Top Data Analytics Trends in 2018

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Data analytics has become a hot topic in recent years, quickly becoming a staple in many organizations. Thanks to advancements in big data, data science, deep learning and machine learning, the ability to analyze stunning quantities of information has altered the course of business as we know it.

However, even if you’ve embraced data analytics, failing to keep up with trends can lead you to fall behind. Here are a few of the top data analytics learnings that should be within your purview:

Self-Service Business Intelligence

With the rise of self-service BI tools like Power BI, Qlik Sense and Tableau, companies can view pertinent business information on demand. This includes graphical formats, thanks to data visualization capabilities and the ability for previously created analyses to update automatically.

While IT can play a significant role in setting up these systems, once the data sources are added most of the related tasks, like completing analyses, can be managed by business analysts. The managers can then use the results to make smart business decisions quicker than ever before.

Mobile Dashboards

Let’s face facts; we are living in a mobile world. Many professionals spend less time at their desks. They are heading from one meeting to the next or trying to complete their tasks while they are on the road. Mobile-friendly dashboards have become a tool by which managers don’t have to log in to their primary computer to access critical data, freeing them to handle their objectives regardless of their location.

R Programming Language

It isn’t uncommon for programming languages to fall in and out of favor. When it comes to data analytics, R is currently leading the way. It is considered both powerful and convenient, as the scripts can readily be audited and re-run whenever the need arises. It also plays a strong role in machine learning while providing access to a broad selection of statistical techniques.

Deep Neural Networks

Deep neural networks, or DNN’s, are neural networks comprised of multiple layers that alternate between linear and nonlinear processing units. The systems are trained using a substantial amount of training data, as well as through the use of large-scale algorithms. The depth of the network allows it to recognize more characteristics, though the training and calculation time can be a challenge. Additionally, locating data scientists with experience in deep neural networks can be difficult, as the specialty is still somewhat new. However, many packages designed for creating deep neural networks are gaining prominence, making the technology more accessible.

If you would like to learn more about how the power of data analytics can further your business objectives or are seeking a skilled data scientist to join your team, the professionals at Solving IT can help. Contact us to speak with one of our knowledgeable team members today.