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Chapter 9
Information Technology

Although the use of information technology (IT) is not a focal point of the integrated reporting movement's conversation today, it should be. IT, which involves the “development, maintenance, and use of computer systems, software, and networks for the processing and distribution of data,”¹ poses a major challenge for integrated reporting. Yet it is also an opportunity. Not only can IT dramatically improve the process and quality of integrated reporting to the benefit of both the company and its audience, it can also improve both parties' integrated thinking.

To understand how this can be accomplished, corporate reporting in general and integrated reporting in particular must be considered in the context of four technological trends sweeping the business world today: big data, analytics, cloud computing, and social media. Companies have rightly focused on how these technologies can support and transform their business model. Virtually no attention, however, has been given to their application in integrated reporting. We believe that should—and will—change. Until senior management gives proper consideration to how to leverage IT for corporate disclosure, the full promise of integrated reporting (<IR>) and integrated thinking (<IT>) will not be achieved. The previous two chapters' analysis supports this contention. As shown in Chapter 7, paper-based reports have severe inherent limitations and, as shown in Chapter 8, the corporate reporting websites of the 500 largest companies in the world today only scratch the surface when it comes to using currently available IT. To put IT more directly into the movement's conversation about integrated reporting, we have devoted an entire chapter of the book to this topic.²

We will begin by explaining how existing IT can be used to support the processes required for integrated reporting. Emphasizing the role intelligent, machine-readable data will play in the not-too-distant future, we review the four trends and how they might contribute to <IR> and <IT>, ultimately introducing the concept of “contextual reporting”—a kind of reporting in which any single piece of information is easily viewed in the context of the “big picture.” The chapter concludes with a brief glimpse into the future of integrated reporting with the hypothetical company World Market Basket.

Integrated Reporting Processes

Used properly, IT, along with supporting internal control systems, can play a major role in the support of integrated thinking and integrated reporting. But this can only be accomplished if the company has a clear strategy for how to use IT to support its fundamental business processes. In an integrated reporting context, these processes are identification, validation, analysis, audience filtering, publishing to internal audiences, and publishing to external audiences (Figure 9.1).³

Identification

Integrated thinking inside a company is contingent on management's access to information about business processes, their outcomes, and the positive and negative externalities created as the company uses the International Integrated Reporting Council's (IIRC's) six capitals to create value for its shareholders. This information exists in at least three different forms: narrative or story format, structured information, and unstructured information. Given the broad scope and holistic nature of integrated reporting, the information that drives <IT> and <IR> could come from any unit in the company or outside the boundaries of the enterprise, including its suppliers, customers, business and partners, nongovernmental organizations (NGOs), and members of civil society. For this reason, it is necessary to identify relevant sources of information and, when it is not available, use proxies or develop new sources.

While a company's enterprise resource planning (ERP) system⁴ offers one major source of information, others include employee spreadsheets, online databases, and social media platforms like Twitter. The ERP's carefully structured information usually relates to the tracking of transactional data. As they exist now, these systems are limited in their ability to track the sort of nonfinancial information integrated reporting requires. Much of it is unstructured; it is not neatly organized within the company's ERP system or any other information system that has conventions to which the data must adhere. The challenge for IT is to pull together the structured and unstructured information that comprises the content elements in the International <IR> Framework (<IR> Framework), which, taken together through the Framework's guiding principles, create the narrative backbone of an integrated report.

Validation

While audit and validation processes for financial reporting have been in place for a very long time, they are largely still immature when it comes to nonfinancial information. As a result, report producers and consumers alike are justifiably suspicious of the reliability of the nonfinancial information they report on and use. IT can provide a greater degree of comfort regarding the quality of information being used in integrated reporting by ensuring that there is a single source of truth—that all who consume information inside the company acquire it from a single, systemic source, like a relational database, a data warehouse, or virtual cloud solution. This will reduce errors like the incorrect transposition of numbers or the loss of connection between narrative reporting and the underlying data. IT solutions that deliver a single source of truth are available, but their implementation requires a good alignment between business and IT processes inside the company.

