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Emergence of the corporate brain

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A synthesis of the relevant literature and commentary (focusing on the oil & gas sector although some elements may apply to other sectors) has enabled the identification of a number of potential trends, gaps, challenges and opportunities with respect to enterprise search & discovery. It is anticipated that practitioners may find this multi-disciplinary discussion and corporate brain metaphors of interest as a lens for thinking of long term directions.
A key trend is the convergence of established infrastructures and practices with emerging techniques and vast amounts of information, internal & external to, the enterprise. These areas are shown in the figure below, and include Records Management, Data Management, Business Intelligence, Knowledge Management, Workflow Applications, Artificial Intelligence, Information Retrieval, Knowledge Organization Systems, Analytics and Artificial Intelligence.

The result is a level of connectivity and speed of communication that did not exist within most large organizations ten years ago. In context to enterprise search & discovery capability, this highly connected environment (network of systems with different functions) may be analogous in part to a corporate brain. A highly connected, self-organizing and adapting system. A system in which information is continually monitored, remembered, recalled, browsed and visualized; analysis, inferences & deductions made, forecasts predicted, hypotheses tested & ideas emerged. People, information, machines, communities and physical infrastructure are all connected in this brain.
Organizations are at various stages of convergence, of piecing together their corporate brain, possibly dependent on factors such as industry sector, culture and size. Applying a systems thinking approach, may help organizations avoid fragmentation and becoming too technology centric as they evolve their enterprise search & discovery capability.
Whenever empirical research studies are conducted around search and discovery capability within enterprises, it is common for the results of what is actually happening to come as a surprise to the organization. This may include performance and functionality of information technologies, search literacy of staff, social interactions or information management beliefs and practices. These findings may imply organizations are routinely operating off flawed beliefs and mental models. Building a capacity for introspection and becoming more self-aware may be a starting point for some organizations in this area.

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Emergence of the corporate brain

  1. 1. pg. 1 Paul H. Cleverley (2015), Robert Gordon University Paul H Cleverley (2015) www.paulhcleverley.com
  2. 2. pg. 2 Paul H. Cleverley (2015), Robert Gordon University ENTERPRISE SEARCH & DISCOVERY CAPABILITY: EMERGENCE OF THE CORPORATE BRAIN Paul H. Cleverley, Robert Gordon University, UK (December 2015) Purpose A synthesis of the relevant literature and commentary (focusing on the oil & gas sector although some elements may apply to other sectors) has enabled the identification of a number of potential trends, gaps, challenges and opportunities with respect to enterprise search & discovery. Mention of specific organizations, techniques and technologies has been avoided to keep the discussion at a conceptual level. It is anticipated that practitioners may find this multi-disciplinary discussion and corporate brain metaphors of interest as a lens for thinking of long term directions. Figure 1 – The emergence of the corporate brain Introduction A key trend is the convergence (Figure 1) of established infrastructures and practices with emerging techniques and vast amounts of information, internal and external to, the enterprise. The result is a level of connectivity and speed of communication that did not exist within most large organizations ten years ago. In context to enterprise search & discovery capability, this highly connected environment (network of systems with different functions) may be analogous in part to a corporate brain. A highly connected, self-organizing and adapting system. A system in which information is continually monitored, remembered, recalled, browsed and visualized; analysis, inferences & deductions made, forecasts predicted, hypotheses tested & ideas emerged. People, information, machines, communities and physical infrastructure are all connected in this brain. Utilizing this connectivity, fractals of pattern recognition, reasoning, decision making and learning occur at the machine, individual, group & organizational hierarchies, augmenting each other,
  3. 3. pg. 3 Paul H. Cleverley (2015), Robert Gordon University guided by certain values & principles. The pace of electronic publishing has far exceeded our capacity to read all pertinent information, where perceptions of information overload are prevalent. The sheer volume of information that is already available and being constantly collected & created is pushing organizations to increasingly augment their manual search & discovery and reasoning processes. This is being achieved by using computer algorithms as assistants that are capable of reading far more information than humans can. These techniques may also offer (to some extent), an objective view, letting the data speak rather than imposing existing hypotheses and biases before search & discovery activities are undertaken. They provide another voice in the corporate brain. For most knowledge based work, the emphasis appears to be on decision support, assisting rather than replacing human judgement. The metaphor or analogy of the corporate brain has been used before in the literature. However, it has never been applied with respect to enterprise search and discovery capability, as a holistic concept. This corporate brain metaphor is not technology centric nor is it a monolithic structure. It is heavily distributed, formed from short term (e.g. information of temporary