The Internet of Things (IoT) is ushering in a new wave of low cost sensor devices and their associated data streams. Gartner has identified industrial analytics as the next wave of business transformation for those organizations that can effectively harness this information for more meaningful and timely decisions. But traditional BI and analytic approaches will not be sufficient in an era of high speed, high volume data streams.

Industrial analytics requires a combination of streaming data analytics and visual data discovery that allows operators to be alerted when an important event occurs and to enrich this with the business context to make better, faster decisions and take action. As most sensor information and machine log files include time series data that is continuous and very granular, it needs to be cached and analyzed in a very different manner than traditional BI which expects time in days, weeks and months. And the format of the data is often multi-structured and requires complex preparation in real-time before it can be visualized. You also need to enhance your understanding by applying predictive analytics on the fly.

Datawatch is pleased to provide this important Gartner research on industrial analytics. Datawatch customers have been at the forefront of this next wave by applying our next generation visual data discovery solutions to a variety of industrial use cases. Datawatch has been recognized by Gartner as a “Cool Vendor” for its unique in-memory architecture and ability to visualize streaming data and join this with other sources including OT repositories, time series databases, conventional relational and columnar databases, and multi-structured data.

We hope you enjoy this research report and encourage you to reach out to us to learn how you can begin to transform your organization.

Dan Potter Vice President, Product Marketing Datawatch Corporation

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Industrial Analytics Powered by the Internet of ThingsThe next wave of business transformation

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ways to expand use of low-cost sensors and new data sources that can be mapped to business opportunities.

• Identify a scenario and develop a pilot project that uses diverse sources of OT data, but that interacts with the enterprise system to deliver high-value outcomes.

• Partner with line of business (LOB) counterparts/leadership to understand desired future-state business outcomes, and develop analytics use cases to deliver the necessary intelligence to support this transformation.

Introduction

This document was revised on 27 August 2014. The document you are viewing is the corrected version. For more information, see the Corrections page on gartner.com.

The basis of competition for asset-intensive companies is shifting from physical assets to information assets derived from the equipment. More specifically, the search for competitive advantage is starting to focus on companies’ ability to capture information from a wide spectrum of sources, and then visualize, analyze, propagate and contextualize it in a way that will drive the next wave of business transformation.

In asset-intensive industries (such as energy, manufacturing, oil and gas, mining, transportation, healthcare and utilities), sensors embedded in equipment and infrastructure are generating and collecting terabytes of raw data, most of which is

CIOs of asset-intensive companies are finding themselves at the leading edge of industrial analytics, the next wave of business transformation. Use this research to surmount existing challenges of IT/OT data integration and analysis by examining opportunity, business needs and skills availability.

Key Challenges

• As the cost of sensors and communications comes down, and as storage capability and processing speed increase (for example, Hadoop), enormous troves of operational technology (OT) data are being collected and stored, but remain unused or underused.

• OT is markedly different from IT — in application, architectural characteristics and need for different governance. Data scientists versed in OT are consequently rare.

• Combining IT and OT holds transformational possibilities in analytics for asset-intensive industries, but will require new approaches to data integration and management, and an evolution of infrastructure and skills for data integration, management, and analytics.

Recommendations

CIOs of asset-intensive companies:

• Audit your data. Find underutilized OT data (or “dark data”), and look for new

never used in an analytical role. However, this underutilized data, or “dark data,” represents huge potential, in terms of both asset performance management (APM) and product/service quality management — if only it can be harnessed (see Note 1). The process of capturing, organizing, combining and applying specific and applicable OT data with IT — what Gartner calls “industrial analytics” — represents the next wave of business transformation (see Note 2). The successful CIOs in the asset-intensive industries will recognize and deliver on this value to their business.

Analysis

Look for “Dark” OT Data, Finding New Ways to Expand Use of Low-Cost Sensors and New Data Sources That Can Be Mapped to Business OpportunitiesToday’s asset-intensive companies are replete with equipment and systems with embedded sensors generating data. Thousands of sensors and controllers continually measure, report and record temperatures, pressures, flows and vibrations — sometimes at subsecond intervals — and time stamp the data (often referred to as time series data). Huge volumes are collected and stored, but most of it is never touched again, since it falls outside the traditional confines of IT systems, and is essentially “dark data” to the IT department.

