The other day we were discussing and debating on a solution to be designed to meet the sensing needs for access, temperature, and humidity for some devices which form part of a networking infrastructure ecosystem. The idea was to build an IoT based system for monitoring and control.
The design discussions veered around the ability to collect data from the sensors and the types of short range communication protocols which could be deployed.Questions and clarifications were raised if we were compliant to use short range communication protocols in sensitive areas as customer Data Centres which are like owned and that they may be custodians of data of their end customers.
The hidden perils of data acquisition and data ownership reared its head which needed to be addressed as we moved forward.
The data which is acquired by sensors is essentially Machine Generated Data (MGD).This post will dwell on the subject of data ownership of MGD as follows :
- Sensors ( Data Acquisition and Communication )
- Machine Generated Data
- The Lifecycle of the MGD and the Ownership Paradigm
- Who should be the owner of the MGD?
Sensors (Data Acquisition and Communication):
In the IoT ecosystem, the physical computing frontier is managed by the Sensors .Sensors essentially include three fundamental functions:
- The act of sensing and acquiring the data
- Communication of the data through appropriate protocols to communicate their readings to internet cloud services for further aggregation and trend analysis
- The activity is energized by power supply,
The additional functions would include processing/system management and user interface.
The Digital Computing part comprises the IoT application. This is determined by the types of sensors, cloud connectivity, power sources, and (optionally) user interface used in an IoT sensor device.
When making physical measurements such as temperature, strain, or pressure, we need a sensor to convert the physical properties into an electrical signal, usually voltage. Then, the signal must be converted to the proper amplitude and filtered for noise before being digitized, displayed, stored, or used to make a decision. Data-acquisition systems use ADCs (analog-to-digital converters) to digitize the signals with adequate signal conditioning.
Sensor data communication to the cloud can be done in multiple ways from wireline to wireless communication of various complexities. While wire line communication has some important benefits (such as reliability, privacy, and power delivery over the same wires), wireless communication is the technology that is the key catalyst in the majority of IoT applications that were not previously practical with wired systems. Reliability, channel security, long range, low power consumption, ease of use, and low cost are now reaching new levels, previously thought infeasible
Some examples of recently popular IoT wireless communication types: Wi-Fi, Bluetooth Low Energy (aka Smart), Zigbee (and other mesh 802.15.4 variants), cellular, LPWA (Low-Power, Wide-Area network variants: Ingenu, LoRaWAN, Sigfox, NB-LTE, Weightless), and Iridium satellite.
- Machine Generated Data (MGD) :
Sensor data is the integral component of the increasing reality of the Internet of Things (IoT) environment. With IpV6, anything can be outfitted with a unique IP address with the capacity to transfer data over a network. Sensor data is essentially Machine Generated Data. MGD is that is produced entirely by devices/machines though an event or observation.
Here we would define human-generated data, what is recorded is the direct result of human choices. Examples are buying on the web, making an inquiry, filling in a form, making payments with corresponding updates on the database. We would not consider the ownership of this data in the post and would be limiting our post to MGD.
- The journey of the MCD and the Ownership Paradigm:
The different phases exist in the typical journey of Machine Generated Data .
Capture and Acquisition of Data– This is a machine or a device based function through signal reception.
Processing and Synthesis of the Data – This is a function which ensures enrichment and integration of Data
Publication of the Data – This is done by expert systems and analysts who work on exception management , triggers and trends .
Usage of Data – The action which need to be taken on the processed and reported information is used by the end user .
Archival and Purging of Data – This function is essentially done by the data maintenance team with supervision.
Now let us dwell on the Ownership Paradigms.They range from the origination of data , adding value to the data through make over , monetising of data through insights generated. Interestingly, let us explore if there is any conclusive method for determining how ownership should be assigned. A number of players may be involved in the journey of the data (e.g. the user, hardware manufacturer, application developer, provider of database architecture and the purchaser of data, each having an equal lay of the claim in different stages of this journey )
- Who should be the owner of MGD :
Let me share the multiple and conflicting views :
The owner of the device which records Data .In essence, the owner of machine-generated data(MGD), is the entity who holds title to the device that records the data. In other words, the entity that owns the IoT device also owns the data produced by that device.
But there could be a lack of clarity if the device is leased rather than owned.. When real-world constructs such as lease holdings of (say servers) come into play, it indeed gets complex and even murky
The owner is the user of the Data :The other dimension is data may be owned by one party and controlled by another. Possession of data does not necessarily equate to title. Through possession there is control. Title is ownership. Referred to as usage rights, each time data sets are copied, recopied and transmitted, control of the data follows it. There could be cases where the owner of the device could be the user of the data.
The maker of the Database who essentially invests in aggregating, processing and making the data usable is the owner of the Data :This has a number of buyers of this paradigm . The owner of a smart thermostat does not, for example, own the data about how he uses it. The only thing that is ‘ownable’ is an aggregation or collection of such data provided there has been a relevant investment in carrying out that aggregation or collection (the individual user is very unlikely to have made that investment). The owner here could be the Home automation company. The value which could be generated though this investment could be producing market intelligence, exploiting the insights form data to build market presence and differentiation,
The purchaser of Data could be the owner of the Data: An auto insurance company could buy the vehicle generated data ( from the makers of automobiles ) and could design a product for targeted offerings to specific market segments based on say driving behaviour patterns and demographics .This may not be as easy as this seems – refer the url : http://joebarkai.com/who-owns-car-data/ which states that the owner of the vehicle and not the maker of the car owns the data collected from the electronic data recorder.
The value chain of who owns the data can be a complex one with multiple claimants. As one aggregates more sources it just gets more complicated. A good example is in the making of smart cities. The sources of data can be from multiple layers and operational areas . City authorities would be making the effort to make use of the data in areas of waste management , traffic congestion , air pollution etc . So does the city authority own the data?
My personal take is , if someone in the MGD value chain is making the data usable for a larger good , and in the process may monetize the data to cover the investments , that entity deserves to be the owner of the data as that is where value is generated .