Next Generation Information Management andEnterprise Search & Discovery Capability.

But (probably) not as you know it..

Paul H. Cleverley (2015)

With the exponential growth in ‘big data’ information volumes within the organization, the convergence of traditional business intelligence, enterprise search, social media, text analytics, knowledge organization and machine learning has the potential to revolutionize how we search for and discover information and knowledge.

However, it appears to have become almost unfashionable to mention ‘people’ in this paradigm. Yet these new techniques and information volumes are likely to place an even greater reliance on underlying information flows, social interactions and information literacy.

The findings from this research study lead to a suggestion that some practitioners and management might wish to consider moving away from ‘first generation’ Information Management (IM) and enterprise search & discovery capability viewpoints. These viewpoints have generally been dominated by reductionist (simple answers to complex issues), fragmented, technological and bureaucratic approaches.

A more enlightened ‘second generation’ viewpoint of IM and enterprise search & discovery capability, could be one in which socio-cognitive elements are fully embraced in a proactive ‘systems thinking’ approach; where the organization is the information system, a complex, changeable and unpredictable system. Thinking in this way may lead management and practitioners to change tack and pull different levers to improve IM and enterprise search & discovery capabilities.

Prologue

“Human nature is prone to suppose the existence of more order and regularity in the world than it finds”Francis Bacon

“Tyranny is the absence of complexity”, “..the deliberate removal of nuance”Andre Gide, Albert Maysles

“How do we actually change behaviours? .. in order to make a real change in an organization, you really have to change the beliefs..”Schultz (2002)

“Listening to both sides of a story will convince you there is more to a story than both sides”Frank Tyger

Stimulating some thoughts…

• Aim• Abstract/Summary• Background

– Enterprise Search– Organizational Semiotics– Complexity Theories– Organizational Learning

• Integrated Theoretical Model• Case Study• Discussion, Conclusion and Limitations

Index

Purpose: To provoke and stimulate discussion in the Information Management (IM) and enterprise search practitioner & academic community.

Aims: To examine the role of the Informal (social) values, beliefs, motivations and politics on IM and Enterprise Search & discovery capability.

PhD Research: This is part of a much larger PhD study investigating the conditions and causal factors for poor search task performance in the enterprise.

Aim

This Powerpoint is rather wordy so it can ‘stand alone’ without the presenter as a ‘reference’

Despite significant investments, surveys indicate over 60% of organizations have difficulty finding internal information. Enabling everyone to find or discover all pertinent information without difficulty could be described as a ‘wicked problem’, one that may not be solvable or have an end state, but can be tamed (made better or worse). When traditional practices fail or fall short of expectations, even non-theorists may see that having a theory might help in some way. Information Management (IM) and enterprise search & discovery capability have been dominated by Formal (e.g. technology, bureaucratic policies & governance) and typically reductionist approaches (simple answers to complex issues). In some cases reasoning may be motivated by a desire to vindicate prior beliefs. These approaches can be described as being based on ‘first generation’ viewpoints and might be reinforced (or even influenced) by some influential external commercial vendor marketing messages.

With notable exceptions, little attention has been paid to how Informal (Socio-cognitive) elements (including values, beliefs, motivations and politics) impact search outcomes in the enterprise. Prior research indicates many serious incidents or accidents in organizations result from a series of small failures that went unnoticed and lined up in an unfortunate way. Interviews with scientists and data managers were undertaken in a case study organization whose core values are integrity, honesty and trust. A Complexity Theory framework was subsequently used as a lens to draw out significant events and patterns of behaviour over time from the interviews. The results provide evidence that the organization of enterprise information has characteristics of a turbulent unpredictable chaotic and complex system in need of constant proactive ‘policing’ and ‘nurturing’.

Abstract/Summary (1 of 2)

Small changes in initial conditions led to larger unexpected future state changes (i.e. missing million dollar opportunities). The attractors (behavioural magnets) of ‘lack of respect’, ‘short termism’ and ‘work overload’ appear to create conditions leading to future poor IM and search & discovery outcomes. Lack of effective feedback loops and assignment of accountability without effective engagement may amplify effects. What is written down or formally communicated and the way people actually work, can be significantly different. This comes as a surprise to some practitioners and management in the case study organization. Other studies (e.g. on search literacy performance) by the author confirm the phenomenon of ‘surprise’, implying widespread flawed belief systems and mental models with an over reliance on technology, formality & punitive measures. A more accurate picture of ‘what’s going on’ may be achieved viewing the organization as the information system; a complex system, changing and adapting (not always for the better) where organization is guided by formality but governed through behavioural norms.

