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Found 7 results

  1. Introduction: 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. 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 (using technologies including augmented reality), 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 with 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. Once an organisation has decided to optimise their efficiency using data analytics, they should look at long as well as short term goals, and set specific efficiency or process change targets in order to get the most out of their investment as shown in the diagram below. Diagram courtesy of Umesh Bhutoria, EnergyTech Ventures A gap analysis of people skills (users, engineers, managers), data (points and frequency) and investment needed to reach goals should also be carried out, to ensure that all stakeholders are willing to see the process out through trials to implementation. When approaching vendors, care should be taken not to over- or under- specify requirements. For more information, visit the Project Management for IoT page. It may also be beneficial to invite shortlisted vendors to site to conduct data discovery tasks or solve smaller problems that will help determine if they will be a good fit to help the organisation implement a large-scale data analytic solution. Challenges Industry uses a small fraction of available data due to siloed data in legacy systems and leaders’ scepticism about the impact of technologies such as IoT. For example, added value for a commercial building could be derived from integration of available data into building management systems and building intelligence systems to perform energy analytics and management to improve efficiency, or condition monitoring and predictive maintenance. Three factors which contribute to the slow uptake of IoT data analytics in industry are: multiple data points (including electrical, thermal and mechanical energy, as well as process and production data) which may be housed in separate servers proprietary or inflexible data collection and storage solutions which are difficult to integrate skills gaps in staff and management in understanding the benefits of data analytics Types of solutions There are several different models of IoT data analytics solutions as shown in the diagram below. Diagram courtesy of Umesh Bhutoria, EnergyTech Ventures A stand-alone system could involve purchasing metres or sensors and asking a vendor to integrate them. This model has the potential to be influenced by the vendor rather than the user organisation’s requirement and does not provide integration with existing data. The second model, data as a service, provides monitoring and automated reports, but will not necessarily include integration with legacy data. Insights as a service is a model that is gaining in popularity, and is applicable to organisations with mature data infrastructure, who know what data is available and how the organisation aims to use it. It is typically a cloud-based service that uses company, user and third-party data to provide insights, as well as offering support in using these insights to meet the goals of the organisation. Existing data is also connected and centralised, as shown in the diagram below. Diagram courtesy of Umesh Bhutoria, EnergyTech Ventures The choice of solution should be based on the benefits it will bring to the organisation, weighed against the pre- and post- purchase effort, cost and ease and flexibility of use. 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 and energy analytics Downtime minimisation Network performance management Device performance effectiveness Load balancing optimisation Loss prevention Capacity planning Asset management and inventory tracking Demand forecasting Pricing optimisation Disaster planning and recovery Yield management Sources: The information on this page has been sourced primarily from the following: Webinar titled The data management perspective on IoT by Arthur Baoustanos, Managing Director, aib Consulting Services Case Study titled Studying movement behaviour in a building: A case study of obtaining analytics from IoT Data Webinar titled “The Data Indigestion Crisis: New approaches to Energy Analytics” by Umesh Bhtoria, Founder and CEO, EnergyTech Ventures
  2. One of my colleagues, Hugh McCann, recently presented our company with an update on his data analytics work. He kicked off with a summary of his home energy usage. I knew he'd been tracking this, and I believe we talked on the day he accidentally deleted 3 months of data! Yep, he's a nerd's nerd and hadn't thought to protect the data because it was just a home project, so you can imagine how he felt. He's just published a more detailed description of how and why he did it here. What immediately strikes me is that what he's done isn't particularly complex and it appears to me that it could be commoditised / built into new constructions. This ties in with some other discussions on smart metering but gives some insight into what can be done across a broader range of domestic power usage, and a way to engage the home owner. It seems an obvious extension to add a degree of machine learning and mass energy usage collection for a whole variety of purposes.
  3. One of my colleagues, Hugh McCann, recently presented our company with an update on his data analytics work. He kicked off with a summary of his home energy usage. I knew he'd been tracking this, and I believe we talked on the day he accidentally deleted 3 months of data! Yep, he's a nerd's nerd and hadn't thought to protect the data because it was just a home project, so you can imagine how he felt. He's just published a more detailed description of how and why he did it here. What immediately strikes me is that what he's done isn't particularly complex and it appears to me that it could be commoditised / built into new constructions. This ties in with some other discussions on smart metering but gives some insight into what can be done across a broader range of domestic power usage, and a way to engage the home owner. It seems an obvious extension to add a degree of machine learning and mass energy usage collection for a whole variety of purposes.
  4. Introduction IoT applications discover and store huge volumes of data from multiple sources, and process it using various forms of data analytics. The results of this analysis need to be presented in a way that is useful to end users and aid in their decision making. Presenting data in visual forms, such as charts and graphs, enables users to understand what is happening at a glance and conceptualise what further investigations are required to understand a complex phenomenon (such as building vibrations). Interactive analytics are a set of tools that allow data analysts to investigate the data and create visualisations that present easy to read results and figures that relate directly to users' requirements. They also provide the ability to share these results with others. The main vendors of interactive analytics tools include: Power BI from Microsoft and Tableau, IBM, SAP Oracle, SAS, MicroStrategy and QlikTech. Technologies such as augmented reality can also be used to project IoT data visualisations onto devices or objects in the field. Sources: The information on this page has been sourced primarily from the following: Case Study titled Studying movement behaviour in a building: A case study of obtaining analytics from IoT Data
  5. Introduction Data for IoT applications often comes from many heterogenous sources, which may not be easily brought together for analysis. Data integration is one approach that is used in IoT solutions to allow disparate data to be used to provide useful decision-making support. It is a tool that is often used in data warehousing. Data integration makes use of Extract, Transform, Load (ETL) tools. There are also other new tools including Enterprise Feedback Management (EFM). These are the tools that are utilised to bring the data from one point to the other. ETL vendors are listed in the diagram below. Diagram courtesy of Jorge Lizama, GHD Sources: The content in this page was primarily sourced from Case Study titled Studying movement behaviour in a building: A case study of obtaining analytics from IoT Data Further reading: Data integration info website
  6. Dicker Data has launched an enterprise data business unit and signed a distribution deal with Hitachi Data Systems, focusing on providing new products for IoT and digital transformation solutions in the midmarket and enterprise sectors. More on https://www.arnnet.com.au/article/621862/dicker-data-launches-enterprise-data-business-unit-digital-transformation-drive/
  7. McKinsey has released a report called The age of analytics: Competing in a data-driven world, in which it suggests that the influence of big data is just going to grow.
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