2019 IT trend: Enterprises will enter an age of Information Enlightenment

As data volumes and data correlations explode, enterprises must react faster and faster to the data to capture its value, especially since its value decays over time. The value may be highest right when the data is created but much lower only seconds or minutes later, and have different values depending on the context.

So in 2019, leveraging information will become a core competency of successful companies. In this age of Information Enlightenment, companies will place a premium on information ecosystems and set out to build more effective data supply chains. For example, in 2015 IBM bought The Weather Company to improve the data supply to IBM Watson. Over the next year, other companies will make similar moves.

Companies will understand their information ecosystems better, and what to do to make better, faster data-driven decisions. Machine learning (ML) will be key, to train systems and speed response times. Enterprises will realize it’s sometimes better to take action based on a strong probability of being right (e.g., 70 percent) than hold out for perfect (100 percent). That means paying attention to how ML rules are built.

Artificial intelligence (AI) will come of age as it gets more baked into applications. Whereas ML produces a result based on historic data it learns from, AI can provide an intelligent response. ML might tell you that Sue typically takes three days to review a report, whereas AI might send a report to Sue’s assistant because Sue is traveling and it will likely take her six days to review the report. AI takes what ML “knows,” factors in other information, and exhibits intelligent behaviors.

Data gravity and data decay

Information-enlightened companies will continue to borrow concepts from physics. Last year, we predicted that businesses would begin to understand that data has gravity — where large volumes of data are unavailable anywhere other than at the enterprise edge. Thought leaders like The Register began warning about the effect that the location of your analytics could have on the effectiveness of your insights, and the need to move analytics closer to the data to reduce latency.

This year, businesses will start to understand that information decays. Its value depends on how long it takes to collect data, create an insight and take action. Companies will begin to understand that data has a half life and that the window for time-to-action shrinks significantly depending on what that half life is. And further, they’ll learn that the half life for a single piece of data can change depending on the context.

Data science metrics become business metrics

Much of the Information Enlightenment will be driven by advancements in ML and AI. Most businesses have not yet discovered the powerful effect that ML and AI can have on a business model. Over the next year, companies will discover that some ML and AI metrics are also business metrics. ML and AI can improve service offerings and generate new sources of revenue but only if you have the right algorithms, model orchestration, data and infrastructure. Companies experiencing the Information Enlightenment will elevate metrics like Lift, Root Mean Squared Error, and Information Gain from obscure data science concepts to key business metrics.

2019 will mark the beginning of the age of Information Enlightenment. Companies will realize both the potential value of data and the challenge of data decay, and will begin to invest accordingly.

Also see the 2019 Digital Trends blog post.

Jerry Overton is a DXC Fellow and a data scientist in DXC Technology’s Analytics group. He leads the strategy and development for DXC’s Industrialized AI offering. An author and instructor, Jerry blogs at Doing Data Science, where he shares his experiences leading open research and transforming organizations using data science. @JerryAOverton

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