In my October article, “Transforming the Organization Through Data and Analytics – the Role of Culture and OCM”, I recommended that “an organization’s culture is often too complex to attempt to change at the macro level; it is better to focus on a specific opportunity/problem and to adjust its associated cultural strands.”
In this article we’ll focus on defining the opportunity or problem and the nature of the change. We’ll also provide an overview of a framework for identifying “associated cultural strands”.
WHY? AND WHY NOW?
Creating a Data Culture implies we are going to change “the way we do things around here” with respect to managing and using our data and analytics resources and capabilities to innovate and transform our organizations. However, change is never easy!
If the change is going to be successful at all, we should start by asking two questions: “Why?” and “Why now?” That is: “Why do we want to transform (change) the organization, making it more data-driven, and why must this be done now?” What exactly is the specific opportunity or problem we are trying to address? Once we have a clear opportunity/problem statement, and we understand the business value that will be realized by addressing it, the next step is to classify and understand the nature of the change that will be needed.
‘DIFFICULT’ VERSUS ‘MESSY’ OPPORTUNITIES/PROBLEMS
Ackoff (1974) classified organizational problems as being either ‘difficult’ or ‘messy’. Paton and McCalman (2000) talk about ‘hard’ or ‘soft’ problems. Senior and Swailes (2016) provide a useful comparison between these types:
Hard/ Difficult/ Bounded
Soft/ Messy/ Unbounded
comparatively small and less serious in impact
serious impact for a large audience
can be isolated from their context
interrelated set of complex problems
many diverse opinions
subjective objectives are not easily quantified
focused on systems and technology
involving few people
little consensus on possible solutions
facts are well-known and solution is somewhat obvious
have been around for a long time and have remained unsolved
stakeholders agree on the definition of the problem
no hope for a complete solution; best outcome is an improvement
timescale is easily defined
fuzzy time scales
minimal interactions with the external environment
spread throughout the organization and into the external environment
Table 1: Problem typology – after Senior and Swailes (2016:57-58).
It’s likely that the above characteristics of opportunities/problems also apply to their associated changes. You’ll note that, according to the researchers, there is no such thing as an easy problem!
Paton and McCalman (2000) provide the ‘TROPICS’ test – a dimensional model that can be used to characterize the change, and to determine the best approach – see Table 2. The preferred approach for ‘HARD’ changes focuses on structures and systems. In contrast, Senior and Swailes (2016) advocate the use of a rigorous, culture-focused OCM approach for ‘SOFT’ changes.
Clear, short to medium term
Unclear, medium to long term
Common, shared by all
Lack of consensus
Entirely by internal management
Shared with outside stakeholders
Root cause inside the organization
Root cause outside the organization
Table 2: ‘TROPICS’ – after Paton and McCalman (2000).
I used the ‘TROPICS’ model in a survey conducted in September 2020, asking CDOs to characterize the opportunities/problems and associated changes that they were challenged with in their enterprise data and analytics programs.
A common pattern emerged. The respondents characterized their programs as having the following mix of ‘HARD’ and ‘SOFT’ dimensions: unclear medium to long term timescales, uncertain resource requirements, multiple diverse perceptions and a lack of consensus, limited defined interests, controlled entirely by internal management, the root cause being inside the organization. Many respondents reported that they were faced with subjective, ambiguous objectives, while some of the more mature programs had been successful at distilling a set of clear, quantified objectives.
What is interesting about this pattern is that although most programs appear to be inward focused with what seems to be (on the surface) limited, defined interests, those same interests become blurred through multiple stakeholders’ diverse perceptions, which in turn leads to a lack of clarity with respect to timescales, resources and objectives. Those organizations who are putting in the effort to establish clarity regarding their opportunity/problem statements and objectives, and who are addressing the underlying cultural strands, are likely to have the most successful transformation programs
ADDRESSING THE UNDERLYING CULTURAL STRANDS
While Schein (2017) focuses on beliefs, values and behaviors as the essence of Organizational Culture (OC), other theorists have noted that there are additional underlying cultural strands that must be considered. Strauss (2019:7) proposes the “OC Dynamics Model” (see Figure 1) which shows how contextual determinants of OC can be both influencers of, and be influenced by, the core OC (shown in the center of Figure 1). These relationships are like strands of interwoven fibers which need to be unraveled and individually addressed in order to facilitate cultural transformation.