Analysis

The key to analysis is to use information from internal and external sources to link the content elements—such as strategy and resource allocation, governance, performance, and outlook—in a meaningful way. IT tools for analysis are becoming increasingly available, many for a low cost or even free. Earlier analytical systems focused on transactional data, and subsequent ones then incorporated information tucked away in relational databases.⁵ The advent of big data, however, has catalyzed the development of more sophisticated analytical tools that can use both structured and unstructured data. Discussed below, these tools can generate new insights on how different pieces of information can be understood and how they might be related to each other.

Audience Filtering

While integrated reporting is holistic by nature, not everyone needs all information all the time. As discussed in Chapter 5, what is regarded as material by each audience varies widely. Both the producer and user of information can filter it and, in both cases, numerous IT tools are available for doing so. However, filtration processes certainly need to mature as most companies using integrated reporting have not yet reached this level of sophistication in their integrated thinking processes. When availing themselves of filtering capabilities, audience members should also be conscious of the fact that information they filter out may be related to information they think is material, so users should approach the process with a certain degree of judgment.

Publishing to Internal Audiences

Once the information has gone through a “materiality filter,” it needs to be published for the relevant audience. Currently, a number of potential audiences for integrated reporting information are found inside the company, including line management and the strategic planning, performance monitoring, risk, sustainability, corporate reporting, and investor relations functions. If the information needed by each audience is available in systems or formats that enable portability, then each audience can choose to view and work with the information that is delivered to them using tools appropriate for their task. The challenge for those responsible for the provision of IT is to ensure that any time the users of integrated reporting information massage it, their action does not abstract the information from the context that gives it meaning. The IT system also needs to preserve audit trails, keep track of version control, and maintain links to underlying data sources.

Publishing to External Audiences

For external audiences, the challenge for integrated reporting is to ensure that the important content elements are crafted and honed out of the process of integrated thinking before they are delivered to external consumers of the company's reports. While traditional reports are delivered on pieces of paper (or as PDFs, the digital equivalent of pieces of paper), IT enables more powerful methods of report delivery and consumption. Already, companies are using websites to deliver digital reports that enable role-based or interest-based consumption. While concision is an important guiding principle of integrated reporting, IT can be used to supplement the information in the integrated report by providing metadata (such as through Extensible Business Reporting Language (XBRL)),⁶ context, and access to underlying data sets for those who are interested in more detail.

Four IT Trends

While use of big data, analytics, cloud computing, and social media is mainstream in the IT community, it is also pervading the broader, global business world today. The terms “big data” and “analytics” are used somewhat interchangeably because they are so closely related to each other. Analytics—looking for patterns, trends, insights, and outcomes—are performed on big data sets, but it seems that the more evocative term of big data is what has caught on and is most commonly used. Little work has been done to examine the relevance of these big trends for integrated reporting, but we think that all can be relevant.⁷

Big Data

Big data is defined as a vast quantity of structured and unstructured data from traditional and digital sources inside and outside an organization that represents a source for ongoing discovery and analysis.⁸ From the perspective of integrated reporting, its power lies in the ability to access sources of information ignored by traditional IT systems and to offer proxies for performance outcomes that are difficult to measure (e.g., the value of intellectual property or the benefits of employee engagement) or difficult to track (e.g., customer satisfaction or social impacts). When it comes to big data, companies are doing more than just talking about it; they are spending money on it. According to Gartner, big data investments in 2013 continue to increase. Compared with 2012, in which 58% of organizations surveyed were investing or planning to invest in big data technology, 64% of organizations had taken the plunge.⁹ To date, the main areas companies have addressed through big data concern customers (enhanced customer experience, new products/new business models, and more targeted marketing) and internal operations (process efficiency, cost reduction, and improved risk management).¹⁰

Big data can be both structured¹¹ (prepared according to a well-defined convention) or unstructured¹² (not prepared according to a well-defined convention). For integrated reporting, both financial and nonfinancial data are important. While they can both be structured or unstructured, nonfinancial information is more likely to be the latter. Both can be delivered in terms of three different formats, ordered from least to most useful: (1) human-readable data,¹³ (2) semiautomated data,¹⁴ and (3) intelligent, machine-readable data. The type of data format a company uses for its integrated report heavily determines how quickly, accurately, and cost-effectively a company and its audience can use that information to make decisions.

