What Impact Will Data Management Have on Your IoT Strategy?

Vipika Kotangale | September 23, 2022 | 791 views | Read Time : 11:28 min

Discover the impact
For businesses to grow, they must be armed with the right technology and implement the right strategies to get a high return on their investments. With an IoT strategy, you can successfully make sense of the high volumes of data generated. IoT is about having devices with sensors communicate with other devices over the internet and share real-time data or parameters to maintain healthy system processes. Sharing and transferring data in real time over the cloud creates a lot of data that needs to be carefully managed.
Not having a streamlined method to control and manage the volume of data to capture, send, transmit, and receive over the cloud poses many space constraints as the data piles up quickly. Furthermore, deciding what data to keep and what to discard, how long you need the data, and for what purpose are all critical. Some standard IoT devices include sensors, lights, alarms, and cameras that a smartphone can control.

Learn about the importance of data management in establishing an IoT framework below.


The Top Reason for Establishing an IoT Framework Needs Data Management

Learning from past data trends to make future decisions in an IoT framework is critical. Data management acts as a layer between the IoT devices generating the data and the software accessing the data for analysis and services. It helps review, analyze, and navigate the massive amounts of structured and unstructured data. Defining which actions trigger responses to create data in your process is necessary to monitor your product and services and to keep your customers satisfied. In an IoT framework, managing the large amounts of data that are generated and collected means learning from the past and predicting what will happen in the future.


Why is Data Management for the IoT Framework Crucial for Medium and Large Enterprises?

Creating a better product is essential to add more value to your product offerings and avoid recalls, keeping your brand reputation at stake. The more data, the deeper the analysis, and the more refined the product, the greater the need to manage large amounts of data efficiently.
The future of IoT data management is promising when it comes to improving all aspects of your business processes, mainly controlling the automation and manufacturing processes and software triggers. Check out the in-depth benefits of data management in IoT.

  • Data management in IoT helps conduct a field test of your IoT products before deployment.
  • Improve the uptime of your business production lines and equipment.
  • Perform seamless decision-making for planning, scheduling, and execution systems to meet the changing customer and market demands using accurate and current data.
  • Data management helps efficiently deploy IoT solutions such as enterprise resource planning (ERP), enterprise asset management (EAM), and manufacturing execution systems (MES) in manufacturing businesses.
  • Data management helps remote monitoring of automation systems and robotic systems in industrial IoT needs current data and management.
  • Improve production flexibility and responsiveness by welcoming smart manufacturing using IoT data management.

When it comes to the data management of IoT devices, different types of data management systems take care of structured and unstructured data.


8 Data Management Systems for Your Enterprise IoT Devices

IoT device management means registering, organizing, monitoring, and remotely managing IoT-connected devices at scale. Various cloud architectures with different data management systems help with efficient IoT device management. In addition, equipment data, sub-meter data, and environmental data help track the performance of your IoT devices through IoT data collection. Let's find out how data management systems for IoT devices would help develop an IoT strategy for your large enterprise.
IoT gateway device management involves many steps in keeping your operations healthy and maximizing uptime. These are provisioning, authentication, configuration, control, monitoring, diagnostics, software updates, and maintenance. In addition, data management systems aim to make data available for analysis in the long term. The different data management systems are as under:

  • Querying
  • Production
  • Collection
  • Aggression/Fusion
  • Delivery
  • Pre-processing
  • Storage, updating and archiving
  • Processing or analysis.

These data management systems capture, organize, store, retrieve, and analyze data when required. Sorting out the data management in IoT will initiate your internet of things database scalability. An IoT data lifecycle is built around the data management systems in the data flow, which acts as guidelines or checkpoints for a smooth data flow across your IoT platform. Let us unfold them below.


Seven Guidelines for Cost-Effective IoT Data Management

• Querying: Accessing and retrieving data for temporary monitoring. For example, you could ask IoT devices or sensors for data in real time to learn more about trends and patterns.

• Production: Sensing and transferring data by the "things" or IoT devices in an IoT framework is the data production phase. Pushing the data to the cloud network and the IoT database servers and reporting it to the interested parties. This rich data has different formats such as audio, video, or image content, and is time-stamped and geo-stamped.

• Collection: Collecting and retrieving data for a predefined time interval and sharing it with the governing components within the gateways is a part of the collection. Filtering out valuable data and compressing it accordingly helps seamless data transfer. It is also a part of data collection.

• Aggression or fusion: Part of the aggression is real-time data transmission across the network to increase the rate of data streaming over the limited bandwidth. It pulls together information from different points of contact and reduces the amount of information that needs to be stored and sent.

• Delivery: Collating the data from multiple touch points across the IoT framework and summing it up for the final responses is a part of the data delivery management system. Making data ready for permanent data storage is also a part of it.

