, applications, or myriad external data sources. In turn, organizations must focus these data on real business problems to gain real business insights, drive real business actions, and deliver real business value (see Figure 13.1).

The challenges for organizations are huge. And IT is at the center of it all, architecting the new platforms, selecting the tools, enabling them, participating in con- tent analysis and design, integrating results with more traditional data and processes, and most importantly, working with the business to innovate, redesign and reimagine all aspects of corporate work. Today, organizations of all types are feeling increasing pressure to take action in these areas but most are still in the earliest stages of maturity, typically experimenting with the data generated from social media (Beath et al. 2012).

This chapter explores how IT leaders are trying to conceptualize the integra- tion of big data and social media concepts to deliver value. It begins by discussing the

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opportunities presented by these technologies and what value organizations could expect from them. Next, it examines the different components that must be addressed in order to deliver value successfully. Then, it looks at some longer-term opportunities for deriving value through innovation with big data. Finally, it examines some of the challenges IT leaders face as they try to adapt their work to the significant changes these tools require and presents some actions for IT managers to consider when beginning to implement big data and social media tools and applications.

The Social Media/Big daTa opporTuniTy

Today’s organizations process over 1,000 times more data than they did a decade ago and the volume of data is growing by 30–50 percent annually (Beath et al. 2012). Social media is the largest component of online data and therefore a major source of data for organizations. In 2013, Facebook had 1.15 billion users with Twitter and LinkedIn following close behind (Maoz et al. 2013). Although over 77 percent of Fortune 500 companies are now using social media to build relationships with their brands, there is still a significant gap between social media usage and how compa- nies are using the data generated by these tools (Fitzgerald et al. 2014). This under- scores the fact that social media data are not valuable in and of themselves, but must be analyzed and presented in ways that derive insights into key business questions. Thus, a major question companies should be asking before embarking on any social media initiative is, how can we use insights from the data we collect to improve our inter- actions with customers, suppliers or employees? (LaValle et al. 2011). “There’s a wide gamut of opportunities out there,” one manager noted. “The quick wins are probably internal with customer and product information. However, companies must keep an open mind and look at everything because sometimes, relevant data can come from unlikely places” (see box).

The Organization

The External Environment

Big Data Social Media

Activities EngagementSocial Media Data

Internal Data

Other External Data

Strategy & Decision-

making Information & Analytics

Other Business Activities

• Customers • Partners • Suppliers • Potential Customers

Figure 13.1 The Relationship Between Big Data and Social Media

184 Section III • IT-Enabled Innovation

In the past, disparate, siloed internal data in systems made data consolidation challenging because massive data “plumbing” was required before analysis could begin and data definitions had to be created before data could be stored or consoli- dated. Today, big data management technologies enable all types of data from multiple sources to be available in one place in native form, thereby providing greatly increased flexibility of analysis. More granular data then allow for finer classifications and seg- mentations to be made so that a business can tailor information or services for a single person or situation, if necessary (Davenport et al. 2012).

A large part of the value of the current business value of social media comes from the people, processes, and technologies that turn the data they generate into insights that drive business decisions and actions (McKeen and Smith 2012). Appropriately applied, companies can then use these data to

• Respond more quickly to the market by making faster decisions. • Make patterns more evident, such as problems with a new product. • Facilitate innovation in products and services, based on customer and other types

of feedback. • Improve reputation and brand awareness.

The value delivered through social media and other forms of big data manage- ment increases as tools and methods become more mature and integrated across the entire value chain (Davenport et al. 2012). While early analytics were based on historic, siloed internal data and rudimentary techniques, more mature approaches use fre- quently refreshed internal and external data and more complex analytical techniques that enable rapid decisions based on robust insights.