The most useful, accurate, efficient, and cost-effective form of data is intelligent, machine-readable data. Intelligent data has built-in validation rules, calculations, and formulas that verify its accuracy. It can also be linked to other data or narrative information in order to illustrate its relationships and interdependencies. The latter is important for fostering integrated thinking because it makes it easy for the user to see how one piece of information is related to others. Finally, it contains tags of “metadata”¹⁵ (data about data) that point to other useful information, such as the accounting standard on which the information is based if it is financial information, or the standard or method of calculation used if it is nonfinancial information. Intelligent, machine-readable data means that almost no human intervention is necessary to work with it: the data go directly from the entity's machine that produces it to the entity's machine that consumes it.

To enhance its utility and value while reducing the amount of manipulation and risk of error later in its life cycle, data should be created as intelligent, machine-readable from the outset. XBRL, a proven global technology for making business information machine-readable, offers one way to accomplish this. As only a handful of the world's largest companies currently provide data in this format for their annual reports on their websites, this is an area of immense opportunity for companies to improve their corporate reporting and their integrated reporting.

Analytics

Analytics helps companies identify relationships between financial and nonfinancial performance across functions, operating divisions, and their supply chain to provide greater understanding of the “connectivity of information” in support of integrated thinking. Broadly speaking, big data analytics has four basic types of applications: (1) descriptive analytics¹⁶ for hindsight or understanding what happened, (2) diagnostic analytics¹⁷ for insight why and how it happened, (3) predictive analytics¹⁸ for foresight or understanding about what could happen, and (4) prescriptive analytics for understanding what should happen.¹⁹ The extent to which they create value for the business and foster integrated thinking varies (Figure 9.2). In all cases, the greater the degree to which the input is intelligent, machine-readable data, the greater the power and flexibility of the analytics will be to support integrated thinking on the part of both the company and its audience. Companies typically begin with descriptive analytics, to which they add diagnostic analytics, and ultimately predictive and prescriptive analytics, building from one application to the next as the company gains experience with this IT. Predictive and prescriptive analytics are today's big data “frontier.”

Cloud Computing

Cloud computing, in which a wide variety of business functions are performed on dispersed servers in a secure, on-demand, capacity-sharing, and scalable manner available from wherever an Internet connection exists, is an increasingly important way to perform analytics on big data. In the Gartner study, cloud computing was cited by 41% of participants as the single most popular information technology for deriving value from big data.²⁰ Cloud computing is also regarded as one of the most effective ways of encouraging collaboration—which itself fosters integrated thinking—across functions, geographies, time zones, and organizational boundaries.²¹

Social Media

Social media, which enables individuals to share information and communicate with each other and the company on a real-time basis, from anywhere in the world, on platforms like LinkedIn, Facebook, Twitter, and Google+ is an increasingly important source of big data. Through it, companies can access the perspectives of employees and customers, which can be used in an integrated report. It can also help foster more robust integrated thinking, as humans have a natural tendency to see an issue through the lens of their knowledge and experience, often without the full context in which it resides. When people share these perspectives, all of them develop a more complete picture of the causes of outcomes they care about and what can be done to improve them.

Leveraging These Trends

There is no reason why companies and their audiences cannot use big data and analytics with cloud computing and social media to improve the creation, distribution, and consumption of integrated reports. Most simply have yet to do so—in our view, because compliance and filing requirements in a largely regulatory-driven corporate reporting world have reinforced a paper-based paradigm for decades. When the power and collaborative benefits of cloud computing are brought to bear on big data analytics' applications, using information generated from many different sources, companies can significantly improve their integrated reporting and integrated thinking.

The Gartner report cited above identified the types of data analyzed. Most common were transactions (cited by 70% of respondents), log data²² (55%), machine or sensor data (42%), emails/documents (36%), social media data (32%), free-form text (26%), geospatial data (23%), images (16%), video (9%), audio (6%), and others (12%).²³ Virtually all of these types of data are or can be used in assembling an integrated report. Social media in particular offers a two-way information channel for companies; they can monitor websites to see what employees, customers, and NGOs are saying about them in order to generate data relevant to human and social and relationship capitals, as well as to communicate their integrated report to these audiences.