Moving to an IM practitioner belief system and culture that encourages proactive reflectivity, embracing the inevitability of ignorance, fallibility and error in the information system, could lead to more effective approaches to both managing information and searching for it. New technology developments and formal information governance will continue to play vital roles in IM and enterprise search & discovery capability. However, organizations may benefit from embracing a more enlightened ‘second generation’ viewpoint embracing socio-cognitive elements. One where enterprise search & discovery capability sheds its reductionist, technology centric ancestry, in favour of a complex system thinking mind-set. Thinking and acting in this way may cultivate ‘better’ capability trajectories and is an area for further research.

Abstract/Summary (2 of 2)

• There is no agreed definition for enterprise search, the modus operandi has been to use the term for a particular type of search engine technology. A more holistic definition is proposed here:

• Enterprise Search and Discovery is an overarching concept, focusing on the capability for an organization to easily search, find and discover digital information from multiple sources across the entire enterprise using IR technologies, to meet work task and business goals. In order to achieve this successfully, a variety of enablers may be required in the enterprise environment (including effective Information Management).

Background – What is enterprise search?

• Search & discovery covers not just looking for an item or website you know exists (Lookup/known item search) a ‘closed question’, but also ‘open questions’, exploratory search, which may be multi-faceted give many results and the searcher may not know ultimately ‘how well they have performed’. The capability includes fortuitous information encountering (serendipity); some enterprise search environments may be more likely to stimulate serendipitous discovery than others (Cleverley and Burnett 2015) http://www.slideshare.net/phcleverley/cleverley-dej-facilitating-serendipity-v2

• 24% of a business professionals time is spent looking for information and according to industry surveys (Findwise 2015), over half of organizations are dissatisfied with current practices.

• Several hundred executives estimate the value lost of not discovering and leveraging their information for business opportunities is in the order of 14% of annual revenue (Oracle 2012). Poor search can miss evidence of fraud (Johnson 2013) and has caused fatalities in the health sector (Savulescu and Spriggs 2002).

Background – Why is enterprise search important?

• Traditional Information Systems ‘technology centric’ success (DeLone and Mclean 2002) and technology acceptance (Wixom & Todd 2005) process/causal models do not include organizational feedback loops.

• Some ‘user centric’ process/causal models for search in the workplace exist (Leckie et al 1996) but do not include organizational feedback loops.

• Some ‘organizational centric’, prescriptive models exist for enterprise search (Tubb 2015) but lack empirical evidence and do not consider the Informal (social) organization (the whole ‘system’). The emphasis appears to be on technology, services, policies & standards i.e. Formal Information Management (IM). The reasons for this are unclear. Some researchers hint that informal information culture ‘trumps’ formal IM (Choo et al 2006) in terms of information use outcomes.

Background – Information Search Literature Gaps

• Using a security analogy, for Root Cause Analysis (RCA): “many failures stem from ‘second order’ vulnerabilities. These describe problems that do not directly cause an adverse event but can help to create the conditions in which security incident is more likely to occur.” (Johnson 2005)

• An effect does not need to occur where its cause occurred. For example, “The use of DDT in the 1940’s caused serious reproduction problems in polar bears in the far North in the 1990’s – a clear case of the effect being distant in time and space from the cause” (Tittle 2011)

• When staff in an organization cannot find information using their internal search engine (their corporate “Google”) there is a tendency for people to blame the technology, “we need a better search engine”. In some research studies technology only accounts for 36% of causes http://www.slideshare.net/phcleverley/causes-for-poor-enterprise-search-experience)

Background – Causes can be distant in space/time

• Reductionist approaches tend to look for simple answers to complex issues. When people struggle to find information, laying cause and effect with the search engine ‘Why can’t we have Google?’ may be an example of such thinking. Reasoning may on occasion be driven by a desire to vindicate a prior belief, where ‘new information’ is not necessarily treated with an even hand (Druckman 2012).

• Another example is ‘information tagging’. A reductionist approach could be declaring ‘if everyone tagged their information properly (e.g. using Microsoft SharePoint) we would not have a problem finding information”. In a survey of over 200 organizations (Findwise 2015) ‘lack of appropriate tags’ was given as the main obstacle for users finding the information they were looking for.

