Fuel The Machine: Big Data And Automation Initiatives

Big data is now, thanks to automation, readily available for almost any sized business. Making data directly available to analysts one of the cornerstones of this movement, but a lot has to happen to the data before it even gets to that point.

The primary processes that relate to automation are preparation, analytics, and integration. But how does all this data automation work exactly?

Big data is actually the fuel for the internet of things, but much of it is time sensitive and must be processed quickly to be useful. That is the key. It’s not just data that matters, but usable data. This is why simple access to raw data is seldom helpful.

Using the Cloud 

There are many analytics options in the cloud that take data from this raw state to something truly informative. This ranges from traditional historic business intelligence to more advanced prescriptive cognitive computing analytics. To use these services, organizations must allow providers access to their data.

This decreases physical infrastructure, something often out of reach for smaller businesses. It also allows them to outsource potentially resource consuming processes, also often something they cannot afford.

The cloud also offers the advantage of machine learning algorithms, which can solve complex problems, perform analytics, sort data, and even automate the data modeling process, another labor-intensive effort. This can help make that time sensitive data both accessible and usable in a short period of time.

The true advantage of using big data is the combination of data sources. If a digital marketing agency were using just social signal data to analyze potential prospects for a business, they might spot some trends. But if they combine this with customer profiles and purchase data, they have a much richer picture, and creating personas is a much simpler process.

Data Structure 

One of the most common structures for big data is the “data lake.” This is just all of the data in one place. This makes it easy for analysts to find the data once they know what they are looking for. One issue with this structure was that often data scientists were spending a tremendous amount of time on data preparation, when their talents could have been much better used in doing analytics following that preparation.

This is again where automation and other structures come into play. Machine learning allows data to be prepared and potential connections analyzed early on. These advances still require that the data be housed in a “data lake” type location, but instead of data scientists, intelligent computing is doing much of the preparation work.

The other way to differentiate data structures in the data lake is the use of JavaScript Object Notation (JSON) based document stores like Hadoop. Especially in industry when it comes to sensor data, this is the language that speaks between applications. It is the most common data format in the Internet of Things.

Combined with some SQL solutions, there is essentially no need to depend on IT personnel to massage the data before you access it. These automation processes, done in the cloud, make the data usable in real time, one of the largest needs for industrial data.


This automation also allows you to control security, specifically through cloud automation initiatives Essentially you need to be able to control four things, and the cloud lets you do just that.

  • Authenticate: You need to know who is coming and going, and who is using what data for what purpose. This is the first key to keeping data secure. From password protection to multi-step authentication, data can be restricted to use by only authorized personnel.
  • Control Access: This is the reason for authentication. Access should be limited to personnel who actually need the data not only for security reasons, but for simple logistical ones as well.

  • Audit: You should be able to understand and analyze who did what, and why. That way you know exactly how the data is being used. This not only helps with security, but helps you structure it properly in future JSON and SQL structures, and helps you make connections that might otherwise be missed.
  • Architecture: How is your security structured? Is it just at the authentication or access level, or is it granular, at the data level? This depends largely on how sensitive the data is, but it should be evaluated.

Data is now available to many people in an organization, but securing it properly can make sure that proprietary information and customer data is protected the way it should be at every level.

Data science was once an obscure and mysterious occupation, an art practiced largely in secret behind closed doors. Numerous parts of the process have now been automated, enabling business users who do not understand the ins and outs of analytics and algorithms to access data and use it.

This data can also be secured just as it would on an enterprise level, but by small businesses as well. This makes big data and automation initiatives much more affordable and accessible. This means the fuel for the Internet of Things is readily available in a usable form. Everyone wins as a result of that.

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