And this is just the beginning. Emerging approaches will be based on a deep understanding of real-time data sets from a variety of internal and external sources (Davenport 2013). They will enable real-time decisions supported by multilayered insights from multiple business functions. Companies are just now beginning to com- bine improved sensing capabilities of physical things (i.e., the Internet of things) with other internal and external data sets (Davenport 2013; Laney and White 2014; Smith and

Some Types of Social Media Data

• Wikis • Blogs • Videos • 3D user interface/visualization • Presence awareness • Instant messaging, Twitter • Social networking communities (e.g., Facebook, LinkedIn) • Reputation systems • Collective intelligence systems • Authoring • RSS feeds • Podcasts • Gamified data

Chapter 13 • Big Data and Social Computing 185

Konsynski 2007). Future business opportunities will incorporate real-time information in a variety of new ways, such as:

• Sensing—detecting the current state of a given entity, such as the location of a plane, the speed of a car, or the mood of an individual.

• Mass Visibility—the combination of real-time sensing of multiple entities contex- tualized by their relationships. It can be used to identify such issues as traffic route congestion or how gas prices vary across the country.

• Experimentation—the integration of real-time sensing with the ability to generate and gather reliable data quickly. It can be used to monitor the impact of such things as new Web site layouts or to undertake rapid analytics on new brands.

• Coordination—combining the current state of other entities with the ability to adjust behavior based on fast-cycle feedback, for example, locating people and coordinating their behavior in real time.

To date, most CIOs and business leaders still haven’t identified the value proposi- tions associated with these new types of data or fully understood their organizational implications (McAfee and Brynjolfsson 2012). They are still trying to determine how and where to effectively use social media data.

delivering BuSineSS value wiTh Big daTa

Delivering business value with the big data derived from social media and other data sources requires developing new organizational capabilities in a variety of areas, especially in data and information management. And although it is a truism today that organizational change requires improved governance, sponsorship, processes, and controls, in addition to new skills and technology, these are all essential compo- nents of delivering on the opportunities presented by social media and ultimately, big data (Beath et al. 2012; LaValle et al. 2011). This section explores the key components of developing an organizational capability that can deliver business value from big data and adapt to the rapidly evolving world it represents (see Figure 13.2).

governance

One of the most important questions for companies to ask with respect to social media is, who’s responsible for social media in your organization? Some companies see marketing or corporate communications as having primary responsibility for this function; oth- ers have created an internal social marketing organization, or a committee. Delivering social media today is still fragmented in most organizations, said the IT managers in the group. However, they agreed that because social media also represents an infor- mation asset, ultimately it is IT’s responsibility because, once inside the organization, social media data become part of the organization’s data repository—or big data. Therefore, with the huge amounts of data flooding into organizations, someone needs to be making decisions about it if it is to deliver business value (Ross 2012).

There are a number of issues that IT leaders need to consider when addressing social media/big data governance, such as the control, legal, security, access, staffing, and logistical implications of its management (Laney and White 2014). As just one

186 Section III • IT-Enabled Innovation

example, governance will need to determine which data can be exposed to the public, and this decision in turn will affect all other aspects of governance. In addition, com- panies need to understand their tolerances for risk and experimentation and develop appropriate governance mechanisms to determine whether the risks involved in any social media/big data initiative are appropriate for their organization.

a Business Strategy for data

Increasingly, companies are demanding more and better information to meet their needs (Redman 2013). To obtain it however, companies must first recognize that new big data/social media technologies have the capacity to significantly redefine busi- ness models and they will therefore need a business strategy for how to manage what is done with them (Fitzgerald et al. 2014). “These could be dis-intermediating tech- nologies,” said one manager. “We’re at a critical juncture as companies are beginning to build strong relationships with their customers.” Although many executives fear learning what their customers are saying about their company and their products and services, taking this step can be a strategic differentiator for an organization. Similarly, improved insights gleaned from other types of data can also radically transform how a business operates (Davenport 2013). Companies should start strategizing by asking relevant business questions that address key value levers, such as what are the biggest drivers of our profits? Or, how can we increase customer loyalty? Then, indicators can be developed and key data collected. For example, one focus group firm is developing a consolidated view of its customers using structured and unstructured data from both internal and external sources because it felt that knowing more about its customers would help it target products and services more effectively to them.

