Ours is an era of unstructured information. The data we seek to manage, analyze, and interpret value from is available to us on a scale previously unfathomable–creating both opportunities and challenges for CIOs in every industry. If we can manage that influx of data and coordinate the disparate systems that generate it, we can achieve a real-time view of operations and extract meaningful insights that can improve the bottom line. If not, we’ll find ourselves drowning in data and lagging behind in a relentlessly progressive, high-tech environment, where winners are often the new entrants, not the incumbents.
Challenge #1: Monitoring Assets in Real Time
When I worked for Teekay Shipping, an oil and gas transport company generating $3 billion in revenue annually from a fleet of over 150 oil and liquefied natural gas tankers operating around the world, keeping close tabs on those vessels from shore was paramount. A minor mechanical failure or lost data transmission could cost tens of thousands a day in downtime, and safety breaches could be fatal. In those days, we had to rely on manual maintenance reports communicated from the vessels via slow and costly satellite transmissions, then looked to on-shore personnel to make sense of the data.
We were continually in search of a system or tool that would automate and improve the efficiency of this process–that could provide a more comprehensive, on-shore view of our fleet in real time.
If we knew more about what was happening on our vessels, and had the ability to compare each vessel against the others, it would have been enormously powerful.
We could have recognized patterns and ultimately predicted maintenance and safety issues before they arose, and that would have resulted in cost-savings and risk mitigation for the business.
The latest tanker technology–like many industries that involve monitoring field devices remotely–has the capacity to relay detailed information from sensors or devices on the vessel directly back to shore instantaneously. But capturing all that data is just the first half of the problem. Next, you need to sift through hundreds of thousands of data points to identify meaningful and actionable insights–and it is this business challenge that is common across so many industries. Regardless of where you’re collecting data from, the key is to look for a software solution that can serve as a virtual operator, meaning it can sift through the “noise” and alert only to “actionable” issues. It should also be capable of “predictive operations,” recognizing patterns so that you can mitigate issues proactively.
Challenge #2: Streamlining Data from Multiple Sources
Moving into the finance industry as the CIO for First West, a credit union in British Columbia, Canada, with over $6 billion in assets, I landed in an environment rife with mergers and partnerships. Credit unions everywhere were joining forces, which meant they needed to be able to access members’ account information via each other’s systems. The problem? Each credit union operated its own system. Converting to a new banking system was a massively disruptive endeavor that could take two to three years and drive away thousands of frustrated members–and was thus to be avoided at all costs. So, to pull info from another credit union’s system, we used complex and often cumbersome integration tools instead. However, this focused IT resources on playing catch-up behind the scenes following a merger or partnership, rather than on innovation and progress in a rapidly advancing industry threatened by market-disrupting products like PayPal and Google Wallet.
Our goal was specific: to be able to quickly and seamlessly pull clean data from multiple systems to gain a 360-degree view of our customers and thereby target our products and services to them.
But that goal was hindered by an underlying challenge I’d faced in every other industry I’d worked in: the integral need to coordinate disparate operations systems. To do so (in any context) requires a multilingual software program that can read all coding languages, and is thus able to query and retrieve info from any operating system directly without having to reconfigure the operating systems themselves. This is not only faster and more cost-effective to set up, but it helps to identify the existence and source of bad data, so you’re able to make better decisions based on reliable and more easily accessible information.
Challenge #3: Analyzing Data, Simply
It wasn’t until I reached the utility sector, where I was tasked with choosing an integration tool for a massive smart metering project at BC Hydro, British Columbia’s primary utility company, that I saw the solutions I’d been looking for in motion. I was faced with the same challenges as before: needing a real-time, contextual view of my “fleet” (two million smart meters in the field) and integrating disparate systems (work order routing, asset management, etc.). And in fact, utilities have pioneered machine intelligence technology for the Industrial Internet with the potential to benefit from capturing, analyzing, and interpreting data from any kind of sensor-equipped field device. But this time I had the added challenge of needing something engineered for business users rather than IT staff and other technical experts. With the smart meter rollout being new to the whole company, we needed a tool that was easy to use and quick to learn so that non-technical business users could leverage its full potential with a minimal learning curve.
No matter what type of data or devices you’re looking to manage, a simple interface is key.
It should take no more than a single day’s training to learn to use it to its full capacity, and everyone from the most junior operations staffer to the company CEO should be able to use plain English query language (much like what you’d type into Google) to retrieve valuable and actionable data.
The Balancing Act
As CIOs, we are destined to perform a constant balancing act: championing progress and pioneering innovative solutions in a risk adverse and change-resistant environment. We must stay ahead of the curve, yet, above all, keep the lights on and the technology stable. Rest assured that this is possible – if you know what to look for in a software solution. Whether or not it has already been applied to your industry, machine intelligence technology for the Industrial Internet is in use on a daily basis in enterprises around the world, and can be adapted to almost any application. Above all, look for a technology platform engineered to solve a business problem, rather than a technical problem – one that allows you to manage and analyze data from disparate systems at scale and extract meaningful insights, all in real time.