Storing Data with Big Data Analytics

A few days ago, I was preparing a presentation for some Data Science students. The main objective of this presentation was to generate excitement and confirm that they had made the right decision in choosing their career. While doing this preparation, I became excited about the current and future benefits that data has. Especially considering that these benefits are not just commercial but also have the real possibility of having a social impact. Not only being able to target and serve customers better but also being able to predict peoples’ needs from a healthcare, social benefit, and many other areas.

The reality is that data is everywhere and being generated on a massive scale. The following are only a few of many statistics on data at the moment:

  • By 2020 there will be 40 zettabytes of Data. This is approximately 1440 million years of HD video, click here for more information
  • Internet of Things will grow to a $1.4 trillion industry, according to the North Virginia Technology Council
  • Mushroom Networks stated that 92% of the world’s data was created in the last two years
  • According to Mushroom Networks, over 120 million people in the US own smartphones with data being stored in the cloud

The question is, how do you derive value from the data that you or others are collecting? If you do nothing about it then all it means is that the data is being archived into bits and bytes and taking up storage. The important consideration is to put the data into something that will allow you to actually do something with it.

With the advent of the massive investment in cloud by the vendors, for example, Microsoft and Microsoft Azure, the capability to store data in a place where you can process it has become real.

The capabilities that make this possible is the ability to:

  • Ingest data using IoT hubs and other ingestion mechanisms
  • Create a Data Lake, which is a storage and processing mechanism for large data sets that exist in many different formats
  • Perform compute-intensive tasks on the data because of the scalability of computing capacity
  • Push data into a variety of structure data platforms such as SQL Server or Parallel Data Warehouse to name a few
  • Perform machine learning on the data for predictive categorisation and other requirements
  • Visualise and interact with data using dashboard and other data interrogation tools

Are you concerned that your current data environment will not handle future data requirements? Contact us to chat about how we can provide you with a safe and seamless move of your existing data into a secure cloud environment.

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