• Do values, attitudes and beliefs of staff and management play a role with respect to whether we can find information in the organization and are those values, beliefs and attitudes static or dynamic?

Background – Reductionist v Systems Thinking

• A root cause study of 891 ‘complaints’ over a 2 year period made using the feedback button on the User Interface (UI) of an enterprise search engine yielded the following data:

– 32% Content Related (e.g. missing, poorly named/tags, incomplete, out of date)– 22% Search Literacy related (e.g. poorly formed queries based on need)– 36% Technology Related (e.g. Slow/unreliable, poor ranking, sub-optimal UI etc.)

• This study focuses on Informal content processes (not search literacy or IT) – The author has already investigated search literacy impacts on enterprise search task performance: JASIST

paper here http://onlinelibrary.wiley.com/doi/10.1002/asi.23595/abstract text analytics UI impacts: JIKM paper here http://www.worldscientific.com/doi/abs/10.1142/S0219649215500070 , Formal taxonomy impacts: ISKO paper here http://www.iskouk.org/content/best-both-worlds-highlighting-synergies-combining-knowledge-modelling-and-automated and information needs: JIS paper here http://jis.sagepub.com/content/41/1/97

Background – Causes for search task performance

• In a study of 26 experienced Information Management (IM) professionals in a large oil and gas company under information overload, it was found that there was no relationship between user satisfaction (or self-reported search expertise) and how well professionals actually performed exploratory searches. Low averages were detected in terms of high value items found. http://www.slideshare.net/phcleverley/enterprise-search-research-user-satisfaction-and-search-task-performance

• The IM professionals, CIO office and business management were ‘surprised’ at their poor performance. Organizations may not ‘know’ their search expertise and some may not ‘know’ that they ‘don’t know’ their search expertise (organizational metacognition). Data from the Defence, Pharmaceutical and Aerospace sectors supports transferability of some of the findings. Organizations may not be applying a ‘systems thinking’ approach to ‘search capability’, with an overemphasis on formal & technology elements and an under-emphasis on learning - socio-cognitive elements.

Background – Causes for search task performance

• Systems Thinking (Senge 1990) – think of the ‘whole’ not ‘fragments’• Organizational Semiotics (Stamper 1996, Liu and Li 2015) considers an

organization as an information system. An organization is a social system in which people behave in an organized way conforming to systems of norms. Norms can be modified through feedback loops

• Norms act as a forcefield governing every part of the organization. They can be considered to exist in three layers like an onion:

Technical (IT, automation, search log analysis helps here)Formal (Policies accountabilities, what is written down)

Informal – Social (Values, beliefs, motives etc., not written down)

Background – Organizational Semiotics

• Exploding some myths: In this context, Chaos (Lorenz 1972) is not a synonym for randomness (it is extremely complex information rather than an absence of order). Chaos/Complexity Theory does not mean things cannot be explained or predicted, the possibility does exist.

• Taken from chaos theory, deterministic non-linear dynamical systems which are highly sensitive to initial conditions. A small change in one state can result in larger changes in a later state, the unpredictability of systems.

• Lorenz notes that what appeared to be inconsequential/insignificant changes in initial conditions, had major effects on his weather prediction models.

• A butterfly flapping its wings on one place, leading to a tornado in another is a metaphor for this effect. It is meant to represent the sensitivity of changing initial conditions, which can give rise to a much different alternate reality.

• Complex Adaptive Systems (CAS), unlike Chaotic Systems, can self organize. The interaction of feedback is critical to this process.

Background – Chaos and Complexity Theory

• “Can the processes I am examining be understood and interpreted better if I think about them in terms of Complex Adaptive Systems (CAS) and does thinking about them in that way help me to understand their nature and inform managers and other decision makers how to act effectively in relation to them?” (Byrne and Callaghan 2014).

• “In our analysis of complex systems ... We should rather be sensitive to complex and self-organizing interactions and appreciate the play of patterns that perpetually transforms the system itself as well as the environment in which it operates” (Cilliers 1998)

• Chaos Theory has been applied (metaphorically) to organizational IT/IS projects (McBride 2005, McElroy 2000). It could provide a framework to interpret the enterprise search environment.

• Chaos Theory “encourages us to develop mind-sets and skills that focus on recognizing and changing patterns…. It provides a methodology for analysing a system’s ‘attractor patterns’ and for changing the trajectory” (Morgan 1997, pg. 282).

