Article | March 31, 2020
DataOps helps reduce the time data scientists spend preparing data for use in applications. Such tasks consume roughly 80% of their time now.We’re still hopeful that the digital transformation will provide the insights businesses need from big data. As a data scientist, you’re probably aware of the growing pressure from companies to extract meaningful insights from data and find the stories needed for impact.No matter how in-demand data science is in the employment numbers, equal pressure is rising for data scientists to deliver business value and no wonder. We’re approaching the age where data science and AI draw a line in the sand for which companies remain competitive and which ones collapse.One answer to this pressure is the rise of DataOps. Let’s take a look at what it is and how it could provide a path for data scientists to give businesses what they’ve been after.
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.
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 | April 3, 2020
Primarily,the IoT stack is going beyond merely ingesting data to data analytics and management, with a focus on real-time analysis and autonomous AI capacities. Enterprises are finding more advanced ways to apply IoT for better and more profitable outcomes. IoT platforms have evolved to use standard open-source protocols and components. Now enterprises are primarily focusing on resolving business problems such as predictive maintenance or usage of smart devices to streamline business operations.Platforms focus on similar things, but early attempts at the creation of highly discrete solutions around specific use cases in place of broad platforms, have been successful. That means more vendors offer more choices for customers, to broaden the chances for success. Clearly, IoT platforms actually sit at the heart of value creation in the IoT.