The amount of data in our world has been exploding, and analyzing large data sets—so-called big data— will become a key basis of competition, underpinning new waves of productivity growth, innovation, and consumer surplus, according to research by MGI and McKinsey's Business Technology Office. Leaders in every sector will have to grapple with the implications of big data, not just a few data-oriented managers. The increasing volume and detail of information captured by enterprises, the rise of multimedia, social media, and the Internet of Things will fuel exponential growth in data for the foreseeable future.

Big data: The next frontier for innovation, competition, and productivity

Even though considerable attention has been given to the polarity of words (positive and negative) and the creation of large polarity lexicons, research in emotion analysis has had to rely on limited and small emotion lexicons. In this paper, we show how the combined strength and wisdom of the crowds can be used to generate a large, high-quality, word–emotion and word–polarity association lexicon quickly and inexpensively. We enumerate the challenges in emotion annotation in a crowdsourcing scenario and propose solutions to address them. Most notably, in addition to questions about emotions associated with terms, we show how the inclusion of a word choice question can discourage malicious data entry, help to identify instances where the annotator may not be familiar with the target term (allowing us to reject such annotations), and help to obtain annotations at sense level (rather than at word level). We conducted experiments on how to formulate the emotion-annotation questions, and show that asking if a term is associated with an emotion leads to markedly higher interannotator agreement than that obtained by asking if a term evokes an emotion.

https://svn.spraakdata.gu.se/clt/retorik/nrc/NRC-Emotion-Lexicon-v0.92/1308.6297.pdf

Crowdsourcing a word–emotion association lexicon

https://bura.brunel.ac.uk/bitstream/2438/13152/4/Fulltext.pdf

Critical analysis of Big Data challenges and analytical methods

Even though considerable attention has been given to the polarity of words (positive and negative) and the creation of large polarity lexicons, research in emotion analysis has had to rely on limited and small emotion lexicons. In this paper we show how the combined strength and wisdom of the crowds can be used to generate a large, high-quality, word-emotion and word-polarity association lexicon quickly and inexpensively. We enumerate the challenges in emotion annotation in a crowdsourcing scenario and propose solutions to address them. Most notably, in addition to questions about emotions associated with terms, we show how the inclusion of a word choice question can discourage malicious data entry, help identify instances where the annotator may not be familiar with the target term (allowing us to reject such annotations), and help obtain annotations at sense level (rather than at word level). We conducted experiments on how to formulate the emotion-annotation questions, and show that asking if a term is associated with an emotion leads to markedly higher inter-annotator agreement than that obtained by asking if a term evokes an emotion.

Crowdsourcing a Word-Emotion Association Lexicon

https://ro.uow.edu.au/cgi/viewcontent.cgi?article=1983&context=buspapers

Big data analytics and firm performance: Effects of dynamic capabilities

You have more information at hand about your business environment than ever before. But are you using it to "out-think" your rivals? If not, you may be missing out on a potent competitive tool. In "Competing on Analytics: The New Science of Winning" , Thomas H. Davenport and Jeanne G. Harris argue that the frontier for using data to make decisions has shifted dramatically. Certain high-performing enterprises are now building their competitive strategies around data-driven insights that in turn generate impressive business results. Their secret weapon: Analytics: sophisticated quantitative and statistical analysis and predictive modeling. Exemplars of analytics are using new tools to identify their most profitable customers and offer them the right price, to accelerate product innovation, to optimize supply chains, and to identify the true drivers of financial performance. A wealth of examples - from organizations as diverse as Amazon, Barclay's, Capital One, Harrah's, Procter & Gamble, Wachovia, and the Boston Red Sox - illuminate how to leverage the power of analytics.

