Establishing an Algorithm governance strategy: Best practices-Part (I/II)

Algorithms are the future and will become critical assets for organizations in the near future

If you have been following the much deserved hype around Machine Learning and Artificial Intelligence, you are no stranger to the fact the organizations of tomorrow will be run algorithms (of course “trained” and managed by human intelligence).

In fact, many companies that started early be integrating algorithms in their core operating strategies have allowed them to scale in ways that would not have been feasible without the help of algorithms. Typical examples are likes of UPS and Google.

Given the scale of UPS’ operations, where its fleet of 55,000+ trucks makes 16 million deliveries daily, even slight operating inefficiencies can cost millions of Dollars. However, by applying telematics and algorithms, UPS saves its drivers 85 million miles a year, which translates into approximately $2.3M in savings.

Corporate is already aware of the criticality of algorithms

Almost every company in the world realizes the importance of developing algorithms that help run their business. And most recognize that the convergence of artificial intelligence, (AI)/machine learning (ML) technologies, the explosion of data from sensors and IoT devices and new information from social media and other sources make it the right time to invest in a more integrated algorithm strategy.

In the future, markets will be won by companies that effectively create, manage,
and deploy algorithms for everything from customer demand sensing to data cleansing to inventory management. Companies that are faster and smarter at developing and managing algorithms, proprietary and public, are going to win in industry after industry.

Why are companies struggling to implement algorithms that work?

  • Lack of consensus about what algorithms are, a lack of visibility in the company
    about which algorithms are effective and how to utilize them across an enterprise
    for extended functional value (EFV).
  • Inadequate people resources that have the requisite data science skills.
  • Inadequate people resources including people that can be the bridge between
    business units and data scientists
  • Inufficient technology that is capable of collecting, cleaning and analyzing the vast
    amount of new data companies gather every day
  • Organizational boundaries that prevent the collection and sharing of new data inside the company and the creation of powerful algorithms that will win a market
  • Uncertainty about whether and how to share data with customers and suppliers

So what purpose will an algorithm governance team serve?

For many reasons, companies are organized into vertical silos like sales & marketing,
supply chain, finance, IT, risk management and HR. Each silo is measured by a sometimes conflicting set of priorities. For example, sales wants to grow sales and supply chain wants to increase inventory turns and minimize excess inventory.

Of course, everyone wants to help the company succeed, but perspectives, knowledge
and available data are limited in scope. We know that algorithms can successfully grow
a business and get better at doing so with every transaction. But the most powerful
algorithms take market data available to the sales team; combine it with operations data
available to the supply chain group, and customer experience information from a variety of sources.

For example, Amazon is a supply chain play. People are loyal to the Amazon shopping,
buying and fulfillment cycle. There are few barriers between operations and sales at
Amazon which has a well-developed set of algorithms that dictate what products you will
be interested in. Buy something on Amazon, and you will be told what other people like
you also bought. The Amazon “recommendations engine” is amazingly good at generating sales, as is the Netflix application in suggesting movies. The same principles can be applied to your marketplace.

Next..we will  discuss the best approach to set up an Algorithm governance strategy in part II of this post

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