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Intro: The world runs better with IoT

Research from Gartner Ten Critical Data Management Implications for Your Internet of Things Initiatives

Our Perspective

About T-Systems

Making Your Business Thrive With IoT

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Intro: The world runs better with IoT

Today IoT (Internet of Things) is often talked about as if it were the result of a single planned vision in the development of connected technology – but this is not true. Instead, outside of the fields of heavy industry, IoT has until recently, been allowed to develop sporadically with less consideration given as to how it would be used to connect with other ‘things’.

From spontaneous to precision

But this is changing, as IoT technology advances and grows to encompass a wide range of industries, including Transport & Logistics, Retail, Automotive, Public and Manufacturing to name but a few – it is clear that IoT is here to stay. Not only will it remain – but it is evolving rapidly with industry analysts forecasting that by 2020 there will be approximately 30 billion IoT devices in the world – setting the stage for the world’s largest connected network of all time.

As a result, many IoT developers and manufacturers are becoming increasingly aware of the breadth in scope that their devices and software hold for multiple connective applications and are now actively incorporating these aspects into their designs from the outset.

As such a new era has arrived, one where IoT is capable of offering unparalleled levels of interoperability – providing users with a higher

degree of precision, unlocking new ground-breaking capabilities and benefits in the process.

Understanding what IoT needs to succeed

As this report shows, one of the chief requirements for enterprises to incorporate IoT solutions into their digitization vision is that they have the ability to handle the exponential increase in data that IoT software and devices generate. Similarly, for IoT to work best, it needs to do so seamlessly. In order to achieve both, connectivity is a priority and as such must be dependable, flexible and of the highest quality.

A closer connection

That is why for us at T-Systems, connectivity forms one of the cornerstones of effective IoT realization. Enterprises need to be able to work with network providers who can offer connectivity that is consistent, scalable and able to handle any IoT project.

By recognizing that a one-size fits all approach does not remedy connectivity challenges, we have ensured our mobile data networks, from 2G through to the imminent release of 5G – provide businesses with stable and secure IoT capabilities. When combined with our multi-cloud offerings the results are solutions that improve agility, improve performance and reduce cost.

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Similarly, we are committed to offering connectivity that fulfils more specialized requirements, such as our new wireless NarrowBand IoT (NBIoT) – an IoT dedicated network that provides a simple, low maintenance vehicle for secure M2M communication.

Improved platforms

While connectivity is crucial for IoT – improved capacity and a more reliable flow of data would be of little comfort to organizations if it were still too difficult to store, manage and act upon this information. This is where utilizing the right platform or combination of platforms can allow companies to shape and pair IoT technology to their specific needs.

It is the reason we have poured our industry know-how into ensuring that businesses have the appropriate level of control – providing IoT platforms that can be catered to their needs, even as these needs change. Our Cloud of Things was created to enable businesses to manage their IoT devices remotely, ensuring that these machines receive necessary software updates to maintain performance and security while allowing them to be organized in groups as well as individually and – where appropriate, offer a single push button execution for bulk-operations.

Not only should organizations seek network providers who offer the best IoT specific platforms but crucially, they should choose to work with those who provide a one-stop solution and offer ongoing support and provide customizable levels of user/managed control.

Whether it is trail-blazing manufacturing 4.0 – supplying organizations with the means to collect, analyze and visualize sensor data from various IoT machines or to allow retail or transportation enterprises to track and trace the status of inventory and stock in real-time using installed IoT SIM cards – our Cloud of Things SaaS platform dramatically reduces investment and operation costs while simultaneously boosting efficiency. As with the Cloud of Things, our IoT solutions can be run on one or simultaneously on multiple dedicated platforms.

Source: T-Systems

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Ten Critical Data Management Implications for Your Internet of Things Initiatives

Research from Gartner

Internet of Things initiatives, architectures and technologies have many data-related implications. Data and analytics leaders can be best prepared to support their organization’s IoT investments by addressing 10 critical impacts on their data management strategy and capabilities.