Background – Complexity Theory & Organizations

• Initial Conditions - The initial state (governing variables) of an organization, information system or actor at the point at which a change is being analysed (McBride 2005)

• Strange Attractors –Dynamic, semi-stable patterns of behaviour exhibited by organizations, information systems and actors over time. In organizations, strange attractors are fractals - "behavioural magnets" that "create patterns for organizing firms and societies" (Lynch and Kordis, 1988). According to Gilstrap (2005), Strange attractors can be “metaphorically described in organizational settings as shared vision, team processes, and information flows….”

StrangeAttractors

Social Behaviour

Feedback

Feedback

Individuals (Agents)

Background – Complexity Theory & Organizations

• According to Argyris and Schon (1987) when errors are detected, then corrected in order for the organization to continue its policies or objectives – the process is termed single-loop learning. Like a thermostat that learns when it is too hot or too cold and turns the heat off or on. It can work because it has a sensor – it receives feedback. In Information Management, it is equivalent to detecting through metrics and finding that missing document and putting it where it should be, or naming a file correctly if it is found to be incorrect.

• Double loop learning takes place when errors are identified and rectified in a way that involve the modification of an organizations ‘norms’, policies or objectives. In Information Management terms ‘why are we not doing this?’. That could mean for example, assigning new responsibilities to business management and creating new behavioural ‘norms’ that treat information as a valuable asset.

Background – Organizational Learning

A Theory of Action (Argyris and Schon 1978)

Question the ‘norms’

Operationalize actions

Background – Organizational Learning

Information Management

and Enterprise Search

Capability

OrganizationalSemiotics Theory

Lens

Technical

Formal

Informal(Cognitive & Social)

ComplexityTheory Lens

• Sensitive to initial conditions• Difficult to predict• Self organizing

More accurate picture to

‘manage’ the search

environment

Feedback & Learning loops as ‘norms’ (Manning et al 2008, Kolb 1984,

Bandura 1980, Argyris and Schon 1978)

Integrated Theoretical Model

• Organizational Semiotics (OS) helps us view the organization as 3 layers of norms like an onion (Technical, Formal & Informal). Some can be written down and become part of the Formal organization, some coded into computer software to be automated (Technical), all enveloped by the Informal (Social) layer.

• Behavioural ‘norms’ can be grouped in an Organizational Morphology (Liu and Li 2015, Stamper et al 1994) into three types:– Substantive (contribute to organizational objectives e.g. for oil & gas this would be oil exploration,

production etc.; for an educational institute they would be teaching and research etc.)– Communication (informing people about necessary facts, procedures e.g. meetings, sending emails)– Control Reinforce the whole business system (substantive and communication)) so they run properly.

Evaluation and monitoring are key tasks. e.g. attendance registers and annual performance reviews.

• A model weaknesses is the focus on control. Information systems learn and adapt. i.e. machine learning (Manning et al 2008), experiential (Kolb 1984), social cognitive learning (Bandura 1976), self-organizing behaviour (e.g. bottom up communities of interest) and formal organizational learning (Argyris and Schon 1978, Weick 1995). Integrating OS with Complex Systems Theories and Feedback/Learning loops may provide a more holistic and novel perspective in which to analyse IM and enterprise search & discovery capability.

Value of the Integrated Theoretical Model

• Anonymous interviews were conducted with eight purposefully sampled geoscientists and data managers in an oil and gas exploration team. Semi-structured interviews focused on what areas of data & information could be improved in the organization and why. Interviews were conducted using a critical incident technique to focus on specific examples.

• The interview transcripts were then subsequently analysed using a Complexity Theory framework to help explore some significant issues, drawing out significant events and patterns of behaviour over a period of time.

• The findings were shared with senior management in the organization concerned. One narrative is included in the following slides.

Methodology: Oil and Gas Case Study

• Initial conditions (pre-2008)– From their website, organization X core values are HONESTY, INTEGRITY and RESPECT.

These core values could be seen as three behavioural rules by which each individual conducts Data & Information Management interactions and transactions.

– The Data Manager has responsibilities to provide Data & Information Management (D&IM) services to the Geoscientists and ensure (in partnership with the Geoscientists) corporate repositories are updated with final deliverables. Formal D&IM processes exist.

