Analytics, Ethics, And AI – So What?

Marc Teerlink

A whopping 50% by 2022! This shocking percentage is the projected number of business processes that will be fully automated by 2022, compared to today’s average 30% automation of processes. Most of the advantages in digital transformation are enabled by task augmentation through artificial intelligence (AI) or through AI-powered robotic process automation (RPA).

Predictive analytics has evolved in recent years from rule-based to advanced data science, recently adding AI and machine learning models that analyze data, make assumptions, learn, and provide predictions at a scale and depth of detail impossible for individual human analysts.

Using such vast quantities of data in automated processes is already having a massive impact on business and society. Many analysts have projected that by 2020, around 70% of the data that a company uses will come from Internet of Things (IoT) devices and external data streams – in other words, external, non-transactional sources.

This rising impact can be both a blessing and a strong concern. It is a blessing, for example, when AI and predictive analytics use Big Data to monitor growing conditions, helping an individual farmer in India, Africa, or China make everyday decisions that can determine whether he will be able to feed his family. Yet it can also be a real concern when biased information is applied and the results are jettisoned at warp speed via social media.

This raises the issue: What happens when transparency and data quality, ownership, and governance are insufficient?

A core question that companies need to ask relates to their data monetization competence: Is data the core asset that I monetize? Or is data the glue that connects the processes that have made my products or services successful? (See one of my previous blogs for more details on this.)

This is especially urgent as companies start to use third-party data sources to train their algorithms – data that they know relatively little about. Companies need to ask critical questions such as:

  • What is the quality of the internal and external data we’re using to train and to input algorithms?
  • What unknown and unintended biases could our data train into algorithms? How will machines know the biases they operate under if we don’t share how algorithms arrive at answers?
  • What will the impact of this automation be on our business, people, and society? How can we detect and quickly mitigate unanticipated impacts?

In terms of accountability and ownership, this begs the question of creating algorithms in a black box. How does artificial intelligence arrive at its decisions and recommendations? And who within our organization owns this process (and when things go haywire with unintended outcomes, who is accountable)?

Already, 22% of U.S. companies have attributed part of their profits to AI and advanced cases of (AI-infused) predictive analytics. According to a recent study SAP conducted in conjunction with the Economist’s Intelligent Unit, organizations doing the most with machine learning have experienced 43% more growth on average than those that aren’t using AI and ML at all – or are not using AI well.

One of their secrets: They treat data as an asset, the same way organizations treat inventory, fleet, and manufacturing assets. They start with clear data governance with executive ownership and accountability. Because no matter how powerful the algorithm, poor training data will limit the effectiveness of AI and predictive analytics.

Bottom line

What’s the takeaway from this? We need to apply and own governance principles that focus on providing transparency on how artificial intelligence and predictive analytics achieve their answers. Transparency, data quality, ownership, and governance make all the difference for success with this.

Here is one question (borrowed from John C. Havens’ Heartificial Intelligence) to ponder when thinking about how to treat data as an asset to drive success with predictive analytics and artificial intelligence: How will machines know what we value if we don’t articulate (and own) what we value ourselves?

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This article originally appeared on the SAP Analytics blog and is republished by permission.

Marc Teerlink

About Marc Teerlink

Marc Teerlink is Global Vice President of Intelligent Enterprise Solutions at SAP. He drives the strategy, vision, and production of intelligent technologies delivered through the SAP Leonardo Portfolio. Prior to his current role, Marc was IBM Watson’s Chief Business Strategist, where he oversaw the new offerings portfolio for the Watson platform during IBM's formative years of artificial intelligence. During his time at IBM, Marc executed a number of successful transformational projects and created and delivered cognitive computing solutions and services offerings. Before IBM, he built expertise as a banker, consumer products business manager, consultant, and change leader within nine countries across three continents