When businesses started to harness IoT data, it opened up a world of improved reporting and analytics. Now organizations can use all of their data—from remote devices, sensors, machines and other appliances—to drive and grow business. However, managing and integrating that data isn’t as straightforward as just bringing data into a cloud data system or on-premise warehouse. IoT data provides greater visibility to an organization but it is still subject to quality issues. In fact, 97% of organizations believe that creating value from IoT data is challenging.
For example, a global business has connected devices that speak different languages like kilometers instead of miles or Celsius instead of Fahrenheit. When integrated without validation, these two data sets will tell an inaccurate story. Poor data quality is a big hurdle for companies on their digital transformation journey. Siloed data in varied formats, lack of investment in the right technologies and wasted cloud storage costs erode business value and keep companies from meeting key objectives.
Gartner research shows organizations estimate the average cost of poor data quality at $10.8 million per annum. By ensuring the integrity of IoT data, companies can confidently pursue strategic initiatives such as data modeling, generative AI, machine learning and other advanced use cases. Which in turn, can lead to better product innovation, smart manufacturing, predictive maintenance and improved customer experiences. How can IoT development teams ensure quality and consistency when integrating these data sources? It starts with improved data validation, enforcing data policies and controlling data formats via transformation.
Improving data validation with schemas
Data schemas allow users to create the blueprint for data formatting and how that data relates to other systems. Development teams can leverage declarative policies to make sure that if pipeline issues arise, they can be resolved quickly to move data to its desired destination. Validating data with this approach helps to streamline projects and resolve pipeline issues by implementing ingestion limits to stop bad data from coming in.
Enforcing good data hygiene with policies
Data policies define how data and messages are received once they come into the broker. If data fails validation, policy actions will determine what steps to take. For example, messages can be rerouted, forwarded, or logged. This ensures transparency of message movement across the business. The rules and standards provided by these policies give time back to development teams by reducing time spent identifying and fixing data errors.
Enriching and formatting data to improve integrity
Transforming data as it integrates into the database allows users to filter data out and unify data into a single standard. Transformed data provides the quality and consistency necessary to derive value from those sources—before the data reaches any external systems, drastically reducing costs associated with resources to deal with bad data and inaccurate information. This strategy provides better consistency for data integration and replaces manual transformation efforts, which are not scalable and prone to human error.
IoT data validation brings business benefits
When IT systems work with consistent, high-quality data it can greatly improve business processes and decision making.
- Operational efficiency: The real-time nature of trusted data generated from IoT sources gives companies a competitive edge.
- Market differentiation and innovation: Increasing the quality of data for analysis, not only increases efficiency but also accelerates innovations.
- Cost-savings: Enforcing policies for data validation ensures that the data stored and processed are high-value.
By ensuring all connected devices transmit the same data in the same formats organizations can react quickly and efficiently to new opportunities or challenges
The ability to rapidly reconfigure production lines, automate processes and leverage data analytics allows for shorter development cycles
This eliminates the need to pay to store bad, insufficient data.
When data is accurate, integrated and ready for analysis, it delivers important insights that propel business growth. IoT sources are a wealth of information that - when ingested correctly - can give organizations confidence in their decision-making. Using a platform that allows data validation (through policies, schemas and transformation) to bring data quality and integrity to a business will maximize its value.