How to make Internet of Things data a performing asset
The Internet of Things (IoT) and data remain intrinsically linked together. Data consumed and produced keeps growing at an ever expanding rate. This influx of data is fueling widespread IoT adoption. IoT data comes from devices that often record processes such as temperature, motion or sound.
The data generated from IoT devices turns out to be of value only if it gets subjected to analysis, which brings data analytics into the picture. Internet of Things data is highly unstructured which makes it difficult to analyze with traditional analytics and BI tools that are designed to process structured data.
Many companies have data lakes where raw data or unstructured data sits in AWS using S3 or on Azure and ADLS. Business analysts need to find a new place to combine those datasets before they can query it. Many analysts are leaving this IoT data untouched – resulting in under-performing assets. Here are some ways to fix this:
Understanding the security challenges related to IoT data and cloud services
Over the past decade, data breaches have grown – forecasts indicate an impact of $6 trillion on global businesses by 2021. This risk spreads over IoT and cloud services since – apart from devices recording environment data – IoT often connects legacy and modern systems together.
In addition, these systems rely on cloud computing for data storage and processing because they are intrinsically limited in their ability to store and process data locally. The result is a long list of security challenges in IoT: vulnerability on devices themselves, transport layer attacks, insecure data flow from sensor to cloud, data integrity and data accessibility.
Leveraging low-cost object stores like S3 and ADLS without compromising on performance
Internet of Things solutions generally use object storage solutions because of their flexibility, scalability and low cost. In addition, IoT data is massive, and analyzing it calls for elastic computing resources that can easily adapt to heavy analytics workloads
Finally, the data generated represents a challenge, and so does the metadata. Metadata is a key element for providing a contextual understanding of the data that users work with; when data is properly tagged, cataloged, and made searchable, it increases its value by enhancing usability and collaboration amongst teams.
However, to perform analytics on object storage, data first needs to be processed. Users often need to manually transform object store data into a format that is consumable by the tools they use. And while data continues to grow, the effectiveness of processes such as ETL is not increasing to keep up.
Users need a platform that allows them to connect their favorite BI or data science tools directly to their data regardless of where it is located without compromising on performance.
Using standard SQL for interactive analytics from any tool.
The promise of IoT, from an analytics perspective, is having extremely granular data that improves operational efficiency. But in order to realize this promise, enterprises also need to make this data consumable by analysts.
To get the most out of their data, enterprises need to be able to analyze data directly from where it is stored, regardless of where it is stored at and without the added complexity of IT interventions.
Data consumers need capabilities such as the ability to run ad hoc queries, low latency, high concurrency, workload management, and integration with any BI tool, as well as being able to consume any data from any source using the robustness and flexibility of SQL, since, in the majority of enterprises, SQL is the most popular data access language known by users.
Building on open source projects like Apache Arrow to give best-in-class performance, security and self-service
Infrastructure based on open source delivers a number of benefits to enterprises, including: faster development cycles (building on the work of the community of open source contributors), more secure and thoroughly reviewed code, and no vendor lock-in.
For example, data infrastructure built on Apache Arrow allows enterprises to leverage the benefits of columnar data structures with in-memory computing providing dramatic advantages in terms of speed and efficiency.
Increase interoperability and make use of all your data
Interoperability is critical to maximizing the value of IoT ecosystems. Data from different types of sensors and systems is used to improve the efficiency of data-driven decisions. Naturally, each system will generate data in different formats at a rapid rate.
Storage costs have declined more than 20 percent over the past two years meaning that keeping data has become considerably cheaper than throwing it away. But the key is to unlock the value of this data through analytics, rather than letting it lie. Most IoT data collected today is not used or fully analyzed.
Data-as-a-Service allows users to tackle this challenge by providing a platform where business users can easily discover, curate, and share data from any source, then analyze with their favorite tools, all without being dependent on IT.
Combining IoT and business data
IoT analytics involves data sets generated by a virtually limitless number of devices, which are complex – and economical – enough to enable a large number of use cases ranging from preventative maintenance to operations optimization.
The value of the Internet of Things becomes even greater when combined with existing enterprise data sources. Combining IoT data with enterprise data sources e.g sales, customer, and product information is the key to reach business goals such understanding customer behavior, marketing campaign efficiency, and enhancing the user experience, to name a few.
But if an enterprise doesn’t have a data analytics environment that allows users to work with data from different systems, a simple analytics task can turn into a major data engineering project.
Data-as-a-service provides a solution to this challenge by allowing enterprises to consume their data regardless of where it is stored, by allowing for data to be easily joined to other sources, and by eliminating the complexities of moving and replicating data in order to make it available to their users.
One of the main challenges in IoT is that everything is connected, meaning that all data to be analyzed will have to be transported from the device to the repository. This is risky for data integrity and security.
Underestimating security and privacy implications can degrade customers’ trust in the enterprise. Not knowing who owns the data, who is seeing it, how the data has changed, impacts the value of data gathered through IoT ecosystems.
To succeed in the Internet of Things universe, enterprises need to leverage the features of a platform that provides them with a powerful and flexible set of security features that integrate with the controls deployed across enterprise systems, and provides additional capabilities for masking and uniform, fine-grained security policies no matter where the data is managed.
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