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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

Contextual Reporting

FIGURE 9.3 Contextual Reporting

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

1. Merriam-Webster Online, s.vv. "information technology," www .merriam-webster.com/dictionary/information%20technology, accessed May 2014.

2. Since we are not experts on information technology, we could not have written this chapter without an extensive amount of constructive criticism and support from Jyoti Banerjee of the International Integrated Reporting Council, Brad Monterio, and Liv Watson. We learned much from them in the process of writing this chapter. We alone, however, are responsible for any errors and omissions this chapter contains. Our hope is that this chapter will prove to be the start of an ongoing conversation about the role of information technology in supporting integrated reporting and integrated thinking.

3. Jyoti Banerjee is exploring the relationship between reporting processes and information technology through a series of workshops with leading companies practicing integrated reporting. He plans to publish the results of this study in the first half of 2015.

4. An enterprise resource planning (ERP) system serves all departments within an organization. It can include software for manufacturing, order entry, accounts receivable and payable, general ledger, purchasing, warehousing, transportation, and human resources. For more information see, Shehab, E.M., M. W. Sharp, L. Supramaniam, and T.A. Spedding. "Enterprise resource planning: An integrative review." Business Process Management Journal, Vol. 10, No. 4, 2004, pp. 359-386, miha.ef.uni-lj.si/_dokumenti3plus2/192008/ vseoERP-BPMJ-2004-1570100401_nov.pdf, accessed May 2014.

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, 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 xbrl.org. The above information is excerpted from "XBRL Basics, How XBRL Works" at xbrl .org/how-xbrl-works-1, accessed June 2014. For more information about XML, please visit w3.org/XML/.

7. Watson, Liv and Brad Monterio, "Integrated Reporting Technologies in the NOW Economy," September 2014, https://workiva.com/resources.

8. SAS. Insights, Big Data, What is Big Data, sas.com/en_us/ insights/big-data/what-is-big-data.html, accessed May 2014; OgilvieOne worldwide. A Day in Big Data, adayinbigdata.com, accessed May 2014; and Lisa Arthur, "What is Big Data," Forbes, August 15, 2013, forbes.com/sites/lisaarthur/2013/08/15/what-is-big-data/, accessed June 2014.

9. Gartner. "Gartner Survey Reveals That 64 Percent of Organizations Have Invested or Plan to Invest in Big Data in 2013," press release, September 23, 2013, gartner.com/newsroom/id/2593815, accessed June 2014.

10. Ibid.

11. "Data that resides in fixed fields within a record or file. Relational databases and spreadsheets are examples of structured data. Although data in XML files are not fixed in location like traditional database records, they are nevertheless structured, because the data are tagged and can be accurately identified." PC Magazine Encyclopedia, s.vv. "Structured Data," pcmag.com/ encyclopedia/term/52162/structured-data, accessed June 2014.

12. "Data that does not reside in fixed locations. The term generally refers to free-form text, which is ubiquitous. Examples are word processing documents, PDF files, email messages, blogs, Web pages and social sites." PC Magazine Encyclopedia, s.vv. "Unstructured Data," pcmag.com/encyclopedia/term/ 53486/unstructured-data, accessed June 2014.

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.

14. More useful is semiautomated data. This type of data can be automatically processed and converted by software tools that use built-in automation capabilities (e.g., optical character recognition or OCR) to perform a key function (e.g., OCR uses pattern recognition and artificial intelligence to convert text into usable data), but it still requires some type of human intervention given that machine-converted information is generally seen to have a lower level of trust and credibility. Nevertheless, semiautomated data is less time consuming and more cost effective to use than human-readable data, making it more useful overall.

15. "Data that describes other data. For example, data dictionaries and repositories provide information about the data elements in a database. Digital cameras store meta-data in the image files that include the date the photo was taken along with camera settings. Digital music files contain meta-data such as song title and artist name. Meta-data are stored in an HTML page (Web page) to help search engines define the page properly, and most especially, make it rank higher in the results list. Meta-data has existed for centuries. Card catalogs and handwritten indexes are examples long before the electronic age." PC Magazine Encyclopedia, s.v. "Metadata," pcmag.com/encyclopedia/term/ 46848/metadata, accessed June 2014.

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," 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, 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," www-01.ibm.com/common/ssi/ cgi-bin/ssiaUas?infotype=SA&subtype=WH&htmlfld=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, informationweek.com/big-data/big-data-analytics/big-data-analytics-descriptive-vs-predictive-vs-prescriptive/d/d-id/11132 79, accessed June 2014. Wu, Mike, "Big Data Reduction 3: From Descriptive to Prescriptive," Lithium Technologies (Science of Social blog) community.lithium.com/ t5/Science-of-Social-blog/Big-Data-Reduction-3-From-Descriptive-to-Prescrip-tive/ba-p/81556, accessed June 2014.

20. Gartner. "Survey Analysis: Big Data Adoption," September 12, 2013, Figure 10, p. 14, https://gartner.com/doc/2589121/survey-analysis-big-data-adoption, accessed June 2014.

21. "When it comes to the strategy and practice of collaboration, nothing can compete with next-generation cloud-delivered tools and processes." "Collaborating in the Cloud," Forbes Insights, p. 2.

22. Log data is data generated by any activity, such as by a click on a website that has a time stamp and perhaps other data associated with it, such as type or location of the person that generated the data (i.e., meta-data).

23. Gartner, "Survey Analysis: Big Data Adoption," Figure 8, p. 11.

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 TFfE 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 2 004/109/EC. ec.europa.eu/internal_market/accounting/ legal_framework/transparency_directive/index_en.ht, accessed June 2014.

 
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