Industrial Analytics Powered by the Internet of Things is published by Datawatch. Editorial content supplied by Datawatch is independent of Gartner analysis. All Gartner research is used with Gartner’s permission, and was originally published as part of Gartner’s syndicated research service available to all entitled Gartner clients. © 2014 Gartner, Inc. and/or its affiliates. All rights reserved. The use of Gartner research in this publication does not indicate Gartner’s endorsement of Datawatch’s products and/or strategies. Reproduction or distribution of this publication in any form without Gartner’s prior written permission is forbidden. The information contained herein has been obtained from sources believed to be reliable. Gartner disclaims all warranties as to the accuracy, completeness or adequacy of such information. The opinions expressed herein are subject to change without notice. Although Gartner research may include a discussion of related legal issues, Gartner does not provide legal advice or services and its research should not be construed or used as such. Gartner is a public company, and its shareholders may include firms and funds that have financial interests in entities covered in Gartner research. Gartner’s Board of Directors may include senior managers of these firms or funds. Gartner research is produced independently by its research organization without input or influence from these firms, funds or their managers. For further information on the independence and integrity of Gartner research, see “Guiding Principles on Independence and Objectivity” on its website, http://www.gartner.com/technology/about/ombudsman/omb_guide2.jsp.

Research from Gartner:

Industrial Analytics Revolutionizes Big Data in the Digital Business

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Much of what IT calls dark data comes from systems known by engineers and operations staff as operational technology, or OT. OT typically involves systems and devices that monitor and control plant functions and equipment. OT takes on a number of forms. Some OT monitors technical parameters and ensures safe operation of complex machines, including those in power plants, on offshore drilling rigs, and on aircrafts, reporting or even shutting down equipment that is in danger of breakdown or failure. Other OT includes event-driven software applications or devices with embedded software, or systems that manage and control mission-critical production and delivery processes. The sources are not even necessarily in your own business; if you are delivering equipment enabled with OT, then dark data may come from your customers

for analysis. The types of data will vary as is reflected in Figure 1.

Even though engineering and operations staff view OT data as the currency of their daily life, they still typically access only for their own daily functional needs. For example:

• A single turbine compressor blade can generate 500GB of data per day.

• A typical wind farm may generate 150,000 data points per second.

• A smart meter project can generate 500 million readings of data per day.

• Weather analysis can involve petabytes (quintillions of bytes) of data.

By their very nature, asset-intensive organizations are collecting and storing huge information assets every second of every day. The volume, variety and velocity of OT are so huge that calling it simply “big data” seems somehow inadequate. The potential benefits of being able to harness and leverage these assets are transformational. Some companies are already beginning to make small strides. For example:

• A medical device manufacturer was able to leverage data from device monitors to nearly double availability of its equipment, while reducing cost of maintenance by 20% to 30%.

• An international petroleum company avoided millions of dollars in downtime and lost production by remotely monitoring and analyzing data collected from critical equipment at a location halfway around the world.

• A global producer of components and solutions is able to monitor the temperature of assets such as cold stores, store freezers, and refrigerators in 4,500 supermarkets worldwide, analyzing thermostats, evaporators, fans and compressors to greatly reduce unplanned failures, reducing expensive downtime and third-party call outs.

• A Canadian power company was able to integrate IT and OT data from $20 billion worth of assets in a smart grid, resulting in significant savings, in terms of reduced truck rolls, and a more cost-effective replacement strategy for aging assets.

• A railroad brake manufacturer was able to attach sensors to brakes installed in major railroad trains. Aggregating this data and producing insight and reports for the owners of the locomotives allowed them to better train the engineers — and better manage the acceleration and braking of the trains throughout Source: Gartner (August 2014)

FIGURE 1Industries Are Adopting Different Types of Big Data at Very Different Rates

Manu. & Nat. Res.

Media/ Comm. Svcs. Govt. Edu. Retail Banking Insur. Health. Trans.

Transactions 73% 62% 67% 67% 54% 93% 83% 81% 75% 79%

Log Data 44% 57% 58% 59% 54% 40% 66% 61% 33% 71%

Emails/Documents

27% 43% 43% 41% 46% 27% 34% 39% 17% 29%

Social Media Data

32% 52% 39% 26% 54% 73% 27% 13% - 50%

Machine Data 37% 24% 29% 19% 23% 20% 27% 29% 42% 29%

Free-Form Text 17% 24% 28% 30% 31% 20% 34% 35% 67% 21%

Geospatial Data 27% 14% 19% 19% 38% 27% 27% 26% 8% 29%

Sensor Data 36% 29% 12% 30% 23% 7% 7% 29% 25% 29%

Images 19% 24% 17% 11% 38% 13% 5% 16% 25% 7%

Video 8% 29% 12% 7% 31% 13% - 6% 8% 7%

Audio 10% 19% 8% 4% 8% - - 6% - -

Other 8% 14% 13% 15% 8% 7% 10% 16% 42% 14%

n = 59 21* 127 27* 13* 15* 41 31 12* 14*

Which types of big data does your organization currently analyze?