Key Findings

• The data-related challenges of the Internet of Things (IoT) extend far beyond the obvious issues of scalability to address the massive data volumes generated by large numbers of devices; new requirements of variable latency, distribution and governance arise.

• Early adopters of IoT solutions emphasize that management of data is a critical competency. In recent Gartner studies of organizations implementing IoT solutions, 68% indicated data security and data quality to be top data governance domains, and 67% indicated a focus on integrating IoT data with traditional data types.

• Interactions with Gartner clients in many industries show the data management, architecture and governance capabilities of most enterprises are not yet equipped to address the highly distributed, continuous, real-time and risk-laden nature of IoT data.

• Issues of data governance, with an acute focus on data quality, privacy and security, are top of mind among organizations that are early adopters of IoT solutions.

Recommendations

Data and analytics leaders seeking to evolve their data management strategies to support IoT requirements must:

• Modernize data management capabilities for IoT solutions by identifying gaps and creating augmentation plans for each of the common capability categories — describe, organize, integrate, govern, share and implement.

• Maximize impact and consistency by applying these common capabilities throughout the flow of data in IoT solutions, and across multiple IoT solutions.

• Support distributed data architectures, in order to adapt to the distributed nature of most IoT solutions, by making data management capabilities available at the edge (on “things” and gateways, in addition to traditional platforms).

Strategic Planning Assumptions

By 2020, most data and analytics use cases will require connecting to distributed data sources, leading enterprises to double their investments in metadata management.

Through 2020, half the cost of implementing IoT solutions will be spent on integrating various IoT components with each other and the back-end systems.

Analysis

Data and analytics leaders are not prepared for the data management-related implications of the Internet of Things (IoT). The IoT will challenge their capabilities, skills, processes and tools with complexity and scale, as well as with new governance implications. While most organizations seek to apply existing capabilities to new IoT initiatives in most cases existing capabilities are not enough. IoT solutions require management of new and different types of data, generated by and housed on new and different platforms, creating new and complex integration challenges (see Figure 1).

Some of the most critical IoT implications for data and analytics leaders involve data management capabilities — the collection of tools and technologies that enables handling of data in the enterprise. Gartner advises organizations to evolve this technology landscape in accordance with the principles of a modern data management infrastructure. These principles are grounded in the concept of common capabilities — a range of data management technology components that can be leveraged and reused in a consistent way across diverse use cases (see Figure 2).

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All data-related use cases, including the IoT and the requirements of IoT solutions, require a range of critical capabilities for managing data — Describe, Organize, Integrate, Share and Govern. Each of these capability classes is composed of many detailed capabilities, which can be combined in dynamic ways.

FIGURE 1 Distributed and Complex Nature of IoT Solution Architectures

Source: Gartner (March 2018)

FIGURE 2 Modern Data Management Infrastructure

Source: Gartner (March 2018)

When looking at the data management infrastructure of a typical enterprise, Gartner observes that the unique requirements of IoT solutions create implications for organizations across this range of capabilities. We look at 10 critical considerations — mapped against the capability categories of a modern data management infrastructure — that data and analytics leaders must address to ready their data management infrastructure for the IoT.

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Describe — Capabilities to Understand the Meaning and Use of Information Assets

Implication 1. Metadata

Metadata is critical to ensuring proper use, governance and value from information assets, and getting complete metadata on all information assets involved in IoT solutions will present challenges. In IoT environments, metadata representing data lineage (sources, flow, usage and so on) will become increasingly difficult to collect and represent, given the highly complex and distributed architectures that many IoT solutions exhibit. Metadata can be held in the “things” themselves, making a completely centralized approach to metadata management impractical or unnecessary. In addition, capturing metadata about new data types (sensor, device and machine data) becomes challenging when schemas and other artifacts may not be readily available or understood by existing metadata-related tooling. Organizations will be challenged to discern how to map from the metadata models representing new data types in the IoT space, to existing IT systems. This will be critical for linking IoT platforms and other IoT technology components to the applications used for performance management, operational processes, and other business activities that must adapt to behavior in the IoT. In addition, the collection of metadata about access, usage and behavior with IoT information assets will be critical to ensuring the enactment of proper information governance. Master data management capabilities interjected into IoT solutions will be valuable in ensuring a clear linkage between, and understanding of, relationships across IoT data and traditional data sources.

