Preparing Data Management for the Internet of Things: A Guide
In today's digital age, organisations are grappling with the challenges of applying existing rules to vast, unstructured, and difficult-to-categorise information types, particularly in the context of the Internet of Things (IoT). A recent survey revealed that close to one in ten respondents admitted that their organisations fail to regulate well-established information types such as email, customer data, and public online content effectively.
The Internet of Things, with its myriad devices generating massive volumes of data, poses unique challenges for implementing effective information governance. These challenges include security and privacy vulnerabilities, data quality and management difficulties, interoperability issues, scalability and device management constraints, maintaining data confidentiality, integrity, and authenticity, and complexities in applying traditional security controls.
Security and privacy vulnerabilities arise due to IoT devices' limited built-in security, lack of encryption, irregular patching, and endpoint protection deficits. These factors increase the risks of breaches, unauthorized access, data interception, and privacy violations. To address these issues, organisations are encouraged to build security and privacy by design, incorporating encryption, authentication, endpoint protection, and automated patching from the outset, aligning with industry standards, conducting staff training, and establishing incident response policies.
Data quality and management difficulties stem from high data volumes, varied data sources, and the need to ensure accuracy, storage, retention, and lifecycle management across diverse IoT systems. To tackle these problems, organisations must establish robust governance frameworks that guide data quality evaluation, data retention, access control, disaster recovery, and compliance to maintain trustworthy and available IoT data. Ensuring data quality management is also crucial, achieved through proper sensor calibration, lossless data transmission, error correction, and scalable storage solutions.
Interoperability issues arise due to many IoT devices using incompatible platforms and protocols, complicating integration and unified governance. To address interoperability, organisations can adopt standardized protocols where possible and use composable applications or middleware platforms to integrate heterogeneous devices and systems smoothly.
Scalability and device management constraints are challenges given the large scale of IoT deployments and limited resources on IoT devices, making updates, monitoring, and patching challenging. To overcome these constraints, zero-trust security architectures, continuous device validation, frequent auditing, and behaviour analysis can be employed to maintain trust and control over IoT networks under high scale and complexity.
Leveraging federated learning cautiously can enhance privacy with reduced data transfer, but organisations must remain vigilant about new threats like poisoning attacks affecting data and model integrity.
As the IoT landscape becomes increasingly complex, legal and regulatory challenges will arise. Defining and enforcing clear responsibilities among the team is essential for effective information governance. Information governance frameworks may struggle under the weight of IoT data unless organisations improve data classification and retention strategies. Pre-defining and automating categorisation can limit storage and vulnerability.
Organisations are incorporating social media posts, texts, instant messages, tweets, and online file sharing into their formal information processes. However, many organisations have not allocated content responsibility for instant messaging, mobile, social media, and cloud-sharing. Information professionals often retain data out of caution, leading to a keep-it-all-in-case culture.
The time to start implementing strong information governance for IoT data is now. Determining what information constitutes a record or has potential business value can be difficult and overwhelming for businesses. A connected device in a domestic fridge, for example, can generate personal information about an individual's health, lifestyle, and changing family structure.
As the number of connected devices is expected to reach between 20 billion and 50 billion by 2020, businesses need to work on their information governance strategies to accommodate emerging information types from connected devices. Judgement calls about record disposition will be necessary, but strong information governance can help make these decisions.
In 2015, the number of connected devices and systems in use is expected to reach 4.9 billion. Sue Trombley of Iron Mountain emphasised the need for organisations to start implementing strong information governance for IoT data now, stating, "The time to start thinking about this is now, because it's not going to get any easier as we go forward."
- In the context of data-and-cloud-computing, businesses are grappling with the challenges of implementing strong information governance for the vast, unstructured data generated by IoT devices.
- To tackle the challenges posed by the massive volumes of data produced by IoT devices, organisations must build a robust governance framework in finance and business, focusing on data quality management, interoperability, scalability, and security and privacy issues.