Base: Invested or plan to invest in technology for big data*Base < 30; results are directionalMultiple responses allowed

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thousand-mile trips. This insight helped train companies save as much as 1% in fuel costs.

Identify a Scenario, and Develop a Pilot Project That Uses OT but Interacts With the Enterprise System to Deliver High-Value OutcomesFor CIOs in asset-intensive environments, essential first steps in defining any business case for optimizing industrial assets are to identify the data needed, and to ensure that the right data is being collecting or can be collected.

IT and OT have evolved in different environments, and are based on different data types and information architectures. They are sufficiently different in purpose and nature as to pose formidable barriers to making them compatible, let alone combining and synchronizing data between them:

• IT has evolved essentially from the top down, focusing primarily on the business — that is, the corporate operations and systems required to manage the business, mainly from a financial perspective. IT also has a substantial consumerism component, being resident on millions of personal as well as business computers. IT systems are based on well-established standards that can accommodate the integration of large volumes of information across different applications.

• OT, on the other hand, grew from the bottom up, with many different proprietary systems designed to control specific processes and equipment. OT systems are (or were) usually based on vendor-specific, proprietary technologies operating in a real-time or near-real-time environment. OT is commonly deployed in harsh environments, and typically has a low tolerance for failure and significant requirements for recovery. OT has highly focused governance models required to

support what are usually highly insulated localized processes.

The transformational opportunities of integrating IT with OT are substantial, but initial objectives should be to balance effectiveness and efficiency. As with any analytics initiative, the key will be to define a business that ties investment to tangible, high-value outcomes. A number of factors come into play:

• The cost of sensors is coming down, making broader use cases more economically viable.

• Data security is a top-of-mind issue, since sensors and controllers are often embedded within critical infrastructure and assets. Such use cases must anticipate transient problems and glitches; additionally, in today’s connected environment, deliberate, malicious hacking is a very real threat.

• Devising and gaining agreement on network technology and device connectivity standards poses a major challenge. While machine to machine (M2M) connectivity has been around for years, no single industry standard exists.

Lately, the Internet of Things (IoT) has become a central rallying point, and is fueling efforts to develop more-inclusive standards. Some proposed standards are beginning to emerge. For example, the Telecommunications Industry Association (TIA), which represents a number of manufacturers and suppliers of high-tech communications networks, has issued an M2M protocol standards series (TR-50 TIA 4940.020 and TR-50 TIA 4940.000) with the declared intent of inspiring companies to implement connectivity to the things they manufacture. In manufacturing industries, the renewed interest in more generic standards like OPC Unified Architecture (UA) may assuage IT/OT integration and information access concerns. (OPC UA is

used for integration with OT data sources such as programmable logic controllers [PLCs], distribution control system [DCS]/supervisory control and data acquisition [SCADA], and other production equipment/machinery.)

Partner With LOB Counterparts/Leadership to Understand Desired Future-State Business Outcomes, and Develop Analytics Use Cases to Deliver the Necessary Intelligence to Support This TransformationTo get business buy-in for industrial analytics initiatives, CIOs will need to understand their internal customers and be able to identify what data (dark or otherwise) will be needed to support the desired outcomes. Figure 2 provides a high-level view of how and where the best opportunities lie for IT/OT integration and convergence.

Most likely, an enterprise is already collecting and storing some OT data. OT data may even be contributing to systems or applications that monitor — and possibly even detect — early warning signals that machines or systems are degrading or in danger of breakdown. While this is valuable information that can reduce risk of unplanned downtime and potentially save the enterprise money, if and when organizations can improve their analytics maturity, IT and OT information can be fully harnessed, and the potential is much greater. Additionally, we see a rise in external monitoring provided by equipment makers that, within their environments, leverage customer OT data to carry out similar objectives, but may extend it to R&D of their own products and enhancing their supply chain and customer service.

