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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Your analytics strategy is your high level plan to deliver significant value, the specific payoff for these plans, and the unique approach you will take given your organization, its industry and the state of your competitors activities. Analytics Strategy must be aligned with the overall corporate strategy.
‘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
Analytic Executives should be reading The Race Against the Machine, Brynjolfsson and McAfee. 2011.
I will quote from the book to raise the point that process re-engineering is critical to analytics return on investment.
“The most productive firms reinvented and reorganized rights, incentive systems, information flows, hiring systems, and others aspects of organizational capital to get the most from the technology…..The intangible organizational assets are typically much harder to change, but they are also much more important to the success of the organization.”
This is partly why analytics needs to rise to the level of a corporate function, with staff level executive leadership, so as to be able to move the organization to re-engineer itself for the technology.
Edward H. Vandenberg
IoT is your job. Data Science has always been greedy for complex data and has a pretty good handle on how to process it for insights and predictions.
For most of us and most projects, the practically of getting it to model and having it available to execute run-time algorithms has been the barrier. IoT data is meaningless without algorithms to process it and provide information and predictions/optimizations from it.
IoT is exciting and will change the fundamentals of businesses and industries. The technology is interesting and very dynamic. All of this has implications for your analytic operation and practice.
The more interesting and challenging future of IoT (and also part of your job): what are the new processes, user roles, use cases, management scenarios and business cases for IoT. Who will manage the IoT function ‘X’ of the future and what does that role look like.
The other important reason for you to pursue IoT is to keep your data scientists engaged and retained. Many are still working around the same types of projects, methods, tools etc. that have been around for 10 plus years. All projects are interesting and challenging but some of them are getting bored.
This means research and discussions with your colleagues, sponsors and stakeholders (while you are still working in the pre-IoT world). Enjoy!
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.
Don’t expect the new VP of Advanced Analytics to do it all. When processes and decision-making have to change to exploit insights from data, the line managers, with executive sponsorship, must carry the weight. Without that, you have models that work on paper but aren’t used in the operations. It’s tempting to blame the analytics leader but that is misplaced. Operations managers actually resist doing things in a new way, despite the math telling them otherwise. And the VP of Analytics likely has no power to change that, short of appealing to the executive staff–not a popular move for the analytics lead that also must evangelize for new projects and problems to solve.
Also, commanding a small specialized team is not normally seen as a position of clout, despite the title and sponsorship. Managers of large operations (financial and headcount) naturally have more influence in most organizations, even if they have a peer title to the advanced analytics leader.
In defense of the operations level manager, they are rightfully reluctant to be accountable for mathematics that is probablistic and hard to understand. Likely they are also normally protective over the operational influence that analytics can have within their business units. This creates tension that makes analytics ROI go sideways.
What’s the answer? For analytics to truly be exploited, operations management must step up….understand the science more, be ready to believe in it and lead their operations to adopting it. That means they are hired for it, trained for it and managed for it. How an organization mobilizes for that is under the leadership of a Chief Analytics Officer and a full program management approach to advanced analytics. Deploying advanced analytics must be seen as the path to promotion for career operations managers.
Secondly, the advanced analytics effort must include Test and Learn experiments for every model pilot that help prove the in-use value of models beyond validation on historic data. This is a natural extension of the model development work
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
Advanced Analytics should be an enterprise shared service. But build that with the end in mind. What is critical to be shared, either because of availability or quality? What is critical to be focused or distributed, because of demand, domain knowledge and legacy issues? You will find that there is more gained around shared aspects than lost when analytics teams are not dedicated to a specific function.
Getting to a shared service enterprise model can be challenging. I will advance this point of view in future posts.
Erik Brynjolfsson: “Big Data should be viewed as a management revolution”
This is why advanced analytics is something different. What other recent business innovation is so important and ubiquitous as to be labeled a ‘management revolution’ . As yet, there is very little structure and form to this revolution and few leaders who understand the technical foundation.
That is the subject of this blog.
The Analytic Executive 