Contextual Reporting

Connectivity, a key guiding principle in integrated reporting, comes from the mutually reinforcing relationship between integrated thinking and integrated reporting. In enabling both <IT> and <IR>, IT can help the company understand and report on the links between the content elements of the company's value creation story. IT can play a similar role for the audience of a company's integrated report. Once published, the integrated report becomes context for the user. Beyond simply being a report, it is a means of providing access to underlying data sets that provide more detailed information than is contained in the integrated report. Conversely, when a company has published an integrated report, users who access information from outside the report from another source can trace it back to the larger context of the integrated report. We call this technology-enabled “two-way street” between an integrated report and specific pieces of information “contextual reporting” (Figure 9.3). Without the appropriate IT, an integrated report is simply a very useful report. With the appropriate IT, it becomes a vehicle for enabling the user to deepen their own understanding of connectivity in terms of the topics that are of interest to them.

Corporate reporting today supplies vast volumes of information, often made available to users via online methods, such as a data terminal, but it often lacks context. These terminals offer the user news, market prices, and messages, in addition to company data. What this plethora of information typically lacks is context regarding a company's strategy, its business model, and its understanding of the risks and opportunities it faces—something an integrated report can provide. With IT embedded in the report, the user can link disparate pieces of information. Conversely, relying solely on the integrated report without the additional insight provided by the underlying data can also result in an incorrect or limited understanding.

Ideally, we need both: large and disparate sets of structured and unstructured data, with the linking apparatus of the integrated report. This way, the user who starts with the integrated report can find the data relevant to the content elements of interest to them, while the user who starts with the data can locate the context for that data via the integrated report. Such an approach would add contextual value to the typical user of the data terminal and the rigor of multiple streams of data to the typical consumer of narrative reporting.

Bringing contextual reporting into existence will require standards (e.g., in the definition of electronic reporting formats, as is being discussed in the European Union's Transparency Directive),²⁴ the application of big data analytical methods, and the integration of digital reporting information with other forms of corporate information. It also potentially challenges the notion that integrated reporting lacks detail because of its focus on brevity. In effect, the integrated report becomes a concise contextual map that points to a rich load of information that can be found beneath the ground for both internal and external users of the integrated report. Without the use of technology, the capability for integrated reporting to provide context and connectivity is limited.

(World Market Basket)

We will conclude this chapter with a short scenario of the 2022 integrated reporting practices of World Market Basket (WMB), a hypothetical Chinese company based in Shanghai that has annual revenues of $225 billion. WMB is a global manufacturer and distributor of food products, both through its 8,000 retail stores—located in Asia, Europe, the Americas, and Canada—and online (from anywhere in the world). Listed on both the Shanghai and New York stock exchanges, WMB has a market cap of $165 billion due to its high margins and growth rate, making it one of the largest 50 companies in the world in terms of market cap. In its 2022 “Statement of Significant Audiences and Materiality” (found in the “Description of Business” section of the company's Form 20-F), the board notes that the company's financial objectives and executive compensation are based on five-year targets. It also notes that its most significant audiences are long-term investors (those which have held the company's stock for three years or more), the more than 100,000 farmers (both company employees and independents) located all over the world from which it sources its products, and, for the first time, its “Big Basket” customers. In its 2021 Statement, the board simply said, “customers,” but it made this change when “Big Basket” customers, representing 5% of the company's 175 million customers (defined by making at least one purchase in the past year), crossed the threshold to account for 80% of the company's annual sales.

Qualification as a “Big Basket” customer is based on an algorithm that reflects the amount and range of purchases within certain time periods, adjusted for local buying habits (Chinese and European customers tend to shop more frequently than North and South American ones) and for self-declared income levels, with this declaration being a requirement for achieving “Big Basket” status. Incentives to do this are great because this status results in automatic 25% discounts on all list prices, along with periodic 50% discounts only made available to them. Incentives to be honest about self-declared income levels also exist because many of the 50% discount products are geared to particular income levels. Purchases by “Big Basket” customers are a key metric included on the company's integrated reporting website. The company's integrated report is a contextual one; users are able to drill down for more detail on individual pieces of intelligent, machine-readable information. Conversely, information on WMB accessed through other sources can be linked back to the integrated report. Detailed analytical tools are also made available to the many different internal users.