Data and analytics leaders must arm their business and IT teams with modern data catalogs that will allow them to develop inventories of their information assets and make them more accessible, usable and understandable. Data catalogs can manage the metadata sprawl across the multiple data assets common in IoT scenarios. They do this by allowing the business users to inventory, document, analyze and share metadata and create a common metadata repository in order to understand and communicate the business value of their data. In the wake of IoT, this could mean the difference between uncoordinated and siloed IoT projects with ungoverned data and governed IoT projects that can share metadata and best practices developed through a better understanding of metadata.

Action Items:

• Review existing metadata management strategy and tools. Identify available metadata sources representing the breadth of information assets in IoT solutions, develop plans to fill gaps (manually, for example), and use metadata accessibility as an evaluation criteria for IoT platform and solution providers.

• Consider where and how to maintain the metadata, including scenarios such as distributed storage of metadata to compensate for offline devices.

• Use data catalogs to curate the inventory of available distributed information assets and to map information supply chains, by making them an essential component of your data management strategy.

Organize — Capabilities to Suitably Structure and Position Information Assets

Implication 2. Storage Capacity and Location

With the volumes of data generated by IoT solutions, existing storage approaches can be quickly overwhelmed. A limited ability to cost-effectively scale existing storage approaches will create a bottleneck. In addition, with the highly distributed architectures required for most IoT solutions there are many places where data is generated and many platforms on which data is processed (including edge devices, gateways, cloud and traditional enterprise environments). As a result, the historical approach of centralized collection of data is under pressure. Organizations must extend these traditional approaches with the ability to support a more distributed data architecture, because IoT solutions are inherently distributed. They must also apply more proactive approaches to determining what data to keep, and where, rather than assuming that everything must be persisted. The nature of the IoT solution architecture (thing-centric, mobile-centric, gateway-centric, cloud-centric or enterprise-centric) will increasingly drive the choice of storage locations.

Action Items:

• Rethink your information architecture with a reduced focus on centralized repositories and increased openness to distributed architectures.

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• Regardless of data storage location, carefully consider what it is appropriate to store (according to business, regulatory or other requirements) and for how long, and what it is advisable to throw away.

Implication 3. Data Persistence Format and Structure

While demand for alternative data persistence formats and structures — extending the capabilities of traditional relational DBMSs — has been growing as a result of new styles of analytic activity, the IoT will bring requirements to yet another level. The volume and pace of inserts resulting from IoT-generated activity can create performance bottlenecks and latencies for traditional persistence approaches. For example, in heavy industrial solutions where thousands of sensor readings per second must be captured, or consumer-related solutions where millions of individuals using “things” are constantly generating data. Newer persistence mechanisms (such as Hadoop and NoSQL DBMSs) or specialized data stores supporting spatial and time-series data may be required to handle the pace and volume of IoT data. This also applies to the widely varying, and sometimes unknown, structure-driving schema-on-read scenarios.

Data and analytics leaders should also consider leveraging their existing investments in data management technology, because existing capabilities may be able to support some

aspects of IoT requirements. Gartner’s 2018 IoT implementation survey shows that while 36% of surveyed organizations are using or plan to use NoSQL or Hadoop for storing IoT data (see Figure 3 below), other styles of data persistence are also being used or considered. Even relational DBMS technology is constantly improving to expand support for non-relational data in multiple formats, including XML, JSON and binary large objects (BLOBs) and also allows for direct SQL based access to data which is still the language of choice for most developers.