Use Case Maturity

Industrial analytics maturity is defined in terms of four states across the eight dimensions. Levels are:

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1 Monitor

2 Diagnose and control

3 Manage

4 Optimize

Each level builds on the previous.

Level 1

In Level 1, companies can monitor and report on asset behavior and even track interactions between them. They can track location and monitor use, including wear and tear. Monitoring not only improves insight into conditions for better maintenance but also can lead to new business models. For example:

• Jet engine manufacturers are creating new business models by retaining ownership of their products while

charging airlines for the amount of thrust used.

• Companies like Zipcar, by equipping cars with sensors, can eliminate the rental car center. They can monitor rental car usage and thereby rent for short periods of time at higher rates.

Level 2

At Level 2, organizations can not only monitor behavior but also diagnose issues, and, in some cases, even respond. For example:

• Monitoring patients and alerting the appropriate caregiver in time can optimize patient outcomes and minimize cost.

• Monitoring and controlling equipment enables the throttling back or shutting down of an expensive piece of equipment when temperatures or pressures exceed certain thresholds that could lead to downtime.

Source: Gartner (August 2014)

Level 3

The Level 3 state enables organizations to manage the performance of asset and processes. This includes examples such as:

• Danfoss — which supplies products and technologies for cooling food, heating and air conditioning buildings, controlling electric motors and powering mobile machinery — was able to create predictive and preventative maintenance strategies that help companies shift from unplanned maintenance to predictive maintenance, resulting in 70% fewer alarms.

• Sensors and data links used in healthcare can monitor a patient’s behavior and symptoms in real time and at relatively low cost, allowing physicians to better diagnose disease and to prescribe tailored treatment regimens.

• A manufacturer of equipment that manages skids that automate the custody transfer of hydrocarbons in the petroleum value chain for multiple industries (such as oil production, trucks and tankers) built a cloud-based remote monitoring system. Data collected from the flow meters that monitor temperature in the skids is sent to the cloud. The data is, in turn, provided to customers as a service to help drive process improvements, and to provide the basis for predictive and preventative maintenance programs, as well as financial processes (accurate billing) improvement. This avoided system degradation, which can lead to quality and cost issues where a 1% error each day can add more than $1 billion of lost revenue per year.

Level 4

Level 4 is about optimizing decisions, processes and systems. This impacts logistics for:

FIGURE 2IT/OT Opportunities

Big Data Means Integration Between IT and OT

IT OT

ERP

CRM

Demand Management

Financial Planning

Network Optimization

Control Systems

Smart Machines

Location Systems

Environmental Sensors

Bar Codes/RFID

Context

Visualization

Prediction

Monitoring

Condition

Location

State

Events

Costs

Orders

Specs.

Constraints

Identify an Anchor Point for IT/OT Collaboration

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• Companies (such as UPS or other delivery companies, airlines and shipping firms) that are able to use industrial analytics by monitoring weather conditions, traffic patterns and vehicle locations to optimize constant routes — while reducing wear and tear, gas consumption and while improving safety and on-time delivery given service levels.

• Facilities like hospitals that need to optimize use of staff, equipment, consumables, beds, rooms and so on while keeping wait times to a minimum.

• Field service and repair of assets across a system that is prioritized and optimized based on likelihood to fail versus impact calculations.

Data Maturity

Within the context of industrial analytics, the typical enterprise data maturity applies. It begins with the analysis of historical data, which, in most cases, will be nicely structured and often siloed data. From there, organizations try to integrate and leverage multistructured, real-time data, as well as external data.

Industrial analytics introduces sensors as new data sources. The first question for the CIO is to determine if you have access to monitor the assets you want to monitor. If not, what will it cost to install the necessary monitoring equipment? Bear in mind that with the replacement cycle of OT systems being much longer than for IT, planning now will enable aligned investments in the

future. Integration of OT data from sensors must be a coordinated and integrated part of an enterprise information management capabilities framework (see Figure 3).

Sensor Integration

Sensor integration will range from tightly coupled integration with proprietary standards at Level 1 up to Internet-connected sensors to cross-sensor integration, to sensors with distributed processing and self-learning and healing capabilities at Level 4.

Key to this process will be the introduction and wide adoption of common standards similar to those that enabled the proliferation of the Internet of “people.” Currently, a few standards are beginning to emerge, such as message queue telemetry transport (MQTT), data distribution service (DDS) and Common Industrial Protocol supporting different layers of the stack. The newly formed Industrial Internet Consortium is another standards body attempted to create common standards. Integrating the OT data from sensors must be a coordinated and integrated part of the information management capabilities framework plan and road map.