Issues that are especially important to the company's audiences are so indicated on WMB's “Sustainable Value Matrix (SVM).” For example, the SVM shows that the company perceives genetically modified food (GMO) as a societally significant issue but not something that is material to the company; it is not an issue that is important to its long-term investors, its farmers, and its “Big Basket” customers. One consequence of this is that NGOs opposed to GMO food are actively campaigning against the company to modify its stance. In response, the company actively monitors social media and includes metrics of NGO perception on its integrated reporting website, available in both Chinese and English. These metrics are updated on a weekly basis. The page on which they are reported also has links to relevant articles and is an open platform for anybody, including company employees, to share their views and debate this issue with others.

The frequency with which performance metrics are updated is determined by the cycle deemed relevant by management. For example, aggregate sales are reported on a daily basis, sales to “Big Basket” customers and farming injuries on a monthly basis, and profits on a quarterly basis. Most metrics regarding material natural, human, and social and relationship capital issues are updated annually. (Assurance You Can Trust), the only China-headquartered member of the Big Five, provides a real-time integrated assurance opinion to individual data items (which can be accessed as such) through certificates that indicate which of five levels of assurance has been provided and when. Assurance for the entire website is done on a pass/fail basis every month. All assurance opinions are delivered quickly and inexpensively and are largely based on technology, with relatively little human intervention.

WMB has outsourced its integrated reporting website to a boutique IT service and consulting firm, London-based Integrated Reporting Solutions (IRS), that specializes in integrated reporting and helping companies build integrated thinking into their strategic planning process. IRS has contracts with cloud computing facility providers and has licensed big data and analytics applications that it uses to do descriptive, diagnostic, predictive, and prescriptive analyses under WMB's direction. Social media data are free and are gathered through IRS-proprietary search engines. Executives in functions spanning finance, procurement, supply chain management, marketing, and stores have access to these applications to do whatever analysis they want. also provides an assurance opinion on IRS's capabilities for its clients. To the extent humans are involved in assuring WMB's reporting, most of this effort is devoted to the scope of audit and contractual terms with IRS.

Simple versions of these analytical tools are provided for free on WMB's integrated reporting website. More sophisticated ones from third-party app providers are available for a fee. Users can download any of the data the company is reporting into these tools, integrating them into their own analytical models if they so choose. For each metric, the company provides equations specifying how this metric is related to other metrics, along with supporting data. A tool is also provided for users to create their own equations to test hypotheses about connectivity. To the extent that competitors are providing similar information, WMB's provides links to their website so that the user can download this information as well for benchmarking purposes.

The SVM is also one of the main platforms for stakeholder engagement. When users connect to WMB's integrated reporting website, they are asked to identify which type of audience member they are. (Long-term investors, farmers, and “Big Basket” customers are automatically tracked.) IRS tracks the usage patterns of website visitors in order to provide data for updating the SVM on an annual basis. All of the issues above the “Societal Issue Significance Boundary” are linked to a page for stakeholder engagement, as is done for GMO foods. This is an important input for the company in developing next year's SVM, which has a page detailing the methodology that is uses for constructing it. Each issue page also has relevant reports and studies done by WMB and other parties, such as academics and consulting firms, who give permission to post them, along with relevant videos produced by the company and its stakeholders (with approval by the company).

While WMB is a hypothetical example, all of this could be done today.

In addition to better incorporating information technology into the integrated reporting movement, there are four other pressing issues that must be addressed as well. We discuss them in our next and final chapter.

Notes

5. “A database is a means of storing information in such a way that information can be retrieved from it. In simplest terms, a relational database is one that presents information in tables with rows and columns. A table is referred to as a relation in the sense that it is a collection of objects of the same type (rows). Data in a table can be related according to common keys or concepts, and the ability to retrieve related data from a table is the basis for the term relational database. A Database Management System (DBMS) handles the way data is stored, maintained, and retrieved. In the case of a relational database, a Relational Database Management System (RDBMS) performs these tasks. DBMS as used in this book is a general term that includes RDBMS.” Oracle. The Java Tutorials, A Relational Database Overview, http://docs.oracle.com/javase/tutorial/jdbc/overview/database.html, accessed June 2014.

