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Chapter 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 generate 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 content 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 integration of big data and social media concepts to deliver value. It begins by discussing the 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.

Figure 13.1 The Relationship Between Big Data and Social Media

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 companies are using the data generated by these tools (Fitzgerald et al. 2014). This underscores 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 interactions 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).

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

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 consolidated. 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 segmentations 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 management 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 frequently 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 combine 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 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 contextualized 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 propositions 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 components 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; others 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 information 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 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, companies 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.

Figure 13.2 Components of a New Organizational Capability for Big Data

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 business 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 technologies,” 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 organization (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 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 volume 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 structuring 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 rapidly (Kiron and Shockley 2011). Much of what is “emerging” today will be mainstream 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 organization, 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 different 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, sandboxes, and subject matter expertise. And knowledge and content management has been responsible for tagging, taxonomy, search, incentives, work routines, and knowledge. Today, these three skill sets are converging and ensuring they intersect appropriately 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 business. 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.

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 strategizing 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 surrounding 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 opportunities 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 confirmations 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.

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 prepared 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 considerations. “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 innovation tool but using it for this purpose requires support and facilitation. “Our staff 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 thinking 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.

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

Changing the culture. IT managers recognize that organizational behavior must change if the value of these tools is to be realized. However, changing embedded 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,” agreed the focus group. For example, some organizations have tried experiments 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 massive 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 inexpensively 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 considerations, 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 are going to succeed. The IT managers groups collectively had the following recommendations 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 overwhelming,” 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 illustrate 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.

Conclusion

Today, many organizations are thinking about how to use social technologies and new forms of data to change the products and services 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 disintermediated by those that are willing to try.

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