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According to research by Gartner, there will be 6.4 billion connected devices in 2016, up 30% from 2015. Within enterprises, these include generic or cross-industry devices as well as vertical-specific devices that are found only within particular industries.
As the Internet of Things (IoT) has pushed enterprise data from both types of devices into the forefront of critical global business decisions, the stakes of automation have dramatically increased. In order for corporations to leverage the benefits of the IoT’s always-on, data-gathering connectivity, they have to ensure that quality, properly governed data are driving the best business strategies possible.
Increasing Data Quality and Incorporating New Data Streams
The data generated from a device on the factory floor being used in the production pipeline may include large amounts of information but it is only valuable if it is accurate. In the connected world, data automation can drive inventory and essentially run the business. The use of “smart” data such as real-time warehouse conditions, inventory fluctuations and asset feedback are often funneled into enterprise resource planning (ERP) and other corporate systems, but whether they are accurate and relevant can have extraordinarily expensive consequences for the enterprise.
Businesses need to ensure that the data driving the business processes is correct and relevant for real-time business decisions. With high data volumes and pipelines accessing data quickly, the process can break down exponentially faster. Due to the speed of automation, businesses simply can’t afford to make mistakes.
Quality data is critical for advancing businesses that are highly data-driven, especially with regards to manufacturing, supply chain and the movement of goods. As the IoT becomes more ubiquitous and the streams of data increase, businesses need be prepared to fully integrate the quality data into their business model.
Ensuring Data Governance: Setting and Enforcing Policy
How can a company ensure that the data being collected is accurate at its core? With the rise in the IoT, there are increased security issues around who owns the data, who has access to seeing the data, and the source of the data. Good data governance ensures good data quality, i.e., data that are both correct and relevant to the company’s operations.
There is currently momentum towards improving application data management and data governance to be able to set and enforce policy on the data. Enterprise policies should be applied to all data streams working within the system to ensure consistent results. For example, for businesses that rely heavily on data to know when inventory gets low or which products were shipped in the last 12 hours, it is critical to manage and govern data in a way that is compatible with the rest of the business strategy.
With the rise of the IoT, there are great amounts of data coming from a variety of different source systems. The data are subsequently used downstream to drive reports that will eventually help shape decisions affecting the overall business. For example, many enterprises use RFIDs to track the merchandise leaving warehouses and being delivered. Others gather data that can be used for preventive maintenance to the entire system and warns about imminent problems.
For both of these examples, if a problem arises, a data governance system would detect the issue and inform the data owner about the policy breach and they could take action based on pre-determined workflows and scenarios previously set forth in the governance solution.
Another major aspect of data governance is establishing ownership of various data so that they can be deemed reliable. Proper data governance enables businesses to be confident that the company’s policies are being executed and that issues with process or reporting that occur downstream from the data collection point can be traced back to the point of origin. Any problem along the pipeline can be pinpointed and notifications can be disseminated so that the business can run properly.
Best practice enterprise governance addresses data quality issues, data errors that can develop from malfunctioning machines and noncompliance that can arise when multiple systems or data sets come together from different connected sources.
If any enterprise data is incorrect, irrelevant or incompatible with the rest of the company’s systems, the whole process can break down and result in a high volume of errors and wasted capital. More interconnected devices and technology systems deliver the promise to significantly accelerate supply chain, customer fulfillment, manufacturing, logistics and other areas of business across the globe. Governance that can set and enforce company data policy, coupled with the proper steps for establishing ongoing quality, reliable data, should be the end-goal for those wishing to reap the benefits of IoT into the future.
About the author: Charles Evans is Vice President of Product Management, BackOffice Associates