value) and long term memories (e.g. records) scattered across the organization. Some organizations beliefs and mental models could be flawed from the outset, believing search & discovery capability is a technology problem to be solved. Enterprise search & discovery capability could be described as a wicked problem, one where there is no solution or end state, although things can be made better or worse. Some organizations appear to have recognized that search & discovery of information is intrinsic in almost everything they do. Other organizations may see it as more of a time saver, a technology centric concept, where the business case for further investment is unclear. Leadership and vision in this area is probably key. Formal elements (what is written down e.g. roles, procedures, standards) and its technical sub- set (what can be automated using computers) could be considered to float within a soup of socio- cognitive sub-cultures, behaviours, beliefs and motivations (the informal organization), the way things are done around here. The informal organization is the more complex and invariably consists of many sub-cultures within the corporate brain, capable of both emotional and logical responses. Both formal & informal organizational layers impact upon (and shape) each other. When recalling information (in a context), organizations may seek to lookup a fact or known item (there is generally a single right answer or result). Alternatively, they may explore an idea, an open ended question, one in which how well they have performed is not known. They may have a need to monitor and recognize new or unusual information and patterns to make interventions. Information Retrieval (IR) In 2015, the number of consumer searches made on mobile devices exceeded those made on desktops. Whilst corporate needs can be somewhat different, mobile search & discovery trends may continue to gain greater prominence in many enterprise environments. OpenSource technologies appear to becoming more prevalent in deployments due to the perceived benefits around cost, flexibility and interoperability, especially where a proprietary de facto standard does not exist. In many cases however, de facto standards are perhaps limiting some capabilities. Documents, expertise profiles, web pages and discussion forums have been indexed by enterprise search engines to improve findability of information (the corporate Google) with mixed results. In some parts of oil and gas, of the total time staff spend seeking information, 40% may be spent looking externally. Searching in the enterprise is clearly not restricted to searching within the
  4. 4. pg. 4 Paul H. Cleverley (2015), Robert Gordon University enterprise. The organization has (or needs) close connections to the external body of knowledge that exists, which is increasingly becoming democratized through open access - available to all. Today, major differences exist between the IR behaviour of consumer web search engines and those as deployed in enterprises. This is causing problems as staff flip between the two; crucial information is being missed in the enterprise. For example, a search in some enterprise search deployments for magnetics (the plural) will miss items about magnetic (singular) that do not contain the plural term. Research has shown that the vocabulary problem (people will not choose the same name for the same concept 80% of the time) leads to staff missing on average, 43% of relevant items in a single subject based keyword search. For example, a search on carbonates, will not return items on limestone that do not mention carbonates. Geoscientists know limestone is a carbonate rock, but keyword based enterprise search engines (without semantics) do not. Business Intelligence (BI) and Structured Data & Information From a technology perspective, Business Intelligence (BI) has a history steeped in financial transactions and reporting, to roll-up and aggregate through common schemas using On-Line Analytical Processing (OLAP) techniques. These business warehouse techniques evolved to include multi-disciplinary information (e.g. engineering and scientific information) for real time queries and dashboard style reporting of structured information across disciplines, aiding insights. Increasingly sophisticated analytics is increased merged into this practice. Geographical Information Systems (GIS) are also often been used as de facto data warehouses to integrate information. Left and Right Hand Sides of the Corporate Brain Metaphorically speaking, if the left hand side of the brain is unstructured information (e.g. web pages, text in documents) and the right hand side is structured information (e.g. images, tables in databases) many organizations do not yet appear to have created deep connectivity between the two sides. Any integration appears to be loosely coupled through federated queries, where documents are often simply treated as containers. With the emergence into the mainstream of flexible graph type databases (semantic networks) particularly on the consumer web, there are some signs that structured and unstructured information could be deeply merged and integrated automatically within the enterprise. This could enable organizations to move beyond documents as containers, focusing on the concepts and associations within, linked to the structured data and information. This may support more advanced suggestion and question & answer capability, following and expanding upon those techniques deployed on the consumer web. As stated by some commentators, serendipity favours the connected. Some systems (technology, formal, informal) may have a greater propensity to facilitate serendipity than others. Just as seemingly random thoughts often enter our minds (sometimes distracting us) on occasion the surfacing of an unexpected association can lead to curiosity and that eureka moment. When designing search and discovery technologies, designing for serendipity may take on a greater level of importance when its creative influence is fully recognized by organizations. This may be especially pertinent in technical environments, unlike the consumer web where what is most popular tends to predominate. Knowledge Organization Systems (KOS) with linguistic type approaches such as taxonomies, ontologies and authority lists (reference data) along with statistical techniques may be critical to bring these two sides of the brain together. The narratives on the Semantic Web in the enterprise (described by some as souped up business intelligence) promise much for interoperability, but implementations within many enterprises
