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    Data Management

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    Tim Kannegieter

    Introduction

    Data management is a generic field of Information Technology that supports the Internet of Things (IoT) and underpins data analytics. The purpose of this page is to introduce the principles of data management and show how it intersects with the IoT.

    Key data management processes

    Data management is the development and execution of architectures, policies, practices, and procedures in order to manage the information life cycle needs of an organisation in an effective manner. The development and execution of the architectures, policies, practices, and procedures needed to manage information will fall into one of the phases or stages of the information value chain shown in the diagram below.

    DM1.thumb.png.e6f2edf201957a42b21582ea2000f416.png

    Diagram courtesy of Arthur Baoustanos, aib Consulting Services

    The stages are to acquire the data, store, then analyse it to present the data to the user in a meaningful way that adds value. The data can be acquired in a number of ways, including: a simple sensor (temperature, load cell or part of a scatter system); creating a bar code; reading an RFID tag; or through vision systems. The data is stored for aggregation and processing in a data warehouse, enterprise resource planning system, or the cloud.

    Data is then analysed, using methods from simple spreadsheet analysis, to OLAP, to sophisticated methods including data mining and machine learning. The data needs to be presented in a form useful to users, whether that be a static report, or interactive reporting, in the case of OLAP.

    Informed decision-making requires data. Data is good if it provides insight into a process in a timely manner. In this respect, the relevant data is what counts. The role of the IoT in data management is shown in the IoT technology stack diagram below.

    DM2.thumb.png.fe94e05088f307b1c6aafdd8b227acac.png

    Diagram courtesy of Arthur Baoustanos, aib Consulting Services

    The stack starts with machines, or physical assets, which are equipped with sensors, actuators or a CPU. The next part of the stack is the communications networks and technologies that connect the machines to the internet. Once the data from the machines is stored on the internet, it provides a platform to enable rapid and efficient data analytics including data management, algorithm creation and data moulds. The final component of the stack consists of applications that run on real time data. The lower cost of communication networks, such as low power, wide area networks (LP LANs, eg. Lora, Sigfox and Zigbee) and advanced data storage is helping the IoT play a pivotal role in data management.

    The relationship between Operational Technology (OT) and Information Technology (IT) is also shown in the  diagram below.

    DM4.thumb.png.7286e447a0ee4b1dacab40d2f390c88c.png

    Diagram curtesy of Arthur Baoustanos, aib Consulting Services

    OT includes SCADA systems, distributor control systems, PLCs and factory floor and plant environment sensors. IT includes the items on the right hand side of the diagram above. Value is derived by ensuring that data flows from the factory floor or plant all the way to the enterprise and business systems. 

    Sources: The information on this page has been sourced primarily from the following:

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