2018: The quantified enterprise — Stop guessing and start measuring

Predictions for 2018:

  • Companies will use the data from digital systems to quantify the business and become more productive
  • The desire to quantify the business will emerge as a primary driver of digital transformation
  • Companies, forced to rethink big data, will turn to advanced machine-learning techniques (like deep learning) to make high-quality decisions

Last year we predicted the rise of intelligent machines. Now, 76 percent of business decision makers say that AI is pivotal to the future success of the organization and 71 percent see the rise of AI as both positive and inevitable for business. This year, the focus is on the impact data and machine-learning technology have on enterprise productivity.

According to X-Prize founder Peter Diamandis, anyone with a smartphone today has better access to knowledge and information than the President of the United States had in the 1990s. Diamandis says that the number of connected devices will grow from 14 billion today, to 100 billion by 2020. We are moving to a world where we can learn anything we want, anytime we want, anywhere we want.

Quantifying the business

Imagine watching raw materials come in and out of competitors’ factories half way around the world. Imagine predicting a competitor’s sales by counting the number of cars in its parking lot. Most companies have already started building the kinds of digital infrastructures required to generate and store these vast amounts of data. The intelligence you get from these systems, however, depends on the questions you ask. Over the next year, companies will increasingly use the data from their digital systems to quantify the business and become more productive.

The best companies are more than 40% more productive than their peers — leading to operating margins 30%–50% higher. So the potential benefits are huge. When it comes to determining what affects productivity, companies will stop guessing and start measuring. Companies will shift from making decisions based mostly on stories to making decisions based on experiment and measured results. Quantification will emerge to empower employees to improve productivity.



Driving digital transformation

Quantifying productivity is difficult. Companies have struggled with how to measure business processes in ways that accurately reflect their value. Digital leaders will turn to skills training — of all kinds — to learn methods for designing business experiments and quickly defining performance metrics that indicate if the experiment is working. Over the next year we will see the desire to quantify the business shift from a benefit of digital transformation to one of its key drivers.

Rethinking big data

The need to quantify the business will force companies to rethink big data. Companies will have to become much smarter (and more deliberate) about what data they use and for what purpose. They will need to consider:

  • Data has gravity. Large volumes of data tend to accumulate in specific places and resist moving freely throughout the enterprise. For very large volumes, much of the data generated can become either unavailable to most or impractical to use for anything other than local decisions at the edge of the enterprise.
  • Data decays. The value of any given body of data decreases over time. A customer’s transaction data may, initially, provide insight into purchase intent but, over time, it becomes a less reliable indicator as the customer’s circumstances change.

Leaders quantifying the enterprise will have to adopt a careful and strategic use of data. Many will turn to advanced machine-learning techniques like deep learning to make high-quality decisions on less data.

Modern digital platforms produce a wealth of data about fluctuating workload patterns and the interactions of customers and partners. Using this data to measure important aspects of the business will become a new source of productivity. Companies that find innovative ways to quantify their enterprise will enjoy substantial competitive advantage.

Jerry Overton is a data scientist and senior principal in DXC Technology’s Analytics group. He leads the strategy and development for DXCs Advanced Analytics, Artificial Intelligence and Internet of Things offerings.

Jerry is the author of the O’Reilly Media ebook, Going Pro in Data Science: What It Takes to Succeed as a Professional Data Scientist. He teaches the Safari Live Online training course “Data Science at Enterprise Scale.” @JerryAOverton


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