  5. 5. pg. 5 Paul H. Cleverley (2015), Robert Gordon University appear patchy in the literature. Many approaches do not appear to be re-usable or scalable across the enterprise and industry sectors, so appear as isolated initiatives. The continued trend of integrating external with internal information, may offer fresh opportunities in this area. Integration with Tasks, Workflow and Applications One advantage the enterprise has over the consumer web, is knowing the specific job role of the searcher and many of the associated tasks to be performed. Organizations and software vendors continue to embed search & discovery into workflow environments, apps and tools, a trend which is likely to continue further. These highly contextual associations (which often use precise metadata) enable information to be pushed and suggested through various flavours of tightly and loosely pre-defined queries. Recalling information (in context) through filters may be analogous to how the brain recalls information through word associations, when primed with a particular situation. Care probably needs to be taken when limiting results based on the discipline, location or personal profile of a searcher. In some scenarios this tunnel vision is likely to be highly effective, in others it may possibly blind the organization to new discoveries, without it knowing so. Artificial Intelligence (AI) Artificial Intelligence (AI) is a broad and diverse field which has evolved over time, covering the science and engineering of making intelligent machines, especially intelligent computer programs to act like humans do. Without getting into finer details, there are some commentators that feel weak AI is in use today (e.g. conversational search on our mobiles), whilst strong AI (machines with consciousness) may or may not be something that can be achieved in the future. AI has benefited from some breakthroughs in recent years, such as voice recognition, image classification and the sheer volume of codified data & information becoming available to learn from. This includes information from the Internet, usage/transactional user information, social media and sensors (Internet of Things). The combination of this information (described by some as Big Data), with Knowledge Organization Systems and machine learning techniques (such as neural networks and text analytics) has been described as cognitive computing. In this paradigm, there appears to be a shift from traditional expert systems where people programmed the rules, to one in which the machines construct the rules. These techniques can be very good as spotting similar or related things, such as words, concepts, entities, sentences, documents, pictures or complex contextual situations. Of particular interest may be analogue identification in the internal and external body of knowledge. The availability of easy to use OpenSource tools for machine learning (e.g. word2vec) may have democratized some aspects of weak AI, although are yet to be included in many enterprise search deployments. Various algorithms have been used to suggest related information, predict events and even prescribe action (prescriptive analytics). Some commentators raise concerns this could place us in a filter bubble, where discovery takes place through the rear view mirror, based on what has been done or already occurred historically. Conversely, there is evidence that these analytical techniques offer significant opportunities to aid new discoveries, making hitherto unforeseen connections. Algorithms that can surface the unusual, non-obvious, or discriminant amongst the vast information haystack may add significant business value and provide competitive advantages. Information Literacy Some researchers and practitioners indicate improvements could be made in information literacy levels (including information search & discovery). Recent research supports that view. There may
  6. 6. pg. 6 Paul H. Cleverley (2015), Robert Gordon University be some misunderstandings within organizations in this area; where some indicate, search engines should be so smart people should not need to know how to search. Whilst this may be the case for lookup searches, search engines are unlikely to be able to produce a reference list for a literature review at the press of a button. Exploratory search user interface design is an area of significant and ongoing research. However, being a good searcher is likely to play a key role regardless of technology used and involve more elements than simply knowing how to construct search queries. Searching the same resource but discovering something a competitor does not can be a competitive advantage and may be a vital component of enterprise search & discovery capability. Knowledge Management (KM) and Social Networks All knowledge could be described as being socially constructed. Knowledge Management (KM) is primarily concerned with exploiting the knowledge held by the organization for business benefit. Whilst it has arguably, never been possible to manage knowledge, it is widely accepted that some conditions may be more likely to create an effective environment in which knowledge can be created, captured, shared and used for business benefit, than others. Informal (e.g. self-organizing, bottom up) and formal (e.g. moderated communities of practice, top down) social networks are a key element of KM and extend beyond organizational boundaries. They are the contextual clusters and nurseries through which the organization often learns and makes serendipitous discoveries. Some researchers indicate prepared minds and immersion