Figure 1: The OC DYNAMICS Model – “an illustration of the dynamic 2-way relationships between contextual determinants and Core Organizational Culture”, after Strauss (2019:7).
A real-life example is global manufacturer ABC, which operates in 5 major regions of the world. Their global data governance initiative is experiencing cultural inertia in one of the five, namely region XYZ; this region believes that it doesn’t need to change anything regarding its data resource management practices. On closer examination, we find that each region has a distinct ‘national culture’ (‘CULTURAL INFLUENCER’) and has different leadership ‘styles’ (‘INTERNAL ENVIRONMENT’). Region XYZ’s workforce respects and follows strong leadership and seldom gives a bad report, or admits to any operational challenges, as that would be disloyal, reflecting badly on their leader and would not be culturally acceptable. In addition, even though the COVID-19 pandemic has placed stress on region XYZ, the ‘economic factors’ have remained stable (‘CLIMATE’), and the ‘output and performance’ (‘EXTERNAL ENVIRONMENTAL IMPACT’) of the company in that region has been remarkably good. Against this backdrop, it becomes clear why region XYZ assumes/believes that it does not have a data quality problem and no change is necessary. As long as these deep-seated beliefs and assumptions remain unchallenged, meaningful and sustained change will be illusive.
A rigorous, culture-focused OCM approach is needed in order to break through these underlying cultural strands. The region’s mental models must be challenged and this will take time to accomplish. Senior and Swailes (2016) propose an organizational development (‘OD’) approach to ‘SOFT/MESSY’ changes, borrowing elements from Kotter (1996), Paton and McCalman (2008) and Buchanan and McCalman (1989). We shall examine their approach in the third article in this series.
THE WAY FORWARD
CDOs that want to succeed at creating a data-driven culture will need to: (1) distill a clear opportunity/problem statement, (2) determine the nature of the change and (3) identify the “associated cultural strands”.
Ackoff, R. (1993) ‘The art and science of mess management’, in Mabey, C. and Mayon-White, B. (eds) Managing Change, 2nd ed, London: PCP
Buchanan, D., and McCalman, J. (1989) High performance work systems: the digital experience, London: Routledge.
Kotter, J. (1996) Leading Change, Boston, MA: Harvard Business School Press
Paton, R., and McCalman, J. (2000) Change Management: Guide to Effective Implementation, 2nd ed, London: Sage.
Schein, E. (2004, 2010, 2017) Organizational Culture and Leadership. San Francisco: Jossey-Bass.
Senior, B. & Swailes, S. (2016) Organizational Change, 5th Edition, Pearson Education Limited, Harlow, UK.
Strauss, D. (2019) Culture, Leadership & Innovation in Organizations, MMK003, 10 November, MBALIC, York St. John University, York, UK: Robert Kennedy College, Switzerland.
DEREK STRAUSS BIO
Founder, CEO and Principal Consultant of Gavroshe. Derek was Chief Data Officer of TD Ameritrade for ~5 years, through September 2016. He has over 3 decades of Data & Analytics experience, including Big Data, Information Resource Management (IRM) and Business Intelligence/ Data Warehousing fields. He established Data Resource Management and IRM Functions in several large Corporations using Bill Inmon's DW2.0 and the Zachman Framework as a basis. Derek established and managed numerous enterprise programs and initiatives in the areas of Data Governance, Business Intelligence, Data Warehousing and Data Quality Improvement. He is a founding member of MIT's International Society for CDOs. Derek has spoken at many local and international conferences on Data Management issues, including seminars in Europe and Africa. Derek co-authored DW 2.0: The Architecture for the Next Generation of Data Warehousing by William H. Inmon, Derek Strauss, and Genia Neushloss (Paperback - Jul 2, 2008).
Specialties: Zachman Framework for Enterprise Architecture, John Zachman
Certified Government Information Factory Architect, Bill Inmon.
Certified DW2.0 Architect and Trainer, Bill Inmon.