The key to digital transformation is data. The main roadblock to digital transformation? That very same data.
It’s a paradox that has become increasingly acute with the deluge of data coming from IoT devices and sensors. To keep pace, companies are investing more and more in IoT — in 2020, spending on IoT is projected to be over $145 billion across manufacturing, supply chains, utilities and heavy process industries.
These companies know that their data is valuable. It can be used for condition-based maintenance, optimized asset performance, more sustainable industry and innovative new services and products. But too many companies don’t know how to make the leap from raw data to data-driven intelligence. A McKinsey & Company report found that more than 99% of data from 30,000 oil rig sensors, which tracked drilling, production and rig maintenance, somehow wasn’t reaching operational and industry decision-makers — so only 1% of the data was turning into value. Forrester surveyed 150 manufacturing, transportation and consumer package good companies and found that only “17% of organizations stated that they are able to collect, aggregate, contextualize, analyze and visualize the insights from their IoT data.”
So, how should a modern company mind the IoT gap?
It is a problem similar to the modern email inbox. A variety of inputs with varying subject lines, abbreviations and attachments comes in, but you don’t always know when you will need to refer back to a particular email or locate a file that someone sent. In 2012, an IDC white paper estimated people spend as much as 4.5 hours per week looking for lost documents. Getting everything in one place is half the battle. Getting things organized into a consistent and findable format is the other.
Much like this scenario, reducing wasted time looking for information starts with system design with a common, organized reservoir for rapid query and retrieval, and interoperability between layers of an enterprise data technology stack.
The same McKinsey report found that, “interoperability between IoT systems is critically important to capturing maximum value; on average, interoperability is required for 40% of potential value across IoT applications and by nearly 60% in some settings.”
Unfortunately, it isn’t always in the interest of IoT vendors to ensure interoperability. Many want to have a proprietary claim on the data in their systems. This is game theory at work as vendors sell vertically integrated technologies that deliver an initial business value, but create silos that limit the future value of data.
Minding the IoT gap must start with breaking down these silos and democratizing access to data. It’s hard work, but it pays.
Manufacturer Cemex needed visibility into 70 plants across 21 countries. Each plant had a different mix of equipment and its own processes for handling 10,000 to 15,000 data points. Gathering data from all of these plants for a global report was two months of work. Cemex established a global technology group which developed shared standards for how information was collected and contextualized. Today, data is readily available in a usable format to anyone in the company. And the report that used to take two months can be compiled in less than an hour.
UK power provider Centrica was relying on spreadsheets and a labyrinth of code to run over 200,000 calculations. The approach relied on a single employee on the verge of retirement and was prone to errors that were not easy to identify, which caused the reporting process to be time-consuming. Centrica focused on automating operational and business data preparation and eliminated the spreadsheets and custom coding. Now, data can be viewed in automated dashboards.
The amount of data will only increase, and the value of data as an asset will not be value that enterprises can leave on the table if they want to keep operating.
In my blog next month, I’ll talk about how the Linux Foundation is looking to tackle interoperability by establishing shared standards and providing platforms that unhitch application development from infrastructure and data management. More next month.
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