How the 2019 digital trends impact Asia – Part 2

DXC recently released six digital trends that will drive the market in 2019. In my previous post, I addressed three trends that center around “data gravity.” In this post, I want to tie together the other three trends:

  1. Enterprises adopt next generation IoT platforms.
  2. Enterprises enter an age of information enlightenment.
  3. Enterprises redesign customer experiences amid stronger data privacy rules.

These trends are about how information will be created, leveraged and secured in the coming year, particularly in Asia. To examine these trends, let’s use the analytics maturity model below. I use this model with customers to describe how analytics projects proceed.

Analytics Maturity Model (click image to enlarge)

 

Gaining visibility

As organizations recognize the value that data can provide, massive instrumentation of processes becomes a requirement. Customers in manufacturing are looking to instrument factory processes, supply chain processes and any other related processes to find areas to optimize for cost savings, quality improvements or any other competitive advantage. Healthcare enterprises are looking to collect richer data from an extended set of devices that reach deeper into the daily lives of healthcare consumers, including wearable devices and home healthcare monitoring devices.

In these and other industries, the dimensionality of the data extends beyond the limits of human comprehension. Therefore, in 2019 we expect to see next-generation IoT implementation to provide automation and data correlation that creates the proper level of visibility.

This is critically important because enterprises that are making decisions based on limited data visibility are creating significant risks. Blind spots in the information landscape can be probative in many business decisions, so decision-making with limited visibility is often no more effective than guessing. Worse, because enterprises don’t know what they don’t know, enterprises can project a level of confidence in their decision-making that is unwarranted.

For example, if an enterprise is trying to optimize its supply chain processes but does not receive data from a particular set of locations, the enterprise is unlikely to achieve the returns on investment that justify the cost of collection. Decision-making will be hampered by a significant information blinds spot, so what may appear to be a data-driven decision is really just a best guess.

Leveraging information for insight

The “Insight” phase of the project also carries significant risks. In this phase of the project, the enterprise gains insight from the information and begins to formulate action plans. These insights are often not particularly deep. Most of the customers I interact with find that the first set of insights they draw from the data sets they collect point in some obvious directions.

In one case, operational data collection showed conclusively that changing a subset of employees’ lunch hours would significantly improve productivity. No data science was required to gather this insight. A simple visualization told the story. This is where we see enterprises entering an age of information enlightenment in 2019.

But even when visibility is complete, the insight phase can be problematic. Often businesses derive insights that are not actionable. For instance, a retailer may have a complete set of data regarding customer foot traffic in a store, but if the real estate is not subject to change, there is not much that can be done. Also, in this phase, because it is presumed that some sort of data science will be brought to bear, the insights sometimes are simply not business-focused. Change that doesn’t have significant downstream business impact should not be undertaken.

Moving into automation

Finally, once insights are brought to the business, user stories are developed that form the basis of an automation strategy. However, these automation stories and implementations carry many risks. Bias in information can lead to runaway automation.

For instance, an enterprise may engage a chatbot to automate customer response based on collected data and insight regarding how customers wish to interact. If, for instance, that chatbot is designed with a built-in prejudice, this is a major problem. A prejudiced chatbot will apply that prejudice to every single customer it interacts with. We are seeing such runaway automations appear in the market, and often companies are paying a significant price for these mistakes.

This leads us to the third prediction: enterprises will redesign customer experiences amid stronger data privacy rules. This prediction goes beyond emerging regulations like the General Data Protection Regulation (GDPR) in Europe. The market has placed a value on privacy, and companies that design their automation user stories to overstep boundaries with respect to customer privacy will pay a steep price. We see these market penalties emerging just as strongly as government regulation in 2019.

Risks and rewards

Interestingly, the risks that are associated with these analytics projects are cumulative. If you have limited data visibility, the risk that your automation stories will reflect bias becomes greater. If your insights are not business-focused, the chances of you getting value from your automation stories drop significantly.

The good news is that through a proper implementation strategy, massive business value can be achieved. The bad news is that there are no shortcuts.

There are two closely related theorems that underpin the business case for monetizing the data collected by an organization:

Theorem 1: Data-centric automation projects have the potential for massive returns on investment.

Theorem 2: Data-centric automation projects are inherently risky.

In 2019, the market will be driven by these two theorems as more enterprises begin the journey to automation and insight through the collection of massive data sets. This will lead to the emergence of enterprises that achieve information enlightenment, leverage next-generation IoT platforms and still manage to respect the guardrails of regulators and the market. Enterprises in Asia will overcome the risks as long as they take the time to implement properly.


Daniel Angelucci is the chief technology officer for DXC Technology’s Asia region. He is responsible for promoting DXC’s technology story in the region as well as helping to shape the future technology direction of the organization. Daniel held a similar role at CSC. Prior to CSC, he served as a senior director in the engineering team at Visa Worldwide, was a senior consultant at Verizon and spent many years in the global architecture team at DHL. @dangeluc

 

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