Enterprise Data is Coming of Age, Thanks to Machine Learning

As more organizations embark on their digital transformations, the fundamental role of data across all departments and roles has come under the spotlight. In a recent report published by Blue Hill Research, Principal Analyst Toph Whitmore reveals that digital transformation as it concerns data is not, as some may see it, a “once and done” event, but one that involves three distinct phases:

The Three Phases of Digital Transformation

Whitmore explains that, in the first phase of digital transformation, Commodity Storage, enterprises gather data, store it, then lock the door and hand over the keys to their IT departments. In Phase 1, the average business user is totally walled off from enterprise data:

“For example, the business analyst submits a data request, the SQL analyst parses it and hands it off to IT, IT eventually processes the request, then passes data back to the business analyst, who in the ensuing three weeks has moved onto something else.”

At this stage the process is slow, cumbersome, and only effective in cases where weeks- or even months-old data is still valuable to the end user.

As users start to demand better, faster access to the timely information they need, the enterprise moves into the second phase, Self-Service Everything. No longer siloed in the IT department, data becomes an accessible asset, placing valuable information (ideally) at users’ fingertips when and where they need it. In this phase, as Whitmore puts it, “[d]ata access becomes (seemingly) immediate, offering faster time to insight, and the promise of faster/better decision-making.”

Phase 2, where most enterprises find themselves today, presents its own set of limitations. Many self-service platforms are cumbersome for non-technical personnel to use, and few provide the necessary context business users need to do their jobs more effectively. Furthermore, as Whitmore points out, “self-service delivery can outpace IT’s ability to govern data. As data volume grows (think IoT), business users’ ability to consume it in a timely fashion diminishes.”

When this friction reaches critical levels, forward-thinking enterprises will be forced to move on to a third phase of digital transformation: Machine-learning Ubiquity. To cope with the rising flood of data and remain competitive, enterprises will leverage artificial intelligence (AI), machine learning, and automation to deliver data-driven insights in context and within users’ existing workflows.

Progressing to this phase allows enterprises to accomplish three key goals:

• Speed time to insigh, by embedding data technology at the point of work
• Reduce risks associated with manual process delivery by applying machine learning to suggest actions or automate tasks
• Avoid process “lock-in,” by employing AI and benchmarking to let systems “learn as they go”

Machine Learning in Action: ServiceChannel

ServiceChannel provides facility managers with a single platform to source, procure, manage and pay for facility maintenance and repair services. Having passed through the first two phases of digital transformation, the organization is now working with GoodData to offer its customers a machine-learning-based decision engine.

“Going forward,” explains ServiceChannel VP, Marketplace Strategy & Experience Sid Shetty, “when our customers view proposals from their service providers, ServiceChannel's Decision Engine will recommend an action, as well as provide supporting intelligence so that customers can make the best data-driven decisions possible."

The (R)Evolution Continues

As the volume of enterprise data continues to grow exponentially and competitors turn up the heat, organizations can no longer afford to be stuck in the second phase of digital transformation. Business users across the enterprise require data-informed insights in context, where they work, in a way that leverages automation to let them focus on what they do best. Smart organizations are heeding the call to evolve, and others must follow suit or be left behind.