• Preprocessing: Removing redundant, missing, and incomplete data and making all the data unified is a part of preprocessing. Data cleaning is also one of the preprocessing methods applied to data mining.

• Storage, Update, and Archiving: Storing data in an organized way for long-term offline usage or big-data systems is a part of the storage data management system. It can be decentralized or centralized as per the required capabilities.

• Processing or Analysis: Retrieval of stored packets of data accessed for an efficient analysis is a part of data processing or analysis in a data management system.

Whenever handling large amounts of data, an efficient data management system will solve numerous problems concerning your IoT strategy, as discussed above. Find out exactly what can keep you from implementing IoT.


5 Growth Challenges in Data Management for IoT Technology

  • High Initial and Ongoing costs:
Upgrading the hardware and software infrastructure that is already in place, hiring IoT-trained staff, and building an IoT infrastructure will all require upfront and ongoing costs.
  • Vulnerability:
Your IoT security strategy is a critical aspect of your IoT platform strategy. Multiple data points for structured and unstructured data captured, transmitted, stored, and retrieved by software come with security risks.
  • Procuring Quality Hardware:
Finding compatible hardware for your requirements and building an infrastructure around them can take a while regarding decision-making for scalability. In addition, hardware must remain supportive of the quick adoption of future software innovations.
  • Installation and Upkeep of Hardware Infrastructure:
Setting up a complex IoT strategy with the implementation of IoT data management, infrastructure, security, and more takes time and expertise. One of the other big worries is keeping the hardware infrastructure in good shape so that security can't be broken.
  • Constraints on Scalability and Agility:
The humungous IoT data traffic poses a severe concern for appropriate control of the data storage, retrieval, analysis, monitoring, and everything aligned with IoT data management. Also, the fact that IoT data doesn't last as long as other types of data is a risk to the way data flows and is collected.

Now, let us figure out how to implement IoT that aligns with your business objectives.


How to Implement IoT in Line with Your Business Goals

A complete analysis of your immediate and long-term business objectives is critical as it helps decide which data to keep and which to discard after how much time. Every byte of data you hold and analyze comes with a cost for storage, retrieval, and security, which can be a barrier to implementing IoT for your business. Identifying IoT data collection helps you align your IoT implementation strategy with your business objectives. Here are a few ways to address your implementation of IoT.

  • Consider the use cases of IoT data management as per the processes involved in your business.
  • Implement security protocols for encryption and restricted access as per the type of business data.
  • Organize training for the existing workforce and hire skilled professionals in IoT.
  • Understand your business's data requirements, including the data collection process.
  • Allow enough budget for IoT infrastructure and resources.
  • Consider the design and development of the product as per the customer's behavior.
  • Consider the impact of the environmental conditions affecting your business.
  • Measure real-time performance metrics using a suitable IoT sensor to streamline your process.
  • Take automated decisions with the help of AI once IoT sensors recognize the performance gaps.
  • Choose the right IoT platform that defines how you communicate and handle data.
  • Understand that IoT implementation is a complex process and needs commitment.
  • Collect only the important data and statistics for a smooth workflow and to lower the cost of putting IoT into place.
  • Taking into account where your storage and production lines are located, choose the best ways to gather, organize, and analyze your data.
  • Use cold path analytics for the long term and hot path analytics for real-time data storage.

Building infrastructure with scalability in mind will help small businesses grab market share quickly and efficiently. As a result, medium-sized enterprises will find prominence in their industry. Using data visualization in business intelligence allows for rapid optimization of your IoT devices and for controlling data management costs in the long run without negatively impacting performance. Explore more about IoT data visualization down below.


Role of Data Visualization in IoT for Business Intelligence

With IoT data visualization, you can optimize business processes by applying visualization business intelligence to get your business ready to scale. Discover the role of data visualization in your IoT strategy.
  • Make sense of the data you've collected or saved.
  • Patterns and trends should be recognized.
  • Check the data for inconsistencies and errors. The output should then be visualized over time for analysis and monitoring.
  • IoT infrastructure and devices improve performance and streamline the IoT data flow.
  • Analyze real-time data correlations across multiple business verticals using the IoT communication platform.
  • Make future decisions based on the data captured in the past.
  • Get actionable insights on customer behavior and
  • Identify the factors impacting your business.

Once you identify the gaps in business processes, you can make changes to the process and further improvise. Creating an optimized workflow and detecting errors and faults in a process early are the primary goals of data management in an IoT strategy. Tackling vulnerabilities in data security and data redundancy helps the cost-effective implementation of IoT for small businesses, opening avenues for scalability. With IoT data management, you can also optimize your products to make customers happier and get a bigger share of the market, which is great for your business's growth.


Summarizing

With secure access control, encryption, software updates, endpoint security, and communication protocols in place, the relentless power of data visualization for analyzing and monitoring the captured data has proved to be unmatched. Bringing resilience and giving a rapid boost to the scalability of your medium and large enterprises is now becoming a norm with organized IoT data management.

