Data Analytics can be very intimidating but are very important in planning and designing your website.
What makes data analytics so hard today is the amount of data you get from different sources, and trying to figure out what is important for your business, and what is just a lot of noise.
A wide range of software, technologies, and strategies are available to address these issues of “big” data, as well as unstructured data, and they are rapidly evolving. In order to maximize your investment in data analytics, you need to be aware of the trends to choose the right ones for you.
Here are the seven top analytics trends of 2018.
Batch analytics fall in disfavor
In a fast moving world where timely information is power, the days of batch data processing are dwindling. Back in the day, data applications could only gather information, and would have to wait until everybody had gone home to process it. It may have been fine back then, but it resulted in delays that are no longer acceptable today.
People want the big data crunched and actionable data available now so they can make decisions. The pressure is on data scientists to justify the investment in data analytics technology by delivering results that will create tangible value for businesses.
Artificial intelligence is back in the mainstream
Bigger, faster, and deeper is the name of the game in data analytics. The sheer volume of data streaming into a typical business is often too much for humans to process and analyze efficiently, so it falls on intelligent machines to do the drudgework. Anything before that is just a costly drain on human resources and time because it takes longer for humans to do them, and they tend to make mistakes with repetitive tasks.
Artificial intelligence (AI) can handle these tasks often associated with large data sets, so its value is in the consistency in producing results. Human intuition and intelligence will kick in at the most crucial step when the results are on hand for decision-making.
Machines learn in real time
You may be imagining that artificial intelligence is a bad idea based on many movie plots of machines taking over the world. However, the continuing fascination for the concept of the Internet of Things and seamless connectivity among devices makes machine-learning algorithms the best way to collate and analyze data as it comes into being. This will not be limited to enterprise-level data either, as micro-services produce a ton of streaming data. Much of this is unstructured data, which can provide important insights into the context of its creation.
In fact, the design of some data applications brings it a step back by anticipating the future with preemptive data analytics. Businesses can choose to become proactive by making decisions based on what historical data predicts will happen before an event, creating a value-added business model for identifying efficiencies, income-generating opportunities, and customer service improvements.
Unstructured data takes center stage
A large percentage of all information is unstructured, meaning they are not easily categorized in neat columns or categories. These include things such as videos, social media, text, RSS feeds, and slide presentations. You can tell at once that they contain important information that can help you understand your business and customers better. However, because the data they contain is not organized in a way that a machine can understand, they are hard and expensive to analyze.
Fortunately, advances in machine learning and similar technology make it possible to compile and analyze unstructured data in a cost-effective way today. If you want to understand the reasons behind the buying behavior of your target market, you need to invest in technology that handles unstructured data more like a human than a machine.
The dark data rises…to the cloud
In line with the renewed interest in unstructured data, many data scientists are looking at “dark” data often stored in the cloud. Dark data include log files and other data that have yet to be processed and analyzed. Dark data is a type of unstructured data that are often relegated to offsite storage when there is not enough room in a company’s data center. This leads to systematic neglect because offsite data are harder to access, aside from being difficult to analyze in the first place. The efficient solution to this is to implement data analytic software on the cloud, where dark data and other unstructured data may be waiting to spill all they know about your business and customers.
One of the more powerful and popular method for cloud data analysis is with the use of the R language to plot, manipulate, and statistically analyze data. It is an open source programming language for creating high quality, reproducible data analysis. It integrates in many commercial machine-learning products such as SQL Server 2016 and Microsoft Azure Machine