6. Introduced in approximately 1996, eXtensible Business Reporting Language (XBRL), is a globally adopted and freely licensed open standard for providing structure and context to information to facilitate the digital exchange of financial and nonfinancial information. “XBRL is a member of the family of languages based on eXtensible Markup Language (XML), which is also a standard for the digital exchange of information between organizations over the Internet.” Under XML, a standardized set of unique “tags” is applied to information so that it can be processed efficiently and automatically by computer software. “XBRL is a powerful and flexible version of XML that has been specifically defined to meet the requirements of business information reporting. It enables unique identifying tags to be applied to individual pieces of information, such as ‘net profit’” or tons of carbon that provide context and structure to the information, identifying whether it is a monetary item, percentage, fraction, or other form of measure. XBRL allows labels in any language to be applied to the information. It also links each piece to any relevant contextual information, like accounting or reporting framework references. XBRL can show how items are interconnected. It can also represent how they are calculated and validate the accuracy of that calculation. Most importantly, XBRL is easily extensible so organizations can adapt the standard to meet a variety of special reporting requirements unique to that organization. The rich, powerful structure of XBRL allows very efficient handling of business data by computer software. It supports all the standard tasks involved in compiling, storing, and using business information, which can be converted into XBRL by suitable mapping processes or generated automatically in XBRL by software applications. It can then be searched, selected, exchanged, and analyzed by a computer or published for human viewing. For more information, visit XBRL International's website at www.xbrl.org. The above information is excerpted from “XBRL Basics, How XBRL Works” at http://www.xbrl.org/how-xbrl-works-1, accessed June 2014. For more information about XML, please visit www.w3.org/XML/.

13. Human-readable data is information in a digital or electronic format that humans can see on a computer screen, as in a PDF document or on a website (in HTML or similar format). This is the most common way in which companies provide data today for their financial, sustainability, and integrated reports because it is an effective and inexpensive way for the company to make its report easily available to its audience. It also has its limitations, particularly when it comes to being searchable. As discussed in Chapter 7, information is typically scattered throughout the integrated report and it is difficult to locate. Although this information is easily readable by humans, it is not in an ideal form to be automatically consumed by computer software. It often requires manual manipulation, copying, and pasting into other software or spreadsheets, and this can introduce errors into the data. Typically, this information has little or no structure or context around it. Giving it sufficient structure to make it useful and meaningful is time consuming and expensive.

16. Companies venturing into the world of analytics typically begin with less complex, descriptive analytics that help the company summarize and present results regarding what has happened in their business operations. It is a way of condensing large volumes of data, perhaps dispersed in many different physical and virtual locations, in order to see patterns which, in hindsight, can be reported internally and externally. Because data are presented in a way that leaves more time for reflection, as opposed to spending that time preparing it for consumption, descriptive analytics lays the foundation for a modest degree of integrated thinking. It involves little internal collaboration or external stakeholder engagement because it is simply and ultimately about “reporting,” rather than initiating a dialogue within and outside of the company. Descriptive analytics are explained at, Advanced Software Applications Corp. “An Introduction to Descriptive and Predictive Analytics,” https://faculty.washington.edu/socha/css572winter2012/ASA_Introduction_to_Analytics.pdf, accessed May 2014.

17. Reflection on the patterns identified by descriptive analytics generates hypotheses about why and how these patterns emerged, what caused them, and relationships between them. Diagnostic analytics describe ways to test these hypotheses, enabling the company to develop insights into why things happened the way they did. Users can do the same. In both cases, insights that are obtained about cause-and-effect relationships and interdependencies improve integrated thinking. The number and quality of these hypotheses, and the insights they can generate, is a function of the degree of internal collaboration and stakeholder engagement involved in generating them. Throughout this book we have emphasized how linkages between different kinds of information, or “connectivity of information,” are essential for a company to move from a combined report to a truly integrated report. While some degree of connectivity can be obtained without diagnostic analytics, it is more difficult to do so and the possibilities are limited. For more information about diagnostic analytics see, IBM. “IBM Watson and Medical Records Text Analytics,” http://www-01.ibm.com/software/ebusiness/jstart/downloads/MRTAWatsonHIMSS.pdf, accessed May 2014.