– The data manager ’polices’ the shared drive, ensuring data & documents are named correctly and put in the right place. Sometimes the scientists ‘push back’ as they are under time pressure, however the Data Manager perseveres, illustrated by, ‘there were [two Data Management staff]…who, in a perfectly pleasant way, would be round at your desk going “come on let’s sort this out”’ [P3]

– “The data manager feels they are part of things, sit in team meetings, have the authority and are encouraged by the Discipline lead to do that little bit of chasing and tweaking and whatever else. For example they would say, “hang on this isn’t well named let’s….so we definitely know what this is and it’s in the right place”, “hang on there’s six versions of this V1 to V6 I don’t know which they are they’re a year old they can’t all possibly need keeping now”.” [P3]

Results

• Strange attractors– Data Managers exhibited proactive, chasing persevering behaviour, reminding scientists

to follow certain procedures, clean-up files, rename files etc. The scientists are under tight time frames, they may not be relied on to follow D&IM processes targeting the long term; the cajoling role of the data manager is a key behaviour perhaps underpinned by the values of RESPECT. A Geoscientist comments, “The Data managers felt they had the authority and also were thoroughly encouraged by the Discipline Lead to do that little bit of chasing and tweaking and whatever else” [P3]. The team has generally good systemic IM behaviours.

– MANAGEMENT BEHAVIOUR & ATTITUDES Business Management gives the necessary attention, “the geophysics discipline lead ..would put some emphasis on this [D&IM]” [P3]. A norm existed of treating D&IM as a long term asset, staff are given the necessary time.

Results

• Events and Choices– In response to poor user feedback, the organization replaces its enterprise

search engine technology.

– In 2008 the Head Office of Organization X embarked on an exercise of D&IM Improvement. The goals included increasing the number of resources, improving competency & skills for D&IM to support oil and gas exploration teams. Accountability frameworks were rolled out. Part of this change involved the provision of D&IM to teams through a central service function, with support staff co-located with geoscientists in exploration teams. D&IM staff report to the central D&IM function, not to business management.

– In 2012 there were a number of staff changes in Organization X. One of the new team leads did not invite their data manager to team meetings or include them on the email distribution lists for their team or their away days. The Data Manager interprets this as a ‘sign of disrespect’, undermining their ‘authority’.

Results

• Edge of Chaos– Business Leadership pay less attention to D&IM, perhaps due to a combination of

business & time pressure and a view the new D&IM function can ‘do it all themselves’. “there been less emphasis on data discipline here coming from leadership” [P3].

– Formal technology auto-generated quantitative metrics reports for Data Quality, & Records Management (RM) are automatically generated. These are mainly coarse volume based but these do not always tell ‘the whole story’. Metrics can show ‘green’ but sometimes may not convey the usability of even findability of the information.

– Management (which can be seen as a hierarchy (fractal-like)) also do not pay sufficient attention and the message that ‘this is important’ is not conveyed systemically to staff. It is unclear if this behaviour can be described as paying ‘lip service’ to accountability (which would break the integrity core value) or it is down to lack of communication and engagement.

– Under increasing workload and tight timeframes, staff prioritize ‘short term’ over ‘long term’ activities. As stated by a Geoscientist, “So the time pressure is undoubtedly a big factor. There will be some people who frankly will be sloppy and not care regardless. There will be the other people who would tidy up after themselves if they had the time to do that but we are so under-staffed most of the time and running headlessly to deliver to the next deadline that it never reaches the top of the priority list”, [P3]

Results

• Edge of chaos– The data manager feels, “We are very much outsiders support staff deemed as

assistance. Not on email lists, do not attend meetings. They did not take me on their team away day”. [P1]. Feeling they have diminished authority (and respect), the Data Manager moves to a more reactive role, less challenging and cajoling. They lightly ‘police’ the shared drive, Geoscientists under increasing time pressure do not spend the time to help organize. “I don’t think anyone would be shocked, we know we need to archive properly but seeing it really happening is different story” [P8]

– Business management do not notice, a Data Manager comments, “Sometimes I am not aware that the project is closing out. The input from the team on this part is very small. I would say that they don’t really care as its not in their immediate need for anybody to put pressure on archiving of course” [P3] “I think what many people lack including me is more strictness on how every person works.” [P5]

– Geoscientists want Data Managers to be more proactive, “I do think that data managers have to be policemen to keep us techies in line” [P5], “it feels as if the data managers are a bit less proactive. They don’t feel to me like they are part of the team” [P3]

– The Data Manager goes to lunch most days with peers, the topic of ‘lack of respect’ begins to resonate with others “they are stuck in their ways” {P1]. Actions of individuals may turn into a behaviour of a group. Some practices ‘descend into chaos’.

