Article | February 23, 2020
Does the success of companies like Google depend on that of the algorithms or that of data? Today’s fascination with artificial intelligence (AI) reflects both our appetite for data and our excitement about the new opportunities in machine learning. Amalio Telenti, Chief Data Scientist and Head of Computational Biology at Vir Biotechnology Inc. argue that newcomers to the field of data science are blinded by the shiny object of magical algorithms and that they forget the critical infrastructures that are needed to create and to manage data in the first place.Data management and infrastructures are the little ugly duckling of data science but they are necessary for a successful program and therefore need to be built with purpose. This requires careful consideration of strategies for data capture, storage of raw and processed data and instruments for retrieval. Beyond the virtues of analysis, there are also the benefits of facilitated retrieval. While there are many solutions for visualization of corporate or industrial data, there is still a need for flexible retrieval tools in the form of search engines that query the diverse sources and forms of data and information that are generated at a given company or institution.
Article | February 10, 2020
We are a species invested in predicting the future as if our lives depended on it. Indeed, good predictions of where wolves might lurk were once a matter of survival. Even as civilization made us physically safer, prediction has remained a mainstay of culture, from the haruspices of ancient Rome inspecting animal entrails to business analysts dissecting a wealth of transactions to foretell future sales. With these caveats in mind, I predict that in 2020 (and the decade ahead) we will struggle if we unquestioningly adopt artificial intelligence (AI) in predictive analytics, founded on an unjustified overconfidence in the almost mythical power of AI's mathematical foundations. This is another form of the disease of technochauvinism I discussed in a previous article.
Article | April 7, 2020
According to software vendors executing the big data projects, the answer is clear: More data means more options. Then add a bit of machine learning (ML) for good measure to get told what to do, and the revenue will thrive.This is not really feasible. Therefore, before starting a big data project, a checklist might come in handy.Make sure that the insights gained through machine learning are actionable. Gaining insights is always good, but it is even better if you can act on this new knowledge.A shopping basket analysis shows which products are sold together. What to do with that information?Companies could place the two products in opposite corners of the shop, so customers walk through all areas and will find other products to buy in addition. Or they could place both products next to each other so each boosts the sales of the other. Or how about discounting one product to gain more customers?As all actions have unknown side effects, companies have to decide for themselves which action makes sense to take in their case.
Article | January 21, 2021
The Internet of Things has been the hype in the past few years. It is set to play an important role in industries. Not only businesses but also consumers attempt to follow developments that come with the connected devices. Smart meters, sensors, and manufacturing equipment all can remodel the working system of companies.
Based on the Statista reports, the IoT market value of 248 billion US dollars in 2020 is expected to reach a worth of 1.6 Trillion USD by 2025. The global market is in the support of IoT development and its power to bring economic growth. But, the success of IoT without the integration of data analytics is impossible. This major growth component of IoT is the blend of IoT and Big Data - together known as IoT Data Analytics.
Understanding IoT Data Analytics
IoT Data Analytics is the analysis of large volumes of data that has been gathered from connected devices. As IoT devices generate a lot of data even in the shortest period, it becomes complex to analyze the enormous data volumes. Besides, the IoT data is quite similar to big data but has a major difference in their size and number of sources. To overcome the difficulty in IoT data integration, IoT data analytics is the best solution. With this combination, the process of data analysis becomes cost-effective, easier, and rapid.
Why Data Analytics and IoT Will Be Indispensable?
Data analytics is an important part of the success of IoT investments or applications. IoT along with Data analytics will allow businesses to make efficient use of datasets. How?
Let’s get into it!
Using data analytics in IoT investments businesses will become able to gain insight into customer behavior. It will lead to the crafting offers and services accordingly. As a result, companies will see a hike in their profits and revenue.
The vast amount of data sets that are being used by IoT applications needs to be organized and analyzed to obtain patterns. It can easily be achieved by using IoT analytics software.
In an era full of IoT devices and applications, the competition has also increased. You can gain a competitive advantage by hire developers that can help with the IoT analytics implementations. It will assist businesses in providing better services and stand out from the competition.
Now the next question arises: Where is it being implemented? Companies like Amazon, Microsoft, Siemens, VMware, and Huawei are using IoT data analytics for product usage analysis, sensor data analysis, camera data analysis, improved equipment maintenance, and optimizing operations.
The Rise of IoT Data Analytics
With the help of IoT Data Analytics, companies are ready to achieve more information that can be used to improve their overall performance and revenue. Although it has not reached every corner of the market yet, it is still being used for making the workplace more efficient and safe.
The ability to analyze and predict data in real-time is definitely a game-changer for companies that need all of their equipment to work efficiently all the time. It is continuously growing to provide insights that were never possible before.