FAQs:


• What is the most significant benefit of IoT?

IoT helps devices or sensors report real-time data for smooth interconnected production operations. In addition, IoT keeps healthy functions throughout and minimizes the turnaround time for troubleshooting and maintenance.


• What are the three types of IoT?

Depending upon the needs from time to time, the three types of IoT include short form, medium form, and long form. The short form meets immediate needs, the medium form meets future needs, and the long form keeps the system running smoothly.


• How does data analytics help IoT?

Effective process optimization is possible by analyzing the data generated in an IoT framework. It helps boost efficiency, and connectivity, cut costs and unlock scalability.

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As a board member of the alliance and one of the participants in the development of Matter, TI can help engineers build wider and more interconnected IoT ecosystems – overcoming current compatibility challenges caused by otherwise fragmented ecosystems. In addition to working alongside the alliance, TI is also a contributor within the Thread Group and a member of the Wi-Fi Alliance. Enhance system efficiency and security with Matter-enabled SimpleLink wireless MCUs New TI SimpleLink wireless MCUs can help designers reduce standby power consumption up to 70% in Thread applications compared to competing devices, extending battery life up to four years while using five-second polling. For long-range connectivity applications, the highly efficient integrated power amplifier in these wireless MCUs enables reliable connectivity by consuming 101 mA at +20 dBm – the industry's lowest power consumption – further reducing battery power consumption at higher output power. Designers of Matter-enabled Wi-Fi applications can leverage TI's dual-band, multilayer security approach to protect device data and secure against cyberthreats without the need for additional external components. Integrate Matter easily with software and hardware resources Streamline your design of Matter-enabled applications by connecting with our applications engineers through the TI E2E™ Wireless Connectivity design support forums or by watching the video, "TI Matter Technology Demo: Unified Standard for Connected Objects," to learn how TI wireless MCUs streamline Matter connectivity. FAQ guides are also available on the TI E2E™ design support forums for getting started with the CC2652R7 (Thread) and CC3235SF (Wi-Fi). Package, availability and pricing Engineers can start prototyping today by requesting LaunchPad™ development kits for Thread LP-CC2652R7 (US$39.99) and Wi-Fi LP-CC3235SF (US$54.99). The CC2652R7 and the CC3235SF are now available through TI and authorized distributors, with pricing in 1,000-unit quantities starting at US$3.01 for the CC2652R7 and US US$4.53 for the CC3235SF. Flexible connectivity solutions tested to match your design needs TI's breadth of connectivity products allows designers to choose the right feature set for their applications, whether they are deciding between wired or wireless, high bandwidth or signaling, short or long range, or any other options that help drive innovation in a connected application. About Texas Instruments Texas Instruments Incorporated (Nasdaq: TXN) is a global semiconductor company that designs, manufactures, tests and sells analog and embedded processing chips for markets such as industrial, automotive, personal electronics, communications equipment and enterprise systems. Our passion to create a better world by making electronics more affordable through semiconductors is alive today, as each generation of innovation builds upon the last to make our technology smaller, more efficient, more reliable and more affordable – making it possible for semiconductors to go into electronics everywhere. We think of this as Engineering Progress. It's what we do and have been doing for decades.

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ENTERPRISE IOT

Growth Opportunities for Sensors Enabling IoT in Retail

ReportLinker | November 25, 2022

Reportlinker.com announces the release of the report "Growth Opportunities for Sensors Enabling IoT in Retail" However, sensors as a standalone device would not be able to play a vital role in the IoT space as only partial data would remain. Hence, enabling technologies that assist data collection and analysis are necessary to generate meaningful information. Retail IoT includes technologies that collect data (sensors) and those that integrate sensors and facilitate a more immersive retail customer experience. The IoT in retail will primarily transform supply chain management (SCM) and customer engagement opportunities. With the need for the end-to-end traceability of goods and services in retail SCM increasing, and end users having high expectations of an immersive customer experience, it is imperative that retail industry participants across the value chain integrate IoT capabilities to compete. The scope of this research study is limited to technologies that enable an interactive and immersive customer experience. The study assesses emerging technologies that facilitate a personalized customer experience in the retail industry, including sensors, RFID, Bluetooth, Wi-Fi, advanced display, smart lighting, robotics, VR/AR, data analytics, and IoT-based payment technologies, such as NFC. Questions the Research Addresses:What are the main enabling technologies transforming IoT in retail?What are the key factors influencing adoption in the innovation ecosystem? What are the key industry initiatives? Who are the main participants impacting technology development?What are the patent and funding trends in the retail IoT space?What are the future growth opportunities for market participants? About Reportlinker ReportLinker is an award-winning market research solution. Reportlinker finds and organizes the latest industry data so you get all the market research you need - instantly, in one place.

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