Analytics

From an analytics perspective, as shown in Figure 4, maturity will involve expanding from the purely descriptive (“What happened?”) to diagnostic (“Why did it happen?”) to predictive (What will happen?”) to prescriptive (“What should be done about it?”):

• Descriptive (Level 1) would leverage descriptive analytics: Which parts/equipment have failed? Which parts failed over time by equipment and manufacturer?

• Diagnostic (Level 2) would leverage diagnostic analytics: Which parts/suppliers/factories are the primary causes of failures? Are there correlations between part failures?

Source: Gartner (August 2014)

FIGURE 3Gartner Information Capabilities Framework

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• Predictive (Level 3) would help organizations tie predictions to decisions using rules, some content analytics to measure which parts or equipment are likely to fail — either based on actual conditions or based on patterns or leading indicators.

• Prescriptive (Level 4) makes it possible to leverage the continuum of capabilities, and to include external data sources such as energy data or weather patterns to create prescriptive capabilities. Organizations can do this to determine the optimal production schedule/product mix or preventive maintenance schedule across assets to optimize asset life and profit, and to minimize downtime while meeting customer demand (business outcome).

Context-Based Decisions

The value of leveraging OT data is less in the technology per se, but in how the data is used to automate and improve operational impacts on business outcomes. Critical to this context is the ability to define and maintain models of multiple, related information sources, and to maintain persistent, functional and operational models (relationships) between the data over an extended hierarchy of time scales and business priorities that extend significantly beyond the operations environment, portraying OT data alongside enterprise data.

A chemicals company combined historical data and unit metrics on asset performance, cost, yield and cycle times by shift, day and month. It uses these models against

Source: Gartner (August 2014)

FIGURE 4Analytics Capabilities

Human InputAnalytics

Data DecisionPredictiveWhat will happen?

DiagnosticWhy did it happen?

DescriptiveWhat happened?

Action

Decision Automation

Decision SupportPrescriptiveWhat should I do?

Asset Management Level: Predictive Forecasting, Financial Optimized

Sample Questions/Use Cases:• What is the optimal preventive

maintenance schedule acrossassets to optimize asset life andprofit, and to minimize downtime?

• How should equipment be automatically controlledto optimize?

Technologies/Approaches:• Event processing• Sensor integration

and other data• Optimization (e.g., linear

programming, mixed integer programming)

• Decision analysis, influence diagrams

• Game theory• Predictive (or

descriptive, or diagnostic) analytics, plus rules

• Control systems• Cognitive systems

near-real-time data from its OT layer (DCS/SCADA) with ERP-based data to do not only activity-based costing, but also simulations to predict the impacts that changes in energy and material (feedstock) costs will have on asset performance and total margin. In Year 1, this project returned over $1 million in savings and a 2% reduction in energy feedstocks and electricity costs. In many instances, these models will start small, but will expand over time to leverage internal and external data sources, and to provide a closed loop that links factory and product data with business performance to improve operations.

As the new networks link data from products, company assets or the operating environment, they will generate better information and analysis. This could significantly enhance decision making, be those decisions at a strategic level (such as where to drill an oil field) or a tactical level (such as a patient treatment plan). They range from batch, offline decision support to real time — both human augmented, in the case of strategic and tactical decisions, and increasing for automated and real time for operational decisions (like routing a truck, auto scheduling and assigning repairs or shutting off a valve before failure). In the pulp and paper industry, one company was able to raise production 5% by using data from embedded temperature sensors to adjust a kiln flame’s shape and intensity. Reducing temperature variance to near zero improved product quality and eliminated the need for frequent operator intervention to adjust temperature (see Table 1).

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Table 1. Industrial Analytics Maturity Find and Develop the Skills for IT/OT Integration

Level 1 Monitor

Level 2 Diagnose and Control

Level 3 Manage

Level 4 Optimize

Decisions Batch offline decision support

Pockets of fact-based decisions, rule-based, predictive

Operationalized predictive models in applications, pervasive fact-based decision making

Human-augmented, collaborative; real-time embedded, automated, decision optimized

IT/OT Convergence Identify how convergence (OT looks like IT) is happening in your business

Consensus that convergence means a change in managing OT

Alignment of how you manage OT with how you manage IT

Integration of IT and OT systems and infrastructure, resources and management

Infrastructure Deployments

On-premises Noncritical tactical cloud Hybrid but cloud tactical; SQL, NoSQL, New SQL piloting

