Updated: May 24, 2019
To fully realize the financial and productivity benefits of IoT deployment, organizations must go beyond simply connecting existing and new machines to the network or cloud. For IT administrators, system integrators and managed service providers, this means designing solutions that enable data coming from the heterogeneous machine network to become part of an automated enterprise workflow. Here we discuss the fundamental principles of data management that need to be addressed and why it's important to achieving IoT deployment success.
IoT is about empowering machines to enhance our lives and business outcomes - whether those goals are making sure managing a city's natural resources to minimize waste, providing critical care to a patient at the right time, minimizing downtime on a factory floor or ensuring a home or office environment is safe and comfortable.
To do this we need machines to more effectively communicate with a) people; and in the not to distant future, b) other machines. The language machines speak in is called data. But not all data is relevant to every audience nor does it need to be transmitted continuously.
Simply put, in the ideal IoT scenario, a machine would be able to push data (or communicate) as needed after answering the following:
Is this data that needs to be communicated?
Who or what is this data important to? Is it a specific person or machine or a group of persons and machines? Or can the machine initiate an automated action itself?
When do they (persons or machines) need the data? (Does this data require immediate action, more information and from who or what, or can it wait?)
How should this data be communicated and in what format(s)?
Does this data need to be stored? If so, where and for how long?
While the above questions may seem a bit simplistic, their potential impact is significant and the answers are more complex to implement than they may first appear. Enabling connected machines to intelligently manage their data is key to making IoT work as it was intended.
Without intelligent data management, we will be only connecting machines with the outcome of creating more work and stress for mankind.
Combined with AI and machine learning, machines will also be able to act on data received from other sources to do a multitude of tasks - from self diagnosing and repairing to automatically adjusting their own operational actions/responses.
At MachineChat, our focus is on delivering solutions that make implementing intelligent data management - whether it's to a new machine or ones that are already deployed - fast and easy. To stay up to date with our latest news, sign up here.