Data can also be used to drive the development of strategy after it has been collected. However, this can only occur if useful information is developed that is used by the orga- nization (Marchand et al. 2000; Marchand and Peppard 2013). Although social media is a marketing tool, it is also extremely important for a business to have the capability to use the data that are generated from it to inform decision making and strategy development

Governance

Business Strategy for

Data

Social Media and Big Data

Use

New Skills and Tools

Business Value

Improved Data and

Information Capabilities

Figure 13.2 Components of a New Organizational Capability for Big Data

Chapter 13 • Big Data and Social Computing 187

over time. Companies should therefore ask, do we have information that is easy to use? and is it useful? This means working with IT to embed insights into business processes and make them more understandable and actionable through a variety of methods such as dashboards, visualization, trend analysis and simulations, and traditional reports, and then validating their usefulness with the business (LaValle et al. 2011).

Better data capabilities

Data have four dimensions (Marchand et al. 2000):

• Unstructured, such as that gained through social media. • Structured, such as that found in databases. • Internal data, information, and knowledge that are found within an organization. • External sources of data or information from outside the company, such as customer

comments, external databases, or sensor data.

Improving big data capabilities involves collecting more data from different data sources to gain a more complete view of customers, supply chains, or other strategic situations. Determining what data to collect and how to get it is an organization’s first challenge. Here, the goal is to transition from siloed data, supporting siloed processes and decisions acting on a partial awareness, to integrated data (both internal and from  social media) that will provide a 360° understanding of an entity or a situation (Austin et al. 2006; Davenport et al. 2012).

A second challenge is how best to organize data and capture context and meaning in order to get to the most useful insights. Although big data tools increase the vol- ume and velocity of data available and reduce the costs involved, companies must still decide how to dissect it to turn data into insights (Beath et al. 2012). “Simply making data available is no guarantee of value. Organizations need data context, centers of excellence, and governance to manage it properly,” said one manager.

Furthermore, most companies still have much room for improvement in structu- ring their data and analytics capabilities, said the focus group. For example, it is still often unclear where in the organization these activities are best performed. In some firms, IT has this responsibility; in others it is an enterprise service or divided among the business units. Such pockets of data capability in different places can detract from what an organization is able to do with data.

Research shows there are three levels of analytics maturity in organizations (Kiron and Shockley 2011; LaValle et al. 2011):

1. Aspirational. At this level, analytics are siloed and largely based on structured data and the use of spreadsheets. Typically, these support targeted activities such as finance and supply chain management.

2. Experienced. More mature companies also use visualization, advanced modeling, and data integration to support more holistic strategy development and marketing and operations activities.

3. Transformational. At this level, firms use a broad portfolio of tools to analyze integrated structured and unstructured data to support day-to-day strategy and operations in a planned and coordinated fashion.

Most companies today are between the first two levels, but the field is moving rap- idly (Kiron and Shockley 2011). Much of what is “emerging” today will be mainstream

188 Section III • IT-Enabled Innovation

in a very few years, so it’s important for companies to be ready for this by ensuring they learn how to think about data, develop more discipline about collecting data, experiment with analytics models, and change corporate culture to enable some risk as business models evolve.

new Skills and Tools

Although tools are a necessary component of building new data capabilities in an orga- nization, improving skills is largely an organizational challenge (Austin et al. 2006; Kiron and Shockley 2011). Internally, IT’s data skills are often separated into three dif- ferent organizational groups that have operated as silos, the focus group explained. Operations have been responsible for speed of delivery, back-up and recovery, 24×7 support, uptime, security and compliance, and process. Decision support has been responsible for number crunching, visualization, metrics, ad hoc requirements, sand- boxes, and subject matter expertise. And knowledge and content management has been responsible for tagging, taxonomy, search, incentives, work routines, and knowl- edge. Today, these three skill sets are converging and ensuring they intersect appropri- ately is essential to leveraging an organization’s existing tools.

However, companies will likely also need to hire and develop IT people who can create value with data and existing IT skills will have to change as well (LaValle et al. 2011). Initially, technical skills will be needed to architect, select, and implement the most appropriate new technologies. Following this phase, data sources need to be identified, collected, and prepared before analytics and other types of information delivery activities can be developed. In this step, it is critical to have people with a combination of business, analytics, and data skills, who are not isolated from the busi- ness. Although hiring more data scientists is part of the solution, “the bigger problem is that we lack the managers and analysts who can ensure that big data can be effectively consumed and used by organizations,” said one manager. “These people need a very broad skill set, ranging from communication to business knowledge to technical and data knowledge.”

