The Internet of Things is not living up to its hype. In 2014, as the anticipation of everything being connected was reaching its zenith, Google purchased Nest for $3.2 billion, thinking that this was going to be their foot in the door to everyone’s smart home. It turns out that people aren’t that interested in taking the time to ensure that their smart light bulb can be turned off when the homeowner is at work. The promise of Google soaking up terabytes of information from everyone’s home is still relegated to fiction (or fear mongering – depending on your perspective).
That doesn’t mean that more and more data isn’t being collected all the time. Although the smart home is still a “Jetsons” fantasy, smart cities and smart companies are a reality. Cities are releasing their data stream to the public so that clever people can analyze it and find trends and other morsels of information that are useful. Companies that have for years, generated enormous quantities of data, are finally teasing useful information out of that data.
Companies, cities, and even countries are all becoming “smart” because they are starting to use the raw data that they collect. The science that allows us to transform meaningless data into useful information is Data Analytics (“DA”). In this era of “Big Data” the Data Analyst is king (or queen?).
Companies use data analytics to make better business decisions. Scientists use it prove or disprove existing theories and models. It is astounding how often our “common sense” approach has led to the exact opposite of the correct solution being implemented. DA has shown us the folly of following our gut on numerous occasions.
Unlike Data miners who are merely searching for patterns, DA is focused on analyzing whether a hypothesis is true or not.
Microsoft Azure has a variety of tools to help you in the game of thrones where you are trying to crown yourself as the DA King or Queen for you employer. In previous weeks I have posted several blogs about Microsoft’s powerful Machine Learning tools. That is only one of many tools that will help you analyze data (big or small).
In the coming weeks, I will introduce you to the variety of tools and give you a brief overview of how they can help you.