in diverse information rich environments are more likely to stimulate serendipitous encounters than not. Cultivating a critical thinking environment and allowing time for staff to experiment and reflect, is likely to be an important factor along with formal recognition & reward structures. Emerging social media technologies and open annotation standards offer further opportunities to increase knowledge capture in context and improve connectivity. Annotations to content could be thought of as mental notes conveying a discourse that may exist on a topic. Care may need to be taken using user clicks and likes or shares in enterprise social tools to infer behaviours, otherwise wrong conclusions may be drawn. For example, web research indicates 55% of people spend less than 15 seconds on a page, only between 1-8% of visitors like or share items and no association was found between how often an item is shared and the amount of attention a reader will give it. For many types of information however, context-rich social networks may be a more effective way to locate information than traditional Information Retrieval (IR) methods using the classic search box. This poses the question of what is considered knowledge, truth and authority in the organization. The most convenient route to information may not always be the most reliable. In some cases the information found may be good enough, in other cases accuracy could be vital. The formal organization can aid this process through nominating experts in their respective fields to act as go to people for certain types of information. Peer review structures is another example. However, when it comes to ignorance, fallibility and error, the corporate brain is likely to be culpable, it is probably inevitable. Organizations should not be surprised, but perhaps plan for it. Not all tacit knowledge (held by people) can be codified into explicit knowledge, so loss of staff due to retirement or circumstance will likely lead to knowledge loss of some form in the corporate memory. Alumni schemes seek to maintain connections, although there is little evidence in the literature these are effective and is possibly an area of opportunity for organizations. Information housekeeping
  7. 7. pg. 7 Paul H. Cleverley (2015), Robert Gordon University If information is not present or stored in the correct associated place (housekeeping), the corporate brain’s connectivity will probably be impaired or damaged in some way. Sub-cultures, values, beliefs and politics can all affect connectivity. Key information which has not been abstracted from project work, put in a certain place and labelled (tagged) correctly is likely to lead to poor memory recall or even future memory loss. There is evidence in some sectors that the average organization believes 17% of its total data is inaccurate in some way. Ignorance, fallibility and error is unlikely to be eradicated in the corporate brain, but it can be made better or worse. Managing information access control (the tension between information security and knowledge sharing) remains a challenge, with many organizations struggling to avoid valuable information being hidden (unconnected). A culture where one asset is competing against another for budget, can lead to behaviours where information is deliberately not shared, leading to poorly connected or isolated islands of memory. Information may become lost in place, perhaps like corporate amnesia. Trails of missing records of activities and absent project deliverables may themselves be artefacts, scars in the memory caused by past traumas, poor practices or governance. Organizational downsizing tied to the economic climate, or replacement of one major technology with another, can also have a traumatic effect on the corporate brain, causing unseen damage. Where connections that were in place are sometimes abandoned or forgotten, in favour of short term goals. Many organizations may struggle to find certain information not because of current practices, but perhaps because of past traumatic experiences. Cause and effect for poor search task performance can be significantly distant in time and space. Organizational consciousness Metacognition is thinking about thinking, higher order executive processes that help planning, monitoring and reflection. Organizational metacognition describes how the organization checks and monitors itself (a form of governance), knows what it knows and knows that it doesn’t know; how it makes sense of information and reflects. This self-awareness could be likened to a form of organizational consciousness. Research has shown poor exploratory search task performances may be caused by poor organizational metacognition. Measuring search & discovery capability may go beyond the technology centric approach of search engine optimization. An organization may sleepwalk its way through activities, obliviously satisfied in its own performance (unbeknown to the organization, their performance is poor), caused by absent or ineffective experimentation, monitoring and sensing processes. Summary Facilitating massive connectivity (the metaphor of the corporate brain) with appropriate behaviours, may enable the creation of an environment in which enterprise search & discovery capabilities are enhanced. Organizations are at various stages of convergence, of piecing together their corporate brain, possibly dependent on factors such as industry sector, culture and size. Applying a systems thinking approach, may help organizations avoid fragmentation and becoming too technology centric as they evolve their enterprise search & discovery capability. Whenever empirical research studies are conducted around search and discovery capability within enterprises, it is common for the results of what is actually happening to come as a surprise to the organization. This may include performance and functionality of information technologies, search literacy of staff, social interactions or information management beliefs and practices. These findings may imply organizations are routinely operating off flawed beliefs and mental models.