The effective use of social media data and analytics to deliver value also requires a tighter integration of business unit and IT functions (Maoz et al. 2013). Business units will also need specialized staff who work closely with IT to develop applications and learn, tightly cycling through the iterative development and implementation of new products as services, as insights are gained. These specialized business unit staff should have considerable technical and analytic skills but should not be viewed as a “shadow IT group,” but rather a new type of business professional who delivers important data and ideas to business and IT leaders. Therefore, increasingly there will be a growing gradation in staff skills between business and IT with the people in the middle skilled in both technology and business, said the focus group.

Overall, companies should have three specific sets of competencies for dealing with big data (Laney and White 2014; McAfee and Brynjolfsson 2012):

1. Information management expertise. This includes data governance, good data management practices, and the ability to deliver the right data to the right people.

2. Business analytic expertise. This is the analytic talent, tools, and technology needed to deliver insights from data.

Chapter 13 • Big Data and Social Computing 189

3. An analytic-oriented culture. This is a broad organizational belief that data and analytics are a strategic asset. It includes analytics champions, a mandate, and us of insights for both strategic and tactical decisions.

innovaTing wiTh Big daTa

In addition to these fundamental components of delivering business value with data, leaders are also looking for IT to help them innovate with data (Fitzgerald et al. 2014; Kruschwitz 2011). We are just beginning to recognize that there are data external to the organization, in addition to social media data, that can be used to generate new and entirely different sources of value for companies (Piccoli and Pigni 2012). In strategiz- ing about how to take full advantage of the internal and social media data they already have, business and IT should also be exploring how best to leverage these external data sets. This process begins by asking five questions:

1. Do we know what data people have socialized around our business and our product?

2. Do we have an inventory of the data streams in our ecosystem and those sur- rounding us?

3. Have we thought about the data streams we produce? Could they be valuable outside our organization?

4. How many of our organizational systems could be architected easily to provide data in real time?

5. Are we keeping an eye on the changing value of our digital assets?

The answers to these questions can then be used to develop new strategic oppor- tunities for organizations with external data (Piccoli and Pigni 2012). These include the following:

1. Data generation. Many firms generate data that can be used by others to create new products or services. For example, TripIt taps into a variety of travel data streams, such as reservations made with airlines, hotel and car rental agencies, and integrates confir- mations into a master itinerary for a traveler or a group of travelers. The company is seeking to be the home base for all of a consumer’s travel information.

2. Aggregation. Here, a firm identifies and harvests a variety of data streams, which are then repurposed and made available to potential users, thereby creating a data platform. For example, Socrata is a platform for government agencies and provides access to public, real-time data in a one-stop shop.

3. Service. Here, a firm uses data to create new services for consumers or to improve service quality. For example, Mycityway is a real-time app designed to help users navigate an urban environment. Integrating over 100 real-time feeds, it helps one find a type of restaurant, a wireless hotspot, buy tickets, connect with other users, or check live traffic feeds.

4. Efficiency. A firm can also use data streams to optimize internal operations, such as waste reduction. For example, Trafikanten in Norway uses real-time feeds to locate buses and optimize traffic lights, as well as inform customers when their bus will arrive. It has generated 15 percent more bus efficiency as a result, while providing a new customer service.

190 Section III • IT-Enabled Innovation

5. Analytics. Companies are using a variety of data to develop superior insight or knowledge. For example, Mint brings a person’s financial accounts together from a variety of sources and automatically categorizes transactions, and helps set budgets and develop savings goals.

