Seven Questions on Adopting Analytics Culture (0 of 7)

Seven Questions on Adopting Analytics Culture



Seven questions are posed and will be addressed in serial.  The theme: ‘how can organizations adopt analytics-based decision making culture?’  In particular, the questions address the use of change management to adopt evidence-based decision making, associated organizational challenges, and how analytics can be used to manage organizational change itself:

  1. What does change management have to do with business analytics?
  2.  Is change management effective?
  3. How does change management work?
  4. Why is the change management track record so poor?
  5. How can change management be improved via analytics? 
  6. What particular information is gained from social network analysis and how is it interpreted? 
  7. How can social network analysis facilitate change programs to adopt analytics culture? (pending July 2013)
decision analytics

Analytics-driven Decision Making

We begin with an introductory question: what do we mean by ‘analytics culture’?  A set of recent MIT Sloan Management Review articles identifies ‘analytics maturity’ as being composed of a combination of: 1) tools and expertise, 2) information management practices, and 3) analytics culture – the broad organization embracing the notion that analytics-driven insight can and should drive decision making (Kiron and Shockley 2011; Kiron, Shockley et al. 2011).

This last factor, analytics culture, is the most difficult to adopt.  Tools can be purchased, experts hired and trained, and processes implemented based upon well-documented best practices.  There is a great deal of guidance available concerning technical and methodological best practices to derive value from analytics. However, in the final assessment, analytics provides guidance only – it is a form of informed advice to support decision making.

The crucial ‘last 10 yards’ of analytics depends upon old fashioned, well-functioning organizational decision making: organizations must frame business problems appropriately, assess alternatives, experiment, interpret guidance from analytics insight, make clear decisions based on the resulting insight, and commit to appropriate reactive action.  This complex, organizational context and implementation, is where analytics initiatives typically sink or swim.

Evidence-based decision making driven by business analytics, if implemented properly, amounts to an organizational paradigm shift in business management.  The traditional organizational management paradigm we will call the ‘Anglo-Saxon business model, as it derives from over a century of British and American corporate management tradition, although this is now the trans-cultural model typifying modern multinationals.  This model places hybrid manager-experts in positions of power, positions which command decision making rights, access to information, control over resources, and discretion over the expenditure of firm capital. It is asserted that the Anglo-Saxon management paradigm, as ruled by manager-experts, is baked into the typical corporation.

The judgment of manager-experts is final and, further, it is jealous: it views dissent as a challenge to vested power and authority.  Where problems are inherently complex (as they are increasingly so) or where many variables are at play (as they are increasingly so) the raw experience and intuition of the expert-manager is the final judge.  In ‘the fog of war’ that is the status quo of modern global business, the intuition-driven manager-expert is the final stop-gap, the judge, jury, and executioner of last resort.

In contradistinction, the evidence-based decision making paradigm has emerged from high-risk/high-impact decision environments such as hospitals and military theaters (the medical field having coined the term). In environments where decisions have heavily consequential implications, margins of error are slim, and human lives are at stake, there are moral and ethical pressures to second- and third-guess decisions.  When lives are at stake, there is a focus to ensure the very best decisions, based on all available evidence and established best practices, are made.  For more information, see:

While not denigrating the importance of business, the lack of serious consequences combined with time pressures in large commercial organizations often can be a detriment to adopting informed and careful decision making.  Missed earnings, shoddy planning, failed mergers, market missteps, bad investments, poorly considered reorganizations, and inefficiency can often be excused-away. Often, there it is difficult to ‘blame’ a single party, or even to attribute a single cause or decision to bad outcomes.  Indeed, when there are business scandals, although wrists are slapped and reputations take a hit, it is rare that business leaders are criminally punished.  Leaders can always claim that were attempting to act in ‘shareholder best interests’.

In many day-to-day business settings, there is a lack of general appreciation for scientific rigor in decision making and planning.  Hasty decisions are often excused by the omnipresence of ‘time to market’ pressures.  Intuition-driven managers typically can skate by on ‘good enough’, and, in the worst case, find another job if things go south.  There is always another round of manager-experts willing to try their hand guided by the fortune of rough intuition.