Action Items:

• Assess the variety of data format, scale and pace demands of IoT architectures in order to refine data persistence requirements and identify specific data persistence technology utilization and modernization plans.

• Evaluate technologies with strength in capturing streaming, time-series and unstructured data, as well as those supported via the elastic scalability of cloud, because these requirements will be common for future IoT use cases.

• Ensure vendors offering IoT solutions or IoT-related technology components have made suitable choices of persistence technology that can handle the scale of planned IoT solutions.

FIGURE 3 Choices of Data Persistence Technology to Support IoT Solutions

Source: Gartner (March 2018)

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Integrate and Share — Capabilities to Ingest, Combine, Transform and Provision Information Assets

Implication 4. Data Ingestion

The rising demand for capturing streams of data generated by things will create challenges for the current integration capabilities most organizations have in place. Both the “always on” nature of some IoT data sources (for example, sensors on manufacturing equipment running 24/7) and the unpredictable availability of others (such as the periodically connected sensors on various consumer devices) create a different paradigm. Traditional data integration tools and architectures, which often focus largely on bulk- and batch-oriented physical data flow, are not suited to handling environments with such widely varying and unpredictable SLAs.

Action Items:

• Identify new sources of data required for IoT solutions and classify them as to their temporal nature (streaming, event-oriented, occasional, for example).

• Review data integration infrastructure and tooling to identify gaps in supporting the various latency types, in particular for streaming ingestion.

Implication 5. Data Integration and Transformation

The repertoire of data integration paradigms must expand to include real-time and event-driven (variable latency) support. Capabilities for event processing, to parse the streams of IoT data being ingested and filter for events and patterns of interest, must become a core competency of the integration infrastructure. In addition, the IoT will require expansion of integration capabilities to be able to transform and combine diverse data types of both streaming and bulk/static nature. Thereby, joining traditional structured data with data of a less structured or complex structured variety; for example, the time-series data commonly captured in the operational technology environments prevalent in industrial IoT solutions. These are not capabilities inherent in the data integration designs and architectures residing at the center of most current data management infrastructures.

In addition, organizations require capabilities that allow them to combine multiple data integration styles to execute their IoT use cases. For example, bulk/batch integration to be complemented with real-time replication and stream data integration for IoT data integration. The real value of IoT data will be realized when it is combined with other data to provide richer analytic insight, cause an action to occur or amplify the value of other information assets. A wider range of integration styles and patterns is necessary to achieve this goal (see Figure 4).

FIGURE 4 IoT Projects Utilize Many Different Integration Tools to Address IoT Integration Requirements

Source: Gartner (March 2018)

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Action Items:

• Classify IoT data sources by degrees of structure, and identify sources that fall outside the range of transformation and aggregation capabilities in the infrastructure currently in place.

• Seek to add capabilities for complex-event processing (CEP) embedded within data integration architectures, in order to support identification of key events and correlation of data points across streams and other source types.

• Focus on new integration requirements that need to be addressed (for example, IoT device integration, IoT streaming data and legacy operational technology devices) that are not well-addressed by existing integration tools, and develop plans to fill the gaps.

Implication 6. Data Movement and Delivery

IoT solutions depend on seamless, rapid and reliable flows of information across all the extended components of an IoT architecture. IoT solutions will have to deal with scenarios where events are lost or missing (due to device failures) or, potentially, duplicated. Information leaders working on IoT initiatives must determine how data will move from thing, to gateway, to cloud, to enterprise application and beyond. This will require support for multiple communication protocols and formats. Existing bulk-batch-oriented infrastructure supporting current analytic needs will not be suitable for the lower- and variable-latency nature of many IoT solutions and must now be extended to deal with data in a streaming and event-driven fashion. In addition to handling data with variable latency requirements, data integration architectures and tools must adapt to new interface types and APIs (for sourcing data from devices and gateways and enabling sharing of data between them).

Action Items:

• Start to build knowledge of emerging IoT-related standards for APIs and connectivity, as well as common data formats and semantics existing in early IoT platforms and solutions.