Results

• Iteration and Bifurcation– Key information begins to no longer end up where they should be and/or named

properly. The team ‘as a whole’ now has inconsistent IM behaviours.

• Implications– “We have small black holes in our datasets” [P7], “The data interpretation that was done

was lost. We never found it back” [P6]. “Clearly if I can’t find all relevant information then technical results will be out…then business decisions will obviously be affected ..you wouldn’t know by how much. Hard to quantify if you don’t know what you’re missing.“ [P4], “[Lack of archiving]..Implications are huge. But not in the immediate term” [P5], “always coming across things that you would like to have known about earlier” [P3], “it becomes an issue when some years later you are looking for the archived thing. Someone else’s issue and that’s exactly the case.” [P3]

– Simulating events in the future: Future geoscientists use the enterprise search engine to find information for a commercial bid (worth $Millions) in Country Y. They find relevant content but not some crucial information because it was not named correctly and/or put in the right place. Critically, they don’t miss what they don’t know exists. A potential multi-million dollar incident is in the making. In oil and gas, there is evidence that in rare occasions blocks not taken up by one company, are turned into $Billion finds by another.

Results

• The ‘information system’ appears to show signs of complex system behaviour. ‘Perceived Lack of Respect’, ‘Short-term Management Behaviour & Attitudes’ and ‘Perception of work overload’ may be ‘strange attractors’ governing behaviour.– Un-predictability

• It could be argued that centralizing IM activities may create a dissociation from the business which is entirely predictable. However, it is unlikely the decentralization by itself led to the new ‘strange attractors’.

• Information practitioners and discipline chiefs were surprised at the study findings, providing evidence that existing Formal dominated control ‘norms’ (e.g. quantitative metrics) were not adequate to sense the IM and Enterprise Search capability environment.

– Sensitivity to initial conditions• Simply removing someone from an email distribution list may result in $Million losses

– Self-organization and emergence• Some examples of self organization were observed e.g. all the data managers going to lunch

together, all the geoscientists going to lunch together, rarely did ‘multi-disciplinary’ teams go to lunch together, reducing potentially ‘connectivity’. Wider community of interests for IM practitioners appeared limited with the exception of a few Yammer style online discussions.

• Areas of creativity were highlighted where formal processes needed to be improved (adapted). e.g. automating file-naming in specialist software to improve standardization and save time.

Discussion

• Further evidence of the impact of ‘short term’ thinking on ‘information discovery’ was observed:– “I think we are not using the data in the database that we have across the company

being able to pull things out of different databases and analyze them interactively is basically where a lot of industry is nowadays big data and all of that I don’t think we have that ability”

– “It consumes a lot of time to keep up with what the competitors are doing. You can find so much with Google but invest lot of time and I personally don’t put enough time in this.”, “there’s a plethora of scientific reference databases out there and I don’t have the time to look through all of them”

– “90% of time fighting fires and very little time to stop and think”• According to (McElroy 2000) “Complex systems innovate by producing

spontaneous, systemic bouts of novelty out of which new patterns of behaviour emerge”. Some innovative companies allow a percentage of work time to think of ways of saving time and/or creating value through new ideas. However, from the data provided by the case study organization, both scientists and data managers are so time poor, their capability to innovate appears to be seriously diminished.

Discussion

Agents

BehaviourPatterns

Data Manager’s perceivedlack of respect from

colleagues/customers

The ‘strange attractors’ (in red) have been derived (thematic mapping) from the interviewsIn this context, the relationship between agents is perhaps more important than the agent itself

Short-term Management Behaviour & Attitudes

Perception of Work Overloadby staff

StrangeAttractors

Discussion – Toy model: Attractors

Fully optimized organization (unlikely to be cost effective). A fallacy to believe we can completely control a complex system?

Time (Years)

Effec

tivel

y or

gani

zed

Info

rmati

on (e

ffort

)

0 n

Discussion – Model 1 Fully optimized

Time (Years)

Interventions

0 n

Reliance on coarse quantitative metrics andaudits. Rather extreme ‘ebb and flow’ ofbehaviour and process degradation, largeefforts needed to return to previous levelsof organization.Eff

ectiv

ely

orga

nize

dIn

form

ation

(effo

rt)

Increasing potential for catastrophic ‘black swan’ events caused by poorly organized information culture

Discussion – Model 2 Over reliance on Formality

Time (Years)

Interventions “Goldilocks zone”

0 n

Avoiding the extreme ‘ebb and flow’ ofbehaviour and process degradation. Lowenergy required to return to previous levelsaccepting ‘the system’ does not always adaptin desirable ways.