Once in place, companies can leverage several of these approaches at the same time or shift between them as their understanding matures.

pulling in Two diFFerenT direcTionS: The challenge For iT ManagerS

As is so often the case with new technologies, IT managers feel torn between their everyday reality and the glamorous and dynamic vision of the future as painted by the proponents of big data and social computing (Spanbauer 2006). Focus group participants were concerned about how demands for new information and ways of working would mesh with their ongoing responsibilities of managing an efficient and effective IT organization. “Social computing is a challenge in our locked down environment,” said one. Another noted, “Our information security principles conflict with it. There are some things we don’t want hitting the 6 o’clock news.” Similarly, big data use requires opening up established and structured organizational processes to a wide variety of data sources, collaborating more extensively with business and enabling flexible and transient applications and information (Davenport 2013; Smith and McKeen 2007).

“We’re being pulled in two directions by these trends. We need to change,” said one manager, “but we also need to protect our corporate assets. We really need to develop policies for how to do these things properly.” They saw their biggest challenge for social computing and accessing external data streams as security and protecting the reliability of the infrastructure they have built up. “If the security issue was addressed, we’d see some of these things as much more acceptable,” said another manager. With big data, the challenges involve rethinking how data management is done, speeding up IT analysis work, and redesigning business processes to be more data-driven, rather than process-driven. Table 13.1 summarizes the vision of social computing and big data and contrasts it with the challenges it poses to IT management.

Some of their other challenges include the following:

• Short business horizons. As has often been the case in the past, business leaders have a much shorter time horizon in their thinking than IT and are often not pre- pared to anticipate or explore new technologies and their implications that might be important in the future. Then, when the technology hits public awareness, they want it yesterday! “We have no active support for social computing or big data,” said one manager. “It’s very hard for the business to see its value as yet.” Yet, in some cases, business users see IT as holding them back because of security and regulatory consid- erations. “We need to work together with the business to identify the risks associated with these new ways of working and protect our operational processes,” said another. “And we need to make sure the decision-makers understand what’s involved in becoming more open and information-oriented.”

• Resources. Social computing is touted as an effective collaboration and innova- tion tool but using it for this purpose requires support and facilitation. “Our staff

Chapter 13 • Big Data and Social Computing 191

is maxed out at present,” said a manager. “If we go down this road, we need to commit resources to doing it properly.” Similarly there must be business support for incorporating new ways to utilize big data. This involves more than just adding a few data scientists but, as noted earlier, requires top-down attention to think- ing about, using, and making decisions with data. Even in those companies that are actively promoting these changes, getting the right resources in both business and IT is a challenge. “And when we’re stressed, we revert to our old behaviors,” explained a participant.

• Changing the culture. IT managers recognize that organizational behavior must change if the value of these tools is to be realized. However, changing embed- ded cultural practices is often extremely difficult. Even where there is a strong emphasis on making information and people more accessible, champions are needed to make sure “we don’t slip back into our comfortable ways of behaving,”

Table 13.1 The Challenges of big Data and Social Computing from an IT Manager’s Perspective

The Vision The IT Manager’s Challenge

Blurred process and organizational boundaries

Firewalls and structured processes

Collaboration and sharing both internally and externally

Intellectual property and privacy protection; formalized external engagement

Situational applications Maintaining transactional applications and operational integrity

Mass participation and accessibility Authentication and authorization

Data orientation Process orientation

Transient information (i.e., systems of engagement)

Creating a permanent record (i.e., systems of record)

Support for social behavior Support for business behavior

Innovation and creativity Efficient use of resources

Viral Secure

Dynamic Backup

Situational roles Regulatory accountabilities

Date governance and etiquette Project governance and policy

Collective intelligence; bottom up innovation; empowerment with data

Top down business strategy

Emergent value Defined business value based on a business case

Data discovery and exploration Managed data environments

Anywhere, anytime connectivity Controlled communication

Ad hoc applications and inquiries Scalable applications

192 Section III • IT-Enabled Innovation

agreed the focus group. For example, some organizations have tried experi- ments with more social ways of working with and sharing information but have found that while the adoption rate is initially high, the drop off in participation is equally steep. This is consistent with the challenges KM managers faced in the past, which effectively killed this function in most organizations. The question for many (and which remains unanswered) is whether these tools will be able to drive the behavioral and cultural changes needed to make the technology effective (Spanbauer 2006; Smith and McKeen 2007).