• Evaluate data integration technology providers based on their alignment with relevant standards, knowledge of IoT concepts and trends, and plans to include IoT-related capabilities.

Govern — Capabilities to Ensure Conformance of Information Assets to Policy

While information governance is not fulfilled via technology alone, the enactment of information governance policy types can be enabled by technology. Gartner advocates deploying a data management infrastructure with governance-oriented capabilities built in. This is particularly important for IoT solutions, where the pace and volume of data — as well as the sensitivity and value — raise governance concerns that are new to most enterprises. This is particularly the case in consumer-oriented IoT solutions where personal data about individuals is collected and tracked.

Implication 7. Data Quality

Data is being generated by many things, with those things being (or attached to) increasingly critical physical assets or even people (for example, connected healthcare devices). Making sure the data is trustworthy in the context of usage is paramount to accurate condition monitoring, managing performance and monetization of IoT data itself. Most enterprises have taken a reactive or tactical approach to monitoring and controlling data quality in traditional environments, and such approaches will be ineffective for the IoT. Identifying and compensating for gaps in data or corrupted data (from faulty devices), and tracking trends in data quality levels across streams of data, will be required. Data quality capabilities in place in current infrastructures are often focused on data “at rest” (in databases) or at entry points in traditional IT systems.

Performing data quality exercises on streaming/event data — simultaneous processing of data across multiple formats of and a huge number of streams — will increase the complexity of data quality work. The ability to monitor data increasingly emphasizes data quality controls applied to events at IoT edges without the need to extract or move the data, which isn’t at rest and static, but constantly in motion and changing.

Action Items:

• Identify the quality levels required for specific IoT data sources and use cases, and common or expected types of data quality flaws.

• Evaluate approaches for embedding data quality controls into ingestion processes (for example, using stream-processing capabilities to identify patterns of low quality in data

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streams), as close as possible to the point of data creation.

Implication 8. Data Privacy and Security

Data privacy and security were the top inhibitors cited by respondents in a recent Gartner survey of organizations exploring IoT solutions (see Figure 5). Consumer-oriented IoT solutions (such as connected homes and vehicles) will often collect and process data about behavior, lifestyle, location, health and other personal attributes. Privacy and security of such data must be a paramount consideration in IoT solutions. Capabilities for tracking, encryption and access control to sensitive data must be built into the data management infrastructure at all points where IoT data is generated, stored, processed and delivered (driven by well-formed information governance policies). Likewise, industrial IoT solutions, while not likely to be dealing with sensitive personal data, must include strong data security. This will avoid downtime or failure of critical infrastructure and processes, which could potentially lead to massive costs or loss of life (for example, in industries such as transportation, manufacturing and energy).

Action Items:

• Assess IoT data risks in a number of dimensions — security, privacy, quality, retention and

lack of standards — to determine which data governance policy types are relevant, and identify required policy changes.

• Engage your organization’s legal, risk and information security teams to be a direct part of the data management infrastructure modernization effort.

• Explore the addition of functionality for masking of personally identifiable information as part of the “Govern” capabilities in the data management infrastructure supporting consumer-oriented IoT solutions.

Implement — Scaling, Distributing and Controlling Data Management Capabilities to Cope With IoT Demands

In addition to the common capabilities categories, the IoT poses two key challenges for information leaders in deploying and managing an effective data management infrastructure that can operate at the magnitudes of complexity and scope that IoT solutions will demand. IoT use cases drive new SLAs for scale and distribution. While the information capabilities framework (ICF) is not use-case-specific, the data management infrastructure needs to be adapted to meet this new range of SLAs.

