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  • Data Analytics

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    Tim Kannegieter


    Data Analytics has traditionally been associated with the processes involved in using data to inform decision making. It builds on the underpinning principles of data management that are required to build any kind of IT system, including the integration of IoT operational and back-end business systems.

    In the context of IoT, Data analytics encompasses many approaches including big data, in-memory computing, cloud computing, NoSQL databases, data integration, and interactive analytics, as shown in the diagram below.

    IoT data analytics approaches

    Diagram courtesy of Jorge Lizama. GHD

    Historically, data analytics took the form of Decision / Executive Support Systems starting in the 1970s, then evolving into Online Analytical Processing (OLAP), Business Intelligence (BI) in the 1990s.  

    It is common to think of data analytics in terms of the volume, velocity, and variety of the data. Volume refers to the quantity of data, velocity to the speed at which the data is generated, and variety to the different types of data. Over the past few years, two new Vs, value and veracity have been introduced. Veracity refers to the quality of the data, and value refers to the benefit that the organisations can gain from the volume and variety of data that is being delivered with great velocity, if they are able to depend on its veracity.


    Diagram courtesy of Arthur Baoustanos, aib Consulting Services

    The current approach to managing data collected from IoT devices is to sense/observe the data, move it into the cloud, process and analyse it there, visualise it for decision making purposes, then either store or discard it partially/completely.

    In recent times the exponential growth of data has created situations where "traditional" analytical methods are not viable and the term big data analytics is being used to describe new analytical techniques developed to cope with these situations. Big data analytics is often associated IoT because many IoT applications involve large numbers of sensors generating large volumes of data. Also, many IoT applications involve the integration of a large variety of data formats such as weather data, machine vision and the like. 

    A key challenge of IoT systems that generate or integrate a lot of data is how to make sense of it and how best to make use of it. This is driving the uptake of cognitive computing systems that assist analysts in determining insights and drive outcomes not possible with traditional analysis.

    Planning for data analytics

    The critical questions that organisations will need to answer when embarking on the journey to advanced data analytics are:

    • Where does the organisation want to go (goals)?
    • How will we get there?
    • What do we need to get there?
    • Will our current structure allows us to get there?
    • What changes do I need to make to get us there?

    It is important to start with the business objective: define critical business issues and decide where value will be derived. Then evaluate which data is required to assess the identified issues and determine any gaps in relevant data. Be as specific as possible about what decisions the company will make based on that information. Departments and divisions within the organisation should collaborate to understand exactly what information is required to address common business goals. Data could also be purchased from outside sources to complement internal data collection.

    The role of data analytics in IoT

    A non-exhaustive list of advanced data analytic applications within IOT applications is listed below. The majority of the applications listed revolve around the broad categories of asset management, planning, and performance management. The IOT has helped businesses to address these applications in a more holistic manner than was previously possible.

    • Predictive maintenance
    • Energy usage optimisation
    • Downtime minimisation
    • Network performance management
    • Device performance effectiveness
    • Load balancing optimisation
    • Loss prevention
    • Capacity planning
    • Asset management
    • Demand forecasting
    • Inventory tracking
    • Pricing optimisation
    • Disaster planning and recovery
    • Yield management

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










    Edited by Peter Harvie

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    Big data is often confused with lots of data. This topic raises the issue of how to handle the growing volumes of data, and the 'Flood of data' knowledge note shows just how large this can be, but it there is further discussion that can be had on the differences between handling high volume data as a technical issue, and conducting data analytics of a big data set.

    The link can also be drawn to IOT design whereby the purpose for sensing and observing a particular data event should be a deliberate outcome of the design process to inform a decision. I see reports of data stores being overwhelmed by the volume of data collection and without a plan to reduce or analyse the key elements.

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