FirST STepS For iT leaderS

Established mental models, business models, and systems can be serious inhibitors to the new ways of working implied by social media and big data. Discontinuous change requires thinking about needs differently and envisioning what is possible. As Henry Ford once said, “If I had asked people what they wanted, they could have said ‘faster horses’.” At that time, few would have imagined the automobile and its industry and infrastructure as it is today. Getting the mindset and model right involves much change, such as obtaining data from multiple sources, making sense of huge amounts of data, developing complex analytics algorithms, and dealing with cultural objections to standardized data. IT leaders should begin the change process by asking themselves a number of questions including the following:

• How can we attract, grow, and retain employees with the skills we will need? • What data do we need and what is the optimal way to collect and manage the mas-

sive amounts of structured and unstructured data involved? • How can we best support varied and dynamic business needs for information more

rapidly?

The focus group believed it is not necessary to spend large amounts of money to demonstrate the value of these new approaches. “Expensive analytics projects are not required to get started with big data,” said one manager. “Companies should start small and focus on proving value at each step.” He noted that it is possible to begin inex- pensively with open systems, which are scalable and require no licenses. “While you wouldn’t want to run an entire enterprise this way, you can start small and then add variables and improve your models,” he said.

Big data technologies can coexist with existing data warehouses and so can be introduced slowly, replacing specific storage and computing scenarios over time. “Start with the basics to build competencies, reduce processing, and take care of the mundane, and then grow,” recommended one manager. As a company gets some quick wins, it will be more willing to develop pilot use cases for enterprise value realization. “As you move up the maturity curve, you will be able to figure out value optimization with big data,” he added.

There are still many immediate big data and social media issues that need to be considered. These include immature technologies, legal and regulatory consider- ations, ownership of data quality, expectation management, establishing an effective organization structure, and optimal utilization of specialized resources. Privacy and data quality are also critical issues that must be properly managed if these initiatives

Chapter 13 • Big Data and Social Computing 193

are going to succeed. The IT managers groups collectively had the following recom- mendations for IT leaders:

1. Focus. Identify specific problems and then use data and/or social media to solve them. “If we just look at generic opportunities, the scope can be overwhelm- ing,” said one manager. Leaders should look for the biggest play they can get, either on the top or bottom line. “Start tactically and use success stories to illus- trate how social media/big data can fit into your organizational strategy,” they recommended.

2. Develop business-savvy IT staff and encourage development practices such as shadowing and colocation. Tap into your own expertise, promote business–IT rotation programs, and hire power users into IT. Colocating business intelligence delivery groups from IT in the business units and developing a business-led governance structure for data and social media prioritization projects are best practices. These steps will enable IT to focus on foundational components such as, standards, metadata, and data models, while business can focus on delivering intelligence.

3. Become a “data factory” with supportive methodologies and practices and an optimized ecosystem of advanced and traditional data technologies. Work to improve data quality, usability, and integration. Clarify responsibilities for data and manage the conflicts between security, privacy, and compliance requirements and information delivery. Finally, CIOs should consider reorganizing to facilitate the convergence of operational with decision support data, and unstructured with structured data.

4. Listening and engaging. Ensure your company is listening to its customers and others to find out their concerns and interests. Build deliverables that will engage customers with the company and provide superior customer service. Identify “killer apps” and highlight their value and relevance to customers.

5. Consider hiring a graphic designer. This will support IT in developing intuitive and easy interface designs and efforts to move to mobile devices.

6. Support actions that improve use. Communicate the link between use and value to keep teams focused on usefulness and ease of use in social media/big data applications.

Today, many organizations are thinking about how to use social technologies and new forms of data to change the products and ser- vices we use daily. Over the next few years, they will create new information platforms on which ideas that we never dreamed of will surface. Social media and the data they generate are still immature as are other new types of data and companies should therefore

adopt them in an evolutionary fashion rather than in a “big bang.” However, they cannot be ignored because they are going to be a part of every business. The question is, how big? The key to success is learning how to manage and think about data in an evolutionary way. If companies don’t begin, they won’t know what they can leverage and risk being disin- termediated by those that are willing to try.

Conclusion

194 Section III • IT-Enabled Innovation

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