Platform Analytics Are Dead — Long Live the System of Intelligence

Platform Analytics Are Dead — Long Live the System of Intelligence
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The end of the year is nigh once again. What a blur the prior 11 months always seem when December rolls around. Very soon 2018 will be here. Twenty Eighteen — it certainly seems hard to believe.

Before we know it, predictions about 2020 will be discovered as true or false. Already, predictions about 2030 are popping up like so many optimistic DC commuters’ smiles upon learning about the progress of Elon Musk’s Hyperloop in suburban Maryland. While the future holds much promise, I for one am not quite ready to greet 2020 — let alone 2030 — just yet. Instead, I can only hope to look toward and prepare for the future.

The Future of Intelligence

Back in April, a very forward-thinking Greylock perspective was published by Jerry Chen entitled The New Moats: Why Systems of Intelligence™ Are the Next Defensible Business Model. This was the first time I had heard the phrase ‘system of intelligence.’ Jerry defines a system of intelligence as one that “typically crosses multiple data sets, multiple systems of record.” He categorizes the system of intelligence as useful for three major areas: customer-facing applications, employee-facing applications, or infrastructure systems. The last aspect of a system of intelligence that Jerry emphasizes, and is quite important to realize, is that “ultimately the product becomes tailored for each customer.”

At Numerify, I have the good fortune to speak with strategic IT leaders on a regular basis. I find that almost without exception they are searching for their own system of intelligence. They are faced with difficult-to-answer but practical business questions such as:

  • What is the health of our applications?
  • How do we stratify our infrastructure modernization project?
  • Are we able to complete all of our pending projects with defined time and budget constraints?
  • How are we trending toward meeting annual goals around compliance, rate of change, and increase in customer satisfaction?

Questions like these require such a system of intelligence as the one Jerry described. For example, to completely answer questions around capacity and resources, one may need to combine data from systems of record such as Human Capital Management (HCM), Project Portfolio Management, Financials, and Development platforms. Acquiring key operational insights may require analyzing data from such siloed sources as Application Performance Monitoring, IT Service Management (ITSM), Configuration Management Databases (CMDB) and Automated Call Distributors.

Siloed Data: The Abyss Preventing Intelligence

Where does one begin when faced with such a data challenge? At a minimum one would need a database capable of storing the necessary metadata from the systems of record, a data pipeline capable of feeding the data integration, a data scientist able to build and manage the models, a visual analytics application, and an analyst able to leverage the entire stack.

To many, even this very simplified view of the resources required to answer such business questions seems daunting. However, many organizations already have all the pieces in place to build solutions for these questions. So why is Jerry so bullish on the idea of new businesses coming along as analytical systems of intelligence?

Quite simply, it is because building and maintaining the data integrations and models is very difficult and expensive. Forbes recently produced data which showed that these motions account for 80 percent of the time and expense of analytical projects. (Also discovered was that nearly 80 percent of those tasked with these activities describe them as the least desirable part of their job.)

Image credit: Forbes

However, a small company with laser focus on a single industry or market can spend the time building the integrations and data models. One delivering as a service can offer continued domain expertise in addition to monitoring and managing of the integrations and models.

A System of Intelligence Now

In a recent webinar, one of my colleagues discussed several business problems occurring within a Fortune Global 500 organization. Leveraging Numerify’s platform as a system of intelligence, this customer was able to drive insight around the employee experience, among other interesting use cases.

As a large organization, extensive merger and acquisition activity had resulted in a complex environment. Overlapping services and confusing operational level agreements had left employees frustrated with IT. Although leaders had used surveys to capture where and how to improve the employee experience, this approach did not produce the desired outcome. A top-down initiative to reduce churn and retain top talent led the organization to a system of intelligence to solve their business pain.

IT leaders needed data from many different applications to truly measure and improve the employee experience for end users and service desk agents. Data about employee sentiment was already in social channel applications and ITSM systems of record. Views of the employee and associated assets were available in HCM, CMDB, and Asset Management reporting systems. Aggregation of data across these sources delivered the intelligence leadership was looking for through an Employee Experience Scorecard.

Service desk agents can now quickly and clearly understand how IT has been performing on behalf of the employee with all recent and open work items available, the sentiment of the employee towards IT through an algorithmic score, and the assets available to the employee to quickly identify the source of the trouble. After establishing this baseline, leaders began looking for macro-level trends around support groups, services/applications by age, tenure, region.

In the end, this organization realized Jerry Chen’s vision of a system of intelligence. As a result, they established standards, improved data quality, became more proactive in issue identification, gained transparency around the service experience, exposed visibility into outsourced service areas, and ultimately improved the employee experience.

While the seeds of these large business outcomes have been planted in 2017, it is clear that in 2018 and beyond forward-thinking organizations will be turning to systems of intelligence to prepare for the future.

[Photo credit: Pexels]

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