Competing on Analytics: The New Science of Winning

Table of Contents Preface and Acknowledgements Chapter 1 What It Means to Put Analytics to Work Part 1 The Analytical DELTA Chapter 2 Data Chapter 3 Enterprise Chapter 4 Leadership Chapter 5 Targets Chapter 6 Analysts Part 2 Staying Analytical Chapter 7 Embedding Analytics in Business Processes Chapter 8 Build an Analytical Culture Chapter 9 Review Comprehensively Chapter 10 The Analytical Journey: Meeting Challenges Along the Way Chapter 11 Toward More Analytical Decisions and Better Results Appendix The DELTA Journey

Analytics at Work: Smarter Decisions, Better Results

Enterprise systems packages have long been associated with process change. However, it was assumed that most organizations would simultaneously design and implement process change while implementing the systems. A survey of 163 organizations and detailed interviews with 28 more suggests that enterprise systems were still being implemented even among early adopters of the technology, and that process change was being undertaken on an ongoing basis. After the prerequisites of time, critical mass of functionality, and significant expenditures were taken care of, the factors most associated with achieving value from enterprise systems were integration, process optimization, and use of enterprise‐systems data in decision making.

Enterprise systems and ongoing process change

Many firms know about their customers, but few know the customers themselves or how to get new ones. Leaders in customer-knowledge management go beyond transaction data, using a mix of techniques, and they aren't afraid to tackle difficult problems.

Davenport, director of the Institute for Strategic Change, Accenture (formerly Andersen Consulting) and coauthors Harris, also from Accenture, and Kohli, professor of marketing at Emory University, report results from interviews with 24 leading firms and describe seven practices that the leaders share. The companies interviewed ? including Harley-Davidson, Procter & Gamble, and Wachovia Bank ? have undertaken specific and successful initiatives centered around the management of customer knowledge.

Within the seven practices, two results stand out: First, firms are beginning to rely more on data from actual interactions, such as sales and service. They are learning that customers are more than transactions, and they are seeking creative ways to turn data from these interactions ? human data ? into knowledge. Second, even the most ambitious firms are keeping data from different approaches separate. They are not accepting the notion of an integrated data repository.

Focus on the most valued customers. Know which customers are worth the organization's resources. 

Prioritize objectives. Successful firms begin all customer-knowledge management initiatives by prioritizing business strategies and customer-relationship objectives. They know which customers to focus on and what new behaviors the customers should exhibit.

Aim for the optimal knowledge mix. There's no single solution to knowledge management. Use a variety of approaches. 

Don't use one repository for all data. The fully integrated customer-knowledge environment seems more an intriguing idea than a practical reality. Diverse forms of information are difficult to combine in one set of database records, and firms risk having a departing employee walk away with highly developed knowledge. Consequently, customer data is fragmented across multiple systems and locations. No one has been able to combine hard (transaction-based) and soft knowledge in one customer database.

Think creatively about human knowledge. This is the main practice that separates the leaders from the laggards. We saw many creative solutions to managing both explicit (documented and accessible) and tacit (understood but undocumented and not accessible) knowledge.

Look at the broader context. Customer-knowledge initiatives do not exist in a vacuum. Their success depends on the organization's roles and responsibilities, the workplace culture, and the organizational structure.

Establish a process and tools. Many firms seem to stop working when they've selected a management strategy ? avoiding the planning that is critical to implementation. The leading firms work hard to deliberately manage customer knowledge, using a defined process and creating tools as needed.

How do they know their customers so well

Data remains one of our most abundant yet under-utilized resources. This article provides a holistic framework that will help companies maximize this resource. It outlines the elements necessary to transform data into knowledge and then into business results. Managers must understand that human elements—strategy, skills, culture—need to be attended to in addition to technology. This article examines the experiences of over 20 companies that were successful in their data-to-knowledge efforts. It identifies the critical success factors that must be present in any data-toknowledge initiative and offers advice for companies seeking to build a robust analytic capability.

Jeanne G. Harris

Papers

Competing on Analytics: The New Science of Winning

Analytics at Work: Smarter Decisions, Better Results

Enterprise systems and ongoing process change

How do they know their customers so well

Data to Knowledge to Results: Building an Analytic Capability