Archives For Operational Strategy
How you will manage your operations to execute your strategy. This is also a plan with a payoff, but the next layer down in specifics so that you have the detail for how you will achieve your strategic objectives.
After 20 years of building and deploying predictive models into core business processes for insurance, the focus of data science on model accuracy is much overrated. And that the team should spend much more time on how the model will improve business process flows. This paper is an introduction to connecting analytics models and AI agents to process flow analysis to achieve better business outcomes.
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‘Working’ Analytics: a useful term that distinguishes building deployable models that solve problems with a minimal amount of cost and complexity. Almost by definition, ‘Big’ is not ‘working’ analytics; it’s something else. When things get big, they get costly and complex. They get impractical to operationalize much less gain useage in day-to-day operations. A foundation principle for data-science that pre-dates ‘BIG’ is parsimony, also known as Occam’s razor.
For data scientists, ask yourself whether you want to be a ‘working’ practitioner or a developer of complex, inexplicable and mostly unused solutions. You can certainly make complex solutions but your job is to make them simple.
For employers, it is temping to believe in ‘unicorns’….a wickedly complex algorithm that creates a discontinuous shift in your industry and crushes the competition for years to come. But think about hiring people with the attitude and habit of contrarian thinking (e.g. putting a camera on a phone). Hire a blend of ‘working’ practitioners with a philosophy of parsimony, and ‘explorers’ who will thrash data and models regardless of where it takes them.
There are many, many working problems to solve while you are looking for your unicorn.
On this subject, a useful (and challenging) concept from Oliver Wendell Holmes:
“I would not give a fig for the simplicity this side of complexity, but I would give my life for the simplicity on the other side of complexity.
Edward H. Vandenberg
Your stakeholders lack a shared understanding of the methods and practice of advanced analytics. You start out with a trust deficit when explaining how the mathematics will improve business results.
To build trust, start in advance by building an ordinary business relationship to the operations management. Next share stories of analytics successes and how they were achieved (ideally those that you have directed). Next coach your stakeholders to interpret model results by simplifying the complex model validation process.
Gradually build an Arena of shared understanding for how models can help operations arrive at a better performance state. This is hard work and not the stuff of algorithms and data but almost as important.
Look at the Jahari Window for expanding the Arena of trust.
Edward H. Vandenberg
Over the next few years, advanced analytics will emerge as a separate corporate function in some leading companies. The activity is a mix of IT, Finance, Process Management and Knowledge Management that simply defies the current corporate functional matrix. Add to that a highly skilled work force whose ethical behavior is (will be) a matter of corporate ethics and law. It is a strange prediction, admittedly. But Finance, HR, and Accounting started somewhere. And that is where advanced analytics is now.
Astute Analytics Executives will position themselves for this evolution. It is a positive development for the firm, what ever industry you work in. Analytics is the customer interface in a mass market environment where the preferences of one is the required service or product model.
Share things that can (must) be shared
- Specialized Talent
- Infrastructure
- Some datasets
- Tools
Focus services to deliver on demand
- Domain knowledge
- Data and systems expertise
- Capacity for high demand customers
Standardize things that will help deliver consistent quality
- Methodology
- Project Practices
- Role Descriptions
Give synergy to the effort
- Complimentary skills and knowledge
- Knowledge sharing and imagination
- Contrarian viewpoints
Control Risks Formally
- Skills definition
- Independent Quality Review of the models and interim work products
- Checks on conflict of interests and influence
- Management accountability (project level)
- Sign-off and approval process
- Ethical Standards
Develop Enterprise Assets
- Reusable datasets
- Documented models
An Enterprise Identity and Voice
- Organizational voice
- A place for people with unique skills to belong
- Promote identity, value and scope of the work
- Tell the story to the enterprise
A PRACTITIONER’S GUIDE TO BUSINESS ANALYTICS: Using Data Analysis Tools to Improve Your Organization’s Decision Making and Strategy: Randy Bartlett: 9780071807593: Amazon.com: Books
Thanks to Randy Bartlett for writing this book published earlier this year.
There is lots of content and practical knowledge for analytics executives to review and absorb, written from the perspective of experience. Especially important is Randy’s frequent references to the business strategists and authors whose foundation wisdom is very much relevant to advanced analytics. Randy quotes business greats Deming, Bennis and others. Their work predates data science science but their frameworks and foundation principles are important for establishing data science as an integral function within the business enterprise.
Though data sciences is not new, it is emerging as a unique business function as fundamental as accounting, finance and information technology. Those functions have deep roots and are accepted as necessary value-chain activities. Data science differs from these business functions in important ways that make its growth much more challenging and exciting.
If you are shaping your future career as an analytic executive, save yourself some cycles by reading this book and thinking through its application to your work.
Micheal Porter’s Five Forces is a model of the large influences of a business. This model has influenced business thinking for decades. Frameworks like this are useful in abstracting complex subjects and organizing thoughts. In honor of Micheal Porter, here are the large influences or ‘forces’ that influence advanced analytics. You must account for these in developing your enterprise strategy and operational goals.
1) Data Quality
2) Ethical judgement and practice
3) Computing Power
4) Organizational Adoption
5) Social Adoption
6) Intellectual capital
7) Cognitive decision-making
I will elaborate on these in future posts. I invite readers to comment. The exercise is as much about a framework for discussion as the content itself. A framework never tells the whole story.
The Analytic Executive 