The year 2018 may be drawing to a close, but new data analytics trends are still coming to the fore.
Of course, this is not a new development. These days, data analytics are an inseparable part of IT. If you want to gain insight into systems performance and customer behavior, you need to stay on top of the latest trends.
Now, this is easier said than done. Data analytics technologies are expanding at a rapid pace, and it can be hard to keep track of them. Should you focus on big data, deep learning, data science, or something else?
After talking with many renowned data analysts, we’ve come up with the following list. Are you interested in learning data analytics to gain a competitive advantage? If so, you should start here.
1. Cloud Storage and Analysis
By now, you should know why cloud storage is the next big thing. As it turns out, the cloud is also a perfect fit for your data analysis efforts.
Why is that? Simple: it takes a long time for data to move across a local network and over the Internet. If you don’t keep your data in the cloud, your analysis efforts are likely to come with large delays.
As your company is growing, even big data centers won’t be able to house all the data. If your data is in the cloud, you should move your analysis there as well.
In the long run, you should aim to move your business from CapEx to OpEx. This includes implementing new projects in the cloud and migrating the existing ones. If you’re interested in the future of cloud services, you can read more here.
First of all, what is TensorFlow? Simply put, it’s Google’s extensive open source machine learning library.
In other words, TensorFlow is a key element of most of Google’s machine learning services. Are you running a Google app on your smartphone? Well, chances are it’s using a neural network based on TensorFlow.
Once you overcome the barrier of learning the framework, TensorFlow becomes a great data resource. It’s flexible, portable, and it can connect research and production. Maximizing performance is a nice bonus.
3. Mobile Dashboards
In this mobile-first world, it’s no surprise that managers are seldom at their desks.
That’s why today’s management tools must include the mobile dashboard feature. Indeed, most self-service BI (business intelligence) tools already have it. That said, not all business metrics go through BI tools.
For example, let’s take a medium-sized manufacturing plant. A facility of this size is likely to track its production lines through a QA system. If a line drifts out of tolerance, plant managers must know about it immediately.
This is where an app that can query the QA database comes in handy. Many of these apps can also install updates and display a Shewhart control chart. Some of them can even sound an alarm in case a line goes out of bounds.
Much like TensorFlow, MXNet is a deep learning framework. Despite this natural similarity, the two frameworks couldn’t be more different.
For starters, MXNet doesn’t have TensorFlow’s visual debugging feature. What it does offer, however, is an imperative language for tensor calculations.
Why is imperative programming so important? Well, it allows the MXNet platform to parallelize imperative and symbolic operations automatically. Thanks to a graph optimization layer, the whole process is fast and memory-efficient.
5. Jupyter Notebook
Haven’t heard of the Jupyter Notebook? Don’t be hard on yourself. Until recently, this open source web application was the iPython Notebook.
This tool helps data scientists create documents containing equations, live code, and visualizations. This is very helpful with data transformation, machine learning, statistical modeling, and more.
In recent years, the Jupyter Notebook has risen in popularity. It’s now a standard component of many online services that include machine learning, such as Azure and Databricks. Of course, you can also run the application locally.
6. Deep Neural Networks
Deep neural networks are among the most powerful deep learning algorithms out there.
What are deep neural networks? Well, they’re neural networks consisting of linear and nonlinear processing units. Training a deep neural network requires huge amounts of large-scale algorithms.
Now, every deep neural network is constructed from many hidden layers. An average neural network has a few layers, but deep neural networks may have 10-20 of them. The more layers a network has, the more aspects it recognizes.
The main downside of deep neural networks is that they take longer to calculate. Still, the packages for creating them are a common sight in many frameworks. Some of the more common examples include Caffe, Neon, Torch, and Theano.
7. The R Language
The R programming language is an excellent method of analyzing data through statistics.
There are many reasons for this. For one, R scripts can be re-run and audited in an effortless manner. This feature alone makes it much easier to create reproducible analysis.
Additionally, the R language provides a variety of statistical techniques. In fact, the vast majority of these techniques are already implemented in R packages. R is not a perfect fit for machine learning yet, but the support is getting there.
Finally, R is a completely free, open source language. Many commercial products (such as SQL Server 2016) are already embedding it. We don’t see this changing anytime soon.
More on Data Analytics Trends
Hopefully, this list has helped you catch up on data analytics trends. That said, this is only the tip of the iceberg.
See, the technology world is in constant flux. This time last year, few experts were predicting that the IoT would be entering a downward spiral. The same could happen with any of the above trends, so keep up with them as much as you can.
Interested in more technology news? Want to know more about the difference between data analytics and data analysis? You may want to take a look at our blog!