FIGURE 5 Data Security Is a Top Concern at the IoT Planning Stage

Source: Gartner (March 2018)

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Implication 9. Scale

IoT means more, in a variety of different directions. This includes massive growth in the data volumes under management, the number of endpoints (things, in IoT solutions) that must be tracked and connected, and a higher pace of data flow across those endpoints and the rest of the infrastructure. Many current data management infrastructures lack the processing capacity and flexible capabilities to grow across the scope of environments that IoT solutions will bring. They will require substantial investment in computing capacity and/or a shift toward more modern technologies (such as NoSQL for data persistence) in order to scale to the levels required for the IoT.

Action Items:

• Consider the role of elastically scalable computing infrastructure, such as cloud computing, to support the critical capabilities of the data management infrastructure.

• Review processes for the operation and administration of information management capabilities, to ensure they can expand to deliver information management technology capabilities to all the locations and platforms required for planned IoT solutions.

Implication 10. Distribution

Not only will the data in IoT solutions be spread across devices, gateways and repositories in the cloud and on-premises, but the processing of that data may happen in any and all of those same locations. Many devices will be powerful enough to perform sophisticated computation on the data they generate and/or have the need to house and process data locally for autonomous behavior. Data management infrastructure capabilities must be adept at managing many pieces of data spread over a wider and more diverse landscape of platforms than ever before. Such capabilities must also be able to effect the processing of that data on any of those platforms. Ensuring reliable distribution and consistency of business rules applied to the data in all locations, then monitoring the execution of those business rules, adds additional layers of complexity.

Action Items:

• Embrace hybrid architectures — with data and processing on that data being located “on device” — in IoT platforms and in traditional on-premises and cloud-based environments.

• Plan for disaggregation and resiliency of workloads that can be broken into components and run anywhere — specifically, focus on the separation of storage and compute of data management and integration workloads.

• Focus on monitoring and manageability — IoT architectures will be the opposite of monolithic, which increases the challenges of monitoring and managing distributed data and its consumption.

Acronym Key and Glossary Terms

CEP complex-event monitoring IoT Internet of Things

Evidence

The evidence for this research note is derived from inquiries with Gartner clients, vendor briefings, primary research conducted by the author, and a survey of participants in a webinar focused on IoT issues and objectives.

Results presented are based on Gartner studies conducted to collect information on best practices for IoT deployments and strategies for developing IoT solutions.

• 2016 IoT implementation solution research was conducted during May and June 2016, among 250 respondents in North America, Europe and Asia/Pacific.

• 2017 IoT implementation solution research was conducted during June and July 2017, among 202 respondents in the U.S., Germany, China and Japan. The largest industry representation was from manufacturing (25% of the sample).

Participating organizations were screened to ensure that they have already delivered IoT solutions or have working projects in progress. All industries qualified, except technology vendors, business consulting services and investment services.

Respondents were required to have involvement/roles in IoT implementation within organizations with annual revenue greater than $100 million.

The survey was developed collaboratively by a team of Gartner analysts who follow IoT. It was reviewed, tested and administered by Gartner’s Research Data and Analytics team.

Source: Gartner Research Note G00344956, Ted Friedman, Ehtisham Zaidi, 27 March 2018

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Our Perspective

Enabling a smooth IoT transition

As this report illustrates, not all companies are in the position to overhaul their present IT systems in favor of incorporating IoT fully into their existing infrastructure. Many businesses will only be able to do so in stages and over a certain timespan. Therefore, to facilitate a smooth transition – it is essential that a network provider’s IoT products work with, and not against an organization’s critical IT systems and can be achieved at a pace that suits them.

Achieving this requires a healthy range of innovative and dedicated IoT network products that can be customized to the IT environment that they will work in tandem with, while simultaneously allowing enterprises to transfer more of their legacy IT over to the IoT sphere in the future.

Products & Solutions

T-Systems operates as a trustworthy, one-stop partner supporting its customers along the entire IoT stack while defining and implementing their IoT/digitization strategies. As a world-leading telco, we not only offer access products to our customers – but also a complete IoT package that includes connectivity, cloud, platforms, security and data analytics which are fully complimented and realised through a process of consultancy and system integration.