  8. 8. pg. 8 Paul H. Cleverley (2015), Robert Gordon University Building a capacity for introspection and becoming more self-aware may be a starting point for some organizations in this area. Further Reading Abram, S. (2013). Workplace information literacy: It’s different. In M. Hepworth & G. Walton (Eds.), Developing people’s information capabilities: Fostering information literacy in educational, workplace and community contexts library and information science (Vol. 8, pp. 205–222). London: Emerald. Addison, V. (2014). Oil, Gas Industry Focuses on Predictive Analytics. Hart Energy October 6th 2014. Allan, J., Croft, B., Moffat, A., Sanderson, M. (2012). Frontiers, Challenges, and Opportunities for Information Retrieval. Report from the Second Strategic Workshop on Information Retrieval in Lorne, February 2012, ACM SIGIR, 46(1), 2-32 Andersen, E. (2012). Making Enterprise Search Work: From Simple Search Box to Big Data Navigation. Center for Information Systems Research (CISR) Massachusetts Institute of Technology (MIT) Sloan School Management, 12(11). Argote, L. (1999). Organizational learning: Creating, retaining, and transferring knowledge (p. 28). Boston: Kluwer Academic. Argyris, C. and Schon, D.A. (1978). Organizational Learning: A Theory of Action Perspective. Addison-Wesley, USA. Bandura, Albert (1976). Social Learning Theory. Englewood Cliffs, NJ: Prentice Hall Bawden, D. (1986). Information-Systems and the Stimulation of Creativity. Journal of Information Science, 12(5), 203-216. Behounek, S., Casey, K. (2007). EarthSearch=GoogleEarth Enterprise+PetroSearch. Society of Petroleum Engineers (SPE) Digital Energy Conference and Exhibition, 11-12th April, Houston, Texas, USA. Report ID: SPE-108208-MS Berger, P. and Luckmann, T. (1966). The Social Construction of Reality: A Treatise in the Sociology of Knowledge. Penguin, USA. Bhogal, J., Macfarlane, A., Smith, P. (2007). A review of ontology based query expansion. Information Processing and Management, 43, 866-886. Bizer, C., Heath, T., Berners-Lee, T. (2009). Linked Data – The Story So Far. Special Issue on Linked Data, International Journal on Semantic Web and Information Systems (IJSWIS), 5(3), 1-22. Blei, D, Ng, A., Jordan, M. (2003). Latent Dirichlet Allocation. Journal of Machine Learning Research 2003, 3, 993-1022 Blummer, B., & Kenton, J.M. (2014). Improving student information search: A metacognitive approach. Oxford, UK: Chandos Publishing. Borgman, C.L. (1984). The user’s mental model of an information retrieval system: An experiment on a prototype online catalog. International Journal of Man-Machine Studies, 24(1), 47–64. Bowler, L. (2010). The self-regulation of curiosity and interest during the information search process of adolescent students. Journal of the American Society for Information Science and Technology JASIST, 61(7), 1332–1344. Brown, J.S., Duguid, P. (1991). Organizational Learning and Communities-of-Practice: Toward a Unified View of Working, Learning and Innovation. Organizational Science, 2(1), 40-57. Brin, S. and Page, L. (1998). The Anatomy of a Large Scale Hypertextual Web Search Engine. Proceedings of the 7th International Conference on the World Wide Web, 107-117 Brown, N. (2014). Fostering Collaboration Using Analytics & Real-time Big Data Search: Insight into Technology Services. AstraZeneca presentation Enterprise Search Europe, 29-30th May, London, UK. Brown, S. (2014). Investment firm appoints robot to its board. http://edition.cnn.com/2014/09/30/business/computers-ceo- boardroom-robot-boss/ Bushell, S. (1999). Wiring the Corporate Brain. Chief Information Officer (CIO). Online Article 6th October 1999 (Accessed October 2014). Byrne, D. and Callaghan, G. (2014). Complexity Theory and Social Sciences (2014): A state of the art. Routledge, Oxfordshire, UK Caballero, R, Nuernberg, S. (2014). Building an Enterprise Taxonomy. 18th International Petroleum Data, Integration and Data Management (PNEC), May 20-22nd 2014, Houston, USA. Case, D.O. (2012). Looking for information. A survey of research on information seeking, needs and behaviour. Third Edition, Emerald, UK. Choo, C.W., Furness, C., Paquette, S., van den Berg, H., Detlor, B.,Bergeron, P., & Heaton, L. (2006). Working with information: Information management and culture in a professional services organization. Journal of Information Science, 32(6), 491–510.
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