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Cameron Archer
April 02, 2020

The 10 Data Problems that Plague Industry

The Internet of Things (IoT) is transforming the world in many ways. Smart devices, machine-to-machine interactions, and edge computing are enabling us to digitize physical assets and unlock new efficiencies across numerous markets. 


However, progress has been slower than anticipated. Businesses are struggling to scale IoT deployments, especially in the industrial space. According to McKinsey, nearly nine in ten industrial companies fail to get past the pilot phase in the first year of IoT implementation.


What we’ve learned is that industry experience does not directly translate to IoT success. Companies need support to optimize their network infrastructure and achieve real digital transformation. Legacy OT devices, protocols, and architecture simply aren’t built for modern demands.


One of the biggest hurdles businesses face today is how to extract value from data. To maximize ROI and generate meaningful cost savings, companies have to collect massive quantities of high-quality data, which isn’t easy.


We’ve identified ten data-specific challenges that prevent industrial companies from getting what they need out of their data acquisition systems:

    1. Missing data
    2. Bad data
    3. Incomplete data
    4. Latent data
    5. Siloed data
    6. Low-Resolution data
    7. Misidentified data
    8. Unprocessed data
    9. Unstructured data
    10. Insecure data

Below, we provide a brief overview of each of these issues, and we invite you to click the links above, or in the section headers below, to learn more about each of these significant, costly data problems.


Missing Data

One of the primary roadblocks that IoT platform architects and operators face is simple - critical data is missing. In many cases, the cost to plug data gaps is too high to justify. Companies have to invest significant capital upfront to collect when they need it, which is a tricky proposition given the specialized knowledge that may be required for success.

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Bad Data

Bad data leads to bad conclusions. When it comes to data acquisition systems, sensors and devices can malfunction, causing them to gather data that does not represent the real world accurately. The problem of bad data is particularly scary because it can be hard to identify. Information may check all of the right boxes - we have it, it’s labeled, it’s on time, it’s complete - but it’s wrong.

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Incomplete Data

Despite there being more data in the world than ever before, many workers still struggle to get all of the information they need. Data may be incomplete for many reasons. Network downtime, caching issues, device problems, and more, can cause information leakage.

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Latent Data

Oftentimes, organizations struggle to gather data in time to use it effectively. Even if data comes from high-quality sources and accurately represents reality, it may arrive too late for analysis. Those who need real-time intel to make decisions suffer at the hands of the latent data problem.

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Siloed Data

Even when clean data exists, it may not be accessible. In industrial environments, this typically means that office users who need to analyze historical performance don’t have access to process data used by field and operations personnel. As a result, they lack visibility into what has happened across their asset base and can’t act accordingly.  

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Low-Resolution Data

Low-resolution data is characterized by readings or measurements that happen too infrequently for a specific application. Consequently, anomalies or important events go undetected because they occur between samples. Operators can’t develop a thorough and comprehensive understanding of their assets unless they can drill down into high-resolution data.

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Misidentified Data

Misidentified data is produced by improperly labeled sources, which can be frustrating if the information itself is valuable. The quality may be there, but there isn’t enough context around what the data means. Data can also be unidentified, thus leading analysts grasping for appropriate conclusions.

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Unprocessed Data

Data may require processing before it’s useful. It’s not uncommon for smart devices to collect data that isn’t immediately ready for analysis or synthesis. Operators have to spend additional time preparing unprocessed data so that it fits into a broader IoT story and creates enterprise value. 

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Unstructured Data

Data has to be organized in a logical way for it to be useful. Otherwise, users can’t extract what they need to make informed decisions. Unstructured data makes it hard for operators to look up specific data points or understand the context around particular information. To get the most out of real-world data, it must be labeled appropriately and readily available for consumption, with logical connections to other relevant data.

Read more >


Insecure Data

Finally, we have to talk about security. Like hardware in explosive environments, IoT must be “intrinsically safe,” meaning it’s logically secured and readily available to those who need access. Security policies need to be simultaneously flexible enough to evolve and strict enough to prevent external tampering. Insecure data can deter industrial companies from gathering what they need because it's either unsafe or not worth the risk.

Read more >


Explore the 10 Data Problems

As an industrial business with digital aspirations, you need to be aware of these 10 data problems and how they can impact your future projects. 


Click the links above to take a deep dive into each of the 10 data problems, or download our eBook that contains all 10 in one place, so you can better develop a fundamental understanding of the various data challenges industrial companies face today. 




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