Effec

tivel

y or

gani

zed

Info

rmati

on (e

ffort

)

Combining quantitative and qualitative information on Formal and Informal environments

Zone ofManagement

ProbeSenseRespond

Discussion – Model 3 Zone of Management

Time (Years)

Effec

tivel

y or

gani

zed

Info

rmati

on (e

ffort

)

0 n

InterventionsInterventions

An organization is likely to be heterogeneous and contain many models, by business process, by the nature of information content, by perceived risk, by sub-culture etc. in different quantities

Discussion – Model 4 ‘Peak Trough’ Combination

HOLISM(Informal (Social), Formal & Technical)

PROCESSFOCUSED &CHAOTIC

PROACTIVE INTERVENTION(Probing, sensing, responding)

REACTIVEINTERVENTION

OUTCOMEFOCUSED &OPTIMISING

REDUCTIONISM(Focus only on the part e.g. IT or Formal)

PROCESSFOCUSED &OPTIMISING

OUTCOMEFOCUSED &DIMINISHING

Complex system & reflective,beliefs. Embracing inevitability ofignorance, fallibility anderror. Continuous qualitativeand quantitative sensing,experimentation, learning and adapting.

Typically IT centric beliefsEnterprise Search Centre of Excellence. Proactive, but generallysingle loop learning

Systems Thinking Beliefs,Focus predominantlyon Formal Quantitative Metrics reports and Episodic Audits

Belief that‘the systemwill work more or lessas designed’

Discussion – Belief Capability Models

“First Generation”

Reductionist Thinking, Technology Centric view of enterprise search & discovery

capability

“Second Generation”

Complex Systems Thinking view of

enterprise search & discovery capability

Double Loop Conceptual ChangeSearch & DiscoveryCapability Beliefs

Discussion – A conceptual change required?

May more accurately model ‘reality’ leading to

improved outcomes

Single Loop Expectations not met,

models follow departmental and budget structuresPurchase & deploy more search and/or

content management technology

• The integration of Complex System, Organizational Semiotics and Learning Theories offers a unique perspective on the information system. The combination of approaches ensures all aspects of the organization are considered, recognizing the sensitivity of the system to initial conditions and adaptive nature of organizations.

• It is not trivial to assign direct causes to effects in complex systems and to address the ‘wicked problem’ which is ‘Enterprise search & Discovery’. However, there may be behavioural patterns (sub-cultures) that are more likely to give rise to conditions which may lead to favourable enterprise search and IM outcomes. The evidence from this study, supported by the literature indicates IM systems can be unpredictable and do not behave ‘as intended’ and this often comes as a surprise to both practitioner and senior management.

• Adopting a belief system and culture that encourages reflectivity and embraces the inevitability of ignorance, fallibility and error in the information system, could lead to more effective information approaches to both managing information and searching for it. Organizations may benefit from a more enlightened ‘second generation’ view of enterprise search & discovery capability; shedding its reductionist, technology centric ancestry in favour of a complex systems approach. All things equal, organizations which think and act in this way may cultivate ‘better’ IM, enterprise search & discovery trajectories than those which do not.

Conclusions

• The complexity of an organization means that a case study may only sample a small subset given the constraints of time and availability of staff.

• Case study information may not be transferable or made generalizable to other contexts.

• The views of the scientists and Data Managers is their subjective view. No ‘story’ is the ‘full story’. any story is a partial story, not the ‘truth’.

• The researcher has further superimposed their subjective view in how they have interpreted this qualitative data (although this is not different to any other study of qualitative data).

• Epistemologically, taking a pragmatic philosophy, the conclusions are warranted assertions, a plausible explanation for what has been experienced is offered, not necessarily what is true.

Limitations

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References and Further Reading

• Thank you to Professor Simon Burnett and Fionnuala Cousins for their input into this discussion piece.

Acknowledgements

Paul H. Cleverleywww.paulhcleverley.com

http://www.rgu.ac.uk/dmstaff/cleverley-paul

p.h.cleverley@rgu.ac.uk