Some mission-critical cloud; SQL, NoSQL, New SQL tactical deployment

Skills Report writing, SQL Report writing, SQL, OLAP, data modeling, visualization and analysis

R, SAS, SPSS, content analytics

Data science (machine learning, business data management, simulation, optimization, operationalizing) on multistructured data

OLAP = online analytical processing; SPSS = Statistical Package for the Social Sciences Source: Gartner (August 2014)

IT/OT integration will be critical at all stages of the maturation process — from awareness and assessment, to agreement on changes that need to happen in both areas, to alignment of strategy and resources, to integration of resources, infrastructure and management. Identifying the anchor point or use case for IT/OT collaboration is a good first step.

An important part of IT/OT integration will involve the evolution of infrastructure from solely on-premises to likely some hybrid of cloud and on-premises. A number of technologies will be required, including analytics, big data technologies, BPM, support for streaming data, security, sensors and so on.

Overarching all of these considerations, however, will be the need for a maturity of skills — from report-centric capabilities to advanced analytics, big data, and data science skill sets, including:

• Data management — How do I build operational models in processes?

• Analytics modeling — Which algorithms, modeling or machine learning techniques should be used? How are the results interpreted? What are the latest techniques to run them in parallel, or to crowdsource them?

• Domain expertise — What are specific algorithms and workflow that are relevant to the business processes in my industry?

What is possible in my industry and business? How can I have the maximum impact?

To avoid the skills gap, organizations can buy packaged applications (such as those offered by C3global, Clockwork, Mtel and others) targeted at specific use cases. Increasingly, these applications will package industry, aggregate data or act as industry analytics service providers. GE and SKF are examples of equipment vendors providing this type of offering.

Another option is to develop the skills internally. In this case, companies can harvest them from OT or the line of business. Finally, organizations can use crowdsourcing services that bring together data scientists

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usually for prize money, such as Kaggle, topcoder or Meridium asset answers.

Having the skills internally is not the only answer. The best course of action can be to buy or outsource.

Recommendations:

• Consider the “build” solution path if you already have analytics skills in-house, if the required analytics are a critical differentiator, or you require a high level of agility and granularity of control.

• Consider the “buy” solution path when packaged applications are available, and the time to solution is more important than any potential differentiation.

• Consider the “outsource” solution path if you lack the in-house skills to build your own solution, and packaged applications don’t meet your needs or require significant customization to do so.

Note 1. Dark Data

Gartner defines dark data as the information assets that organizations collect, process and store during regular business activities, but generally fail to use for other purposes (for example, analytics, business relationships and direct monetizing). Similar to dark matter in physics, dark data often comprises most organizations’ universe of information assets. Thus, organizations often retain dark data for compliance purposes only. Storing and securing data typically incurs more expense (and sometimes greater risk) than value.

Note 2. Operational Technology

Operational technology (OT) is the technology used in the running of an enterprise that senses or controls physical assets.

OT is used exclusively in enterprise contexts. The term originated in industrial situations where there was investment in plant and equipment control systems. We now use OT to refer to any technology in businesses that is used in the running of the operation, with a few exceptions. Exceptions include back-end IT systems used for processing and storing of information and other general-purpose hardware and software.

Examples of OT include a wide range of items such as ATMs in banks, medical equipment, telecommunications infrastructure switching and routing equipment, building management systems, navigation systems, and many more. OT does not apply to technology used by consumers.

OT excludes IT systems, but the data produced by OT is accessed and used by IT systems for analytics, scheduling, business planning and other purposes. The integration of OT with IT systems is a major issue for enterprises due to the value that can be realized.

OT typically uses dedicated hardware with embedded software, rather than general-purpose PCs and servers, but this is not always the case. Some hardware — for example, computer networks — may be shared between OT and IT systems. OT is, however, normally designed for a specific operational purpose, and is not designed for extensibility beyond that purpose.

OT equipment will normally be connected. In these cases, OT becomes a part of the IoT. There are examples of nonconnected OT, such as handheld test equipment. OT also includes some nonelectronic devices and equipment, such as pressure relief valves. There is, however, a long-term trend toward total connectivity. When this happens, OT will become a subset of IoT.

Gartner Research G00264728, Kristian Steenstrup Rita L. Sallam Simon F Jacobson Leif Eriksen, 19 August 2014

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