Making the Most of your Predictive Analytics and AI: Start by Treating Data as an Asset!

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 have evolved over the last years, from rule-based to advanced Data Science – and recently adding AI (machine learning) models that analyzes data, makes assumptions, learns and provides 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 IoT devices and external data streams– in other words, external, non-transactional source.

This rising impact can be both a blessing and a strong concern. It is a blessing—for example when AI and Predictive analytics are using big data to monitor growing conditions, to help an individual farmer in India, Africa or China to make everyday decisions that can determine if he will be able to feed his family (or not). Yet it can also be 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 about which they know relatively less. Companies need to ask critical questions such as:

  • What is the quality of the data we’re using to train, and to input, algorithms—both internal and external data?
  • What unknown and unintended biases could our data train into algorithms? How will machines know under which biases they operate if we don’t share how algorithms arrive at its 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, it 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 then 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 who aren’t using AI and ML at all—or 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 Artificial Intelligence 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 its answer. Transparency, data quality, ownership, and governance make all the difference for this success!

I will close with asking one question to ponder when thinking about how to treat data as an asset in your organization, in order 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?1

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1 Borrowed from John C Havens “Heartificial Intelligence”