How Systems of Engagement Will Change the Role of Big Data From a Validation Tool to a Discovery Tool

Many companies today have already jumped on the “Big Data Bandwagon” and have allocated substantial budgets and multiple teams to figure out how they can better utilize the data they have to improve their customer relationships and increase their competitive advantage in the markets they serve. At the heart of these efforts, is the utilization of data analytics to lower the costs, improve the efficiency and gain increased value from the company’s existing systems of record.

Systems of Record: The center point for an SOR is a data base that supports a core asset of the company such as a Supply Chain, HR, Financial or CRM system. It must be accurate, up to date and secure. As such, from a corporate governance point of view, both ingress and egress must be tightly controlled by the company.

Most companies have assigned their IT group to lead this effort and have funded new investments in IT tools and hired an army of “data scientists” to search for and document critical and relevant customer insights. The problem is that all major IT projects have historically taken a very conventional approach to data analysis that utilizes large installed systems eg: CRM to mine these mountains of data and more often than not validate pre-existing assumptions or beliefs about customer needs and behaviors. This inside out use of data analytics re-enforces a culture of validation rather than a culture of discovery.

By contrast, if a company starts with an outside in approach to data analytics then it opens up a vast new potential to use Big Data as a discovery tool that facilitates the creation and implementation of new systems of engagement. Rather than starting with the data they have, it enables employees to re-think how they use data analytics to identify and address the emerging needs and issues their customers are trying to resolve at their key moments of engagement with the company.

Systems of Engagement: The center point for an SOE is a moment of engagement with an end user or a customer where the goal is to enable and enhance a value adding experience. Such systems must be friction free, immediately accessible and emotionally as well as intellectually dimensional. Ingress and egress are basically unrestricted and controlled by the end user.

Part of what is driving the Big Data Bandwagon is that organizations have come to the realization that learning faster than their competition is the only sustainable competitive advantage they have left. As such, this now puts a premium on how they use data analytics to get that learning advantage. To date, most of the evidence suggests that companies are utilizing their current systems of record to more cost efficiently mine their existing storehouses of data to gain validation of the changing needs of their customers and the marketplace.

IBM’s Institute for Business Value Study – IBM recently conducted a study that looked at the potential for collaborative relationships between CIOs and CMOs and presented the following key finding from its research:

“While Marketing has always been responsible for knowing the customer, now they are required to understand and respond to customers as individuals. Marketing can only do this if they can manage vast amounts of unstructured data, make sense of it with analytics, and generate insights that are predictive, not just historical – all on a massive scale.

To connect with individual customers at every touch point effectively, they need a system of engagement that maximizes value with each interaction. And they need each touch point to marry the culture of the organization with the brand to create authentic experiences that consistently deliver the brand promise. The way to achieve this unprecedented transformation is through technology.”

This key finding from their study not only validates the core tenets of the evolution of enterprise IT from systems of record to systems of engagement framework but it also highlights the need for Big Data to move from a validation tool to a discovery tool.

There is some early evidence of forward thinking companies that are starting to use data analytics to create new hypotheses and discover new ways of engaging their customers.

Ford Focus Electric Car

Ford Focus Electric Car

The Ford Focus Electric Car – Their new electric car produces large quantities of data while it’s being driven and also when it’s parked. When it’s being driven, it provides the driver with information on the car’s acceleration, braking, battery charge and current location. While this is useful to the driver, the data is also sent back to Ford engineers who can learn about the owner’s driving and re-charging habits. This real time information enables Ford engineers to better understand what their electric car customers need and want so they can develop and deliver product improvements based on analyzing this real time data. Additionally, third-party vendors can use this cumulative driver data to figure out where to put additional charging stations.

Pharmaceutical companies – Many pharmaceutical companies are in the early stages of using patient information monitoring techniques to lower the cost and improve the quality of their drug trials. In the past, doctors monitored trial participants by seeing them periodically in their offices. Today, a patient can have a sensor placed on their body which captures round-the-clock real time data about their adherence to the treatment regime and the positive or negative effects of the drug. With the advent of “outcome based medicine” where insurance companies only want to pay for drugs that deliver the results they are supposed to, it is critical that drug companies have a more timely way to assess the actual impact of the drug versus its desired impact.

While the overall debate on the value and contribution Big Data can make to organizations continues to escalate, I think there is a different and more powerful discussion to be had. How can companies use data analytics as a discovery tool that will allow them to learn faster than their competition? For IT leaders to put this question on the table, they have to change their mindset away from using data analytics to improve the efficiency of their systems of record and transform it into helping them create and deliver new systems of engagement.

Leveraging Offer Power: Who Says You Can’t Differentiate and Neutralize at the Same Time?

In my brother Geoffrey Moore’s new book, Escape Velocity – Free Your Company’s Future From The Pull Of The Past, he lays out a very compelling framework on how companies can significantly increase the ROI on their innovation investments. This framework presents three different ways a company can generate meaningful returns as illustrated in the diagram below.


The first way is to create an “unmatchable offer” that  your key reference competitors can’t or won’t replicate and that your customers will pay you a premium for because there is nothing comparable on the market. The secret here is to make sure you go far enough so that the market clearly sees the differentiation. Many companies stop to soon believing that “best in class” is the goal. What they ultimately find out is that best in class is a “sucker’s bet” because the market won’t pay up for it.

The second way is to neutralize offers from your reference competitors that have features your product doesn’t have. The secret here is to get a comparable offer from your company to market as quickly as possible. The two mistakes companies make here is first they move too slowly and second they spend too much money trying to outdo the competitive offer. In this case, the goal is to get to good enough fast enough so you don’t lose any competitive ground.

The third way is to task a project team to go identify and reclaim resources that have been dedicated to efforts that are not yielding good returns. The challenge here is to make sure that you don’t let any “sacred cows” off the hook in this cost optimization exercise. In this case, best in class is a good target to shoot for.

The other major mistake companies often make is to tie differentiation and neutralization initiatives together in one work stream which guarantees that they won’t maximize the potential value and returns from either one. Each one should have its own work stream as illustrated by the diagram below.


Having said all this, I want to raise another opportunity and that is the potential to conduct both differentiation and neutralization initiatives at the same time while keeping them in separate dedicated work streams. Apple’s recent actions once again provide a good template for how to do this well.

It has been well documented that Apple successfully launched 3 next generation products in the last decade. It should be noted that the iPhone was not released to the market until the iPod had successfully generated material new revenue and profits for the company and similarly the iPad was not released until the iPhone was producing material new revenue and profits.

What has been overlooked by many observers of this decade long unprecedented set of accomplishments is that while the new products were coming to market Apple was also developing lower cost models of each to neutralize competitive offers at much lower price points. The iPod Shuffle and iPod Nano along with the iPad Mini are examples along with the recently rumored launch of a lower cost iPhone.

While there is always a danger of these new lower cost offers eroding the industry high margin rates Apple enjoys, the greater risk is to do nothing and see their market position evaporate like what has happened to Palm, Nokia and RIM.

As I said earlier, it is important to remember that you need good organizational and operational processes in order to drive parallel differentiation and neutralization work streams at the same time. Each one will have its own separate cadence, timetable and set of deliverables. Each will also require different leadership skills to get the desired outcomes. As such, HR should play a strong role in helping to identify the strongest candidates to lead each one.

In the end, in order to maximize the ROI on your innovation investments, I think the message is clear. First, you need to separate your innovation initiatives into three separate work streams with three clearly defined deliverables. Second, even after you’ve successfully launched a truly differentiated offer, you need to immediately  begin thinking about how you can protect that offer from lower cost alternatives.