18. The insights from diagnostic analytics form the basis for the more sophisticated predictive analytics. With predictive analytics, companies gain foresight about what could happen in the future. Forward-looking, predictive analytics utilizes a variety of statistical, modeling, data mining, and machine-learning techniques to study recent and historical data, enabling companies to make predictions about the future. Predictive analytics can forecast what could happen in the future because it looks at probabilities. It does not necessarily predict just one possible future but “multiple futures” that can be proposed based on the decision-maker's choices. Because greater insight into what stakeholders care about (e.g., what is material to them, how they might respond to a new product offering, and what they think about the company's reputation) yields more context and data points to use in the modeling, predictive analytics depend upon higher levels of internal collaboration and external stakeholder engagement to source those additional data points. For more information, see Waller, M.A. and Fawcett, S.E. (2013). “Data Science, Predictive Analytics, and Big Data: A Revolution that Will Transform Supply Chain Design and Management,” Journal of Business Logistics, Vol. 34[2], Forthcoming, http://papers.ssrn.com/sol3/papers.cfm?abstract_id=2279482, accessed May 2014.

19. The future orientation of predictive analytics provides the basis for the most advanced form of analytics, prescriptive analytics, which uses the former's predicted possible outcomes to determine what should be done to achieve the desired outcome. Prescriptive analytics requires the highest levels of internal collaboration and stakeholder engagement to provide input into optimization models for defining what are considered to be the most desirable outcomes. This type of analysis is of the greatest value to a company and its audience insofar as it intelligently prescribes future actions to achieve the desired outcomes. In terms of the <IR> Framework, prescriptive analytics can be used to assess different strategy and resource allocation decisions that will enable the company to achieve its desired level of future performance given its outlook and the risks and opportunities it is facing, adjusting its business model as necessary. Prescriptive analytics helps companies achieve the highest level of integrated thinking by assisting internal collaboration on determining the best possible outcomes and contributing to the creation of economic value over the short-, medium- and long-term. See the following for more information. IBM Software. “Descriptive, predictive, prescriptive: Transforming asset and facilities management with analytics,” http://www-01.ibm.com/common/ssi/cgi-bin/ssialias?infotype=SA&subtype=WH&htmlfid=TIW14162USEN, accessed June 2014. “Predictive analytics is the next step up in data reduction. It utilizes a variety of statistical, modeling, data mining, and machine learning techniques to study recent and historical data, thereby allowing analysts to make predictions about the future.” Bertolucci, Jeff, “Big Data Analytics: Descriptive Vs. Predictive Vs. Prescriptive,” Information Week, December 31, 2013, http://www.informationweek.com/big-data/big-data-analytics/big-data-analytics-descriptive-vs-predictive-vs-prescriptive/d/d-id/1113279, accessed June 2014. Wu, Mike, “Big Data Reduction 3: From Descriptive to Prescriptive,” Lithium Technologies (Science of Social blog) http://community.lithium.com/t5/Science-of-Social-blog/Big-Data-Reduction-3-From-Descriptive-to-Prescriptive/ba-p/81556, accessed June 2014.

24. Effective January 2020, publicly traded European companies will be required to prepare their annual financial reports in a single electronic reporting format. The European Securities and Markets Authority (ESMA) has been charged with the development of draft regulatory standards for adoption by the European Commission. The text of the Directive follows. “With effect from 1 January 2020 all annual financial reports shall be prepared in a single electronic reporting format provided that a cost-benefit analysis has been undertaken by the European Supervisory Authority (European Securities and Markets Authority) (ESMA) established by Regulation (EU) No 1095/2010 of the European Parliament and of the Council. ESMA shall develop draft regulatory technical standards to specify the electronic reporting format, with due reference to current and future technological options. Before the adoption of the draft regulatory technical standards, ESMA shall carry out an adequate assessment of possible electronic reporting formats and conduct appropriate field tests. ESMA shall submit those draft regulatory technical standards to the Commission at the latest by 31 December 2016.” DIRECTIVE 2013/50/EU OF THE EUROPEAN PARLIAMENT AND OF THE COUNCIL of 22 October 2013 amending Directive 2004/109/EC of the European Parliament and of the Council on the harmonisation of transparency requirements in relation to information about issuers whose securities are admitted to trading on a regulated market, Directive 2003/71/EC of the European Parliament and of the Council on the prospectus to be published when securities are offered to the public or admitted to trading and Commission Directive 2007/14/EC laying down detailed rules for the implementation of certain provisions of Directive 2004/109/EC. http://ec.europa.eu/internal_market/accounting/legal_framework/transparency_directive/index_en.ht, accessed June 2014.

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