However, as shareholder value is next to godliness in business, a new generation of businesses have emerged which place data analytics in the forefront as a driver of core value.  Here we simply mention Wal-mart, Amazon, Apple, FedEx, and Google as clear examples.  The pure value-creating power of analytics-based management in these firms has been difficult to ignore – they represent a clear new breed of data-aware, evidence-driven business.  With these firms in the vanguard, analytics-based decision techniques, supported by computational power and structured methods, have emerged as a challenge to the paradigm of the intuition-driven manager-expert.

Within the past five years, a veritable hype has developed concerning the power of business analytics, sometimes popularized under the rubric ‘Big Data’.  A positive development is that the methods and means for conducting intensive data analytics have become much more accessible.  Research, training, manuals, books, best practices, methods, software, systems, processes, certificates, and degrees have sprung up which support workers inapplying intensive data-focused inquiry to business management and decision making.

However, it is not as simple as putting in place the correct technologies and hiring a few computational statisticians and data scientists. Managers, typically those trained in the Anglo-Saxon business paradigm, must make room for decisions to be guided based on the structured methods and principles of analytical inquiry.  This is a trickier proposition, as politics raises its ugly head quickly: managerial livelihood, power, position, authority, and prestige all are at risk when processes, methods, and computer-driven systems threaten the judgment of the deified manager-expert.

How can firms press the paradigm shift from intuition-based manager-experts to data-analytics driven, evidence-based management practices?

Buying software, fast servers, and hiring experts is simply not enough.  Firms must also lay the groundwork for analytics-based decision making within their firm.  There must be a paradigm shift concerning the contract between managers, workers, and the firm.  This can only come about via concerted corporate change management, structured programs to socialize and facilitate the paradigm of evidence-based decision making.

The difficult change is to indoctrinate a new generation of managers to the notion that it is fine to make mistakes during experimentation, that mistakes are information, that information (even if based upon a failure) reduces risk and creates value, and that a manger is tantamount to a business scientist: a professional chartered to experiment, gather data, realize small-scale failures continually (in the name of experimentation), and to socialize the results among a community of peers, with the objective being to make the best decision possible in the interests of the firm. Further, this new paradigm asserts the notion that managers, as a body, should stimulate informed debate in the interests of identifying the best decisions for the company, as opposed to pushing agendas, power politics, or pursuing rhetoric to the detriment of rigorous evidence-based inquiry.

How can organizations undertake and track change initiatives to manage this paradigmatic change, this cultural transition, within their organization?

Change management is the discipline which specifies a process for organizational change.  But is change management a truly effective method?  When asked his opinion on the changes wrought by the French Revolution in 1976, then Chinese Premier Enlai replied: “it is too early to tell”.  Albeit on a less grandiose scale, the effects of corporate change management initiatives are similarly often difficult to track and assess. This poses unique problems for organizations seeking to ‘change’ by developing organizational analytics maturity and by adopting evidence-based decision management.

Adopting business analytics programs and evidence-based decision making inherently involves organizational change management.  The linked set of posts following constitute an ‘idea piece’, supplemented with references, providing an overview of key challenges and emerging approaches for those contemplating or planning a corporate change program to foster ‘analytics culture’.

Corporate change initiatives are complex undertakings with unclear outcomes, largely difficult to manage, implement, track, and measure. However, promising new social network analysis (SNA) based methods are emerging which can be used to structure and guide change programs. To address the challenges posed by analytics focused change management, a structured series of questions will be addressed:

  2.  Is change management effective?
  3. How does change management work? 
  4. Why is the change management track record so poor?
  5. How can change management be improved via analytics?

The relevant posts are numbered in their title following this post. Hopefully they will be of interest and provide value to those interested in adopting analytics cultural change in their organization.  Your comments and feedback are welcome.


Kiron, D., & Shockley, R. (2011). Creating Business Value with Analytics. MIT Sloan Management Review, 53(1), 10.

Kiron, D., Shockley, R., Kruschwitz, N., Finch, G., & Haydock, M. (2011). Analytics: The Widening Divide. MIT Sloan Management Review (Special Report), 21.

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