Our customers benefit from having access to our own dependable assets which are available along the entire IoT stack. The years of expertise we have gained from focussing on vertical markets provides our end users with a reliable and deep level of technical knowledge and insight.

Furthermore, the strong partnerships we have created with many leading industry experts has enhanced the range of products and platforms (e.g. Microsoft Azure, GE Predix) that we are able to offer from our complete portfolio. In addition as a subsidiary of Deutsche Telekom, we are expanding our footprint of IoT solutions across Europe through our Partner Program which covers all relevant industries – from vehicle telematics to retail commerce.

Consultancy & Integration

At T-Systems, we provide full technical integration of all of our IoT products and solutions into our customers’ existing IT infrastructures (e.g. ERP, CRM etc.). With this approach, our customers benefit from receiving a complete End-to-End solution that spans all key stages from a single, dedicated digital solutions and services portfolio unit. With 4,800 employees, this digital solutions and services portfolio unit combines the competencies of the management and technology consultancy Detecon, the digital services provider T-Systems Multimedia Solutions and the digital areas of T-Systems Global Systems Integration. In 2017, the three business units implemented a total of more than 4,000 digitization projects.

Assessment and support

Just one example of where we combine consultancy with integration can be found in our Cloudifier solution. Starting with a fixed-price assessment, the Cloudifier records and assesses all enterprise’s applications, including their properties and interfaces with other IT services. This consultation, reveals which of an enterprise’s IT systems can be moved to the cloud and even allows for creating test environments to demo – so an organization can see how transferring systems to the cloud will look and behave. Our Cloudifier then offers three standardized transformation services for SAP, Applications and for Communication & Collaboration.

At T-Systems – we aim to be the best global partner for our customers to connect things and digitize their business processes by using our M2M and IoT services. Therefore, we offer our customers a complete end-to-end IoT product along the whole IoT stack including digitization consulting and support services.

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Making Your Business Thrive With IoT is published by T-Systems. Editorial content supplied by T-Systems 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. © 2018 Gartner, Inc. and/or its affiliates. All rights reserved. The use of Gartner research in this publication does not indicate Gartner’s endorsement of T-Systems’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.

Our unique vertical market focus and expertise with the industries of Automotive, Logistics, Healthcare, Energy, Public Sector, Smart Cities and Industrial Automation facilitate customer entry into the digitization of their business processes by providing specific starter-kits for condition monitoring and predictive maintenance, as well as our end-to-end bundles “Industrial Machine Monitoring” and “Asset Tracking”.

Mitigating IoT risk

Risk has been a significant deterrence for companies looking to scale-up their IoT operations. That is why security is of paramount importance across all our platforms and service solutions. T-Systems benefits from the strictest German data protection legislation and we have created some of the world’s most robust data centers to ensure not only peace of mind for our customers, but also to maintain a high quality of service with minimal disruption.

About T-Systems

It is likely that further down the line, manufacturers will introduce standardization for the IoT devices they produce which will lead to improved security robustness and connectivity. For the meantime, enterprises will need to consider the security of their network provider and collaborate with industry experts to ensure a smooth IoT-led transitioning to safeguard their IT future and maximize the ease of which they enter the digital age. The challenges are real but the benefits of IoT are near limitless.

Source: T-Systems

With a footprint in more than 20 countries, 37,900 employees, and external revenue of 6.9 billion euros (2017), T-Systems is one of the world’s leading vendor independent providers of digital services headquartered in Europe.

T-Systems is partnering its customers as they address the digital transformation. The company offers integrated solutions for business customers. The Deutsche Telekom subsidiary offers one-stop shopping: from the secure operation of legacy systems and classical ICT services, the transition to cloud-based services (including international networks, tailored infrastructure, platforms and software) as well as new business models and innovation projects in the Internet of Things. T-Systems can provide all this thanks to its global reach in fixed-network and mobile communications, its highly secure data centers, a comprehensive cloud ecosystem built around standardized platforms and global partnerships, and the ability to offer top levels of security.