In 1962, IBM introduce its 7094 mainframe computer, which became the world’s fastest computer. It was used by NASA for the Gemini and Apollo space programs and by the Air Force for its missile defense systems [1]. IBM was a large company and to build the 7094 required good technical talent, good technical leadership, and good governance.
A year later in 1963, the Control Data Corporation (CDC), a small company with only 34 people, including the janitor, released the CDC 6600, which is commonly viewed as the world’s first supercomputer [2]. It was designed by Seymour Cray, one of the co-founders of CDC.
The CDC 6600 was much faster than the IBM 7094. The senior executives at IBM were surprised that such a small company could build such a fast, innovative computer. At that time, CDC was small enough that no governance was needed. Seymour Cray’s dislike of bureaucracy was well known. When required to write a five-year plan for CDC, he wrote: “Five year goal: Build the biggest computer in the world. One year goal: Achieve one-fifth of the above.” Building the CDC 6600 was a project for CDC, although a high risk project. Seymour Cray and his team had all the resources that they needed.
This post is about governance for analytic and AI projects. Unless you have a Seymour Cray and a small team that has all the resources that you need, analytic governance is often what determines whether your analytic project will succeed.
In particular, analytic governance often determines whether your analytics project gets the data it needs, whether the analytic model you develop is biased, whether the model gets deployed, whether the project that you are working on delivers the business value it promised, and whether the business value gets recognized.
First, let’s define analytic governance. A standard definition of IT governance is [3]:
- Ensure that the investments in IT generate business value.
- Mitigate the risks that are associated with IT.
- Operate in such a way as to make good long-term decisions with accountability and traceability to those funding IT resources, those developing and support IT resources, and those using IT resources.
We could simply replace IT with analytics to get a definition of analytic governance. On the other hand, I have found in my experience that in practice it is quite useful to add one more component to the definition. As we have discussed several times on this blog, a useful tool for implementing analytics in a company or organization is the analytic diamond, which provides a framework for integrating analytic infrastructure, analytic modeling, analytic operations, and analytic strategy. For this reason, we use the following definition of analytic governance [4]:
- Ensure that good long-term decisions about analytics are reached and that investments in analytics generate business value.
- Manage the risk and liability associated with data & analytics.
- Operate in such a way as to make sure that there is accountability, transparency, and traceability to those funding analytic resources, to those developing and supporting analytic resources, and to those using analytic resources.
- Provide an organizational structure to ensure that the necessary analytic resources are available, that data is available to those building analytic models, that analytic models can be deployed, and that the impact of analytic models is quantified and tracked.
At the minimum, the analytic governance structure at most companies should include the following:
- Analytic Governance Committee of senior stakeholders
- An Analytics Technical Policy Committee of those involved in developing technical policies
- An Analytics Security and Compliance Committee of those involved in security and compliance for analytics projects
In addition, if there is not already a data committee and/or a data quality committee that is part of the IT governance structure, then this should be added also. Also, some companies would benefit from a cloud computing committee depending upon where they are in leverage commercial cloud computing service providers.
You can find more about analytic governance in my forthcoming book the Strategy and Practice of Analytics, and a bit about it in my primer: Developing an AI Strategy: A Primer.
I’ll be giving a talk about analytic governance at The Data Science Conference (TDSC) that will be taking place on May 18, 2020. Normally, the conference was planned to be held at the Gleacher Center in Chicago, but this year due to the stay at home order, it will be a virtual event.
References
[1] Phil Goldstein, How the IBM 7094 Gave NASA and the Air Force Computing Superiority in the 1960s, FedTech, https://fedtechmagazine.com/article/2016/10/how-ibm-7094-gave-nasa-and-air-force-computing-superiority-1960s
[2] Toby Howard, Seymour Cray: An Appreciation, http://www.cs.man.ac.uk/~toby/writing/PCW/cray.htm. This article also appeared in Personal Computer World magazine, February 1997.
[3] Allen E Brown and Gerald G Grant. Framing the frameworks: A review of IT governance research. Communications of the Association for Information Systems, 15(1):38, 2005. Available at: https://aisel.aisnet.org/cais/vol15/iss1/38/
[4] Robert L. Grossman, Developing an AI strategy: A Primer, Open Data Press, 2020, available online at analyticstrategy.com
The image of the CDC 6600 is by Jitze Couperus, Flickr: Supercomputer – The Beginnings, (License: CC Attribution 2.0 Generic). Also https://en.wikipedia.org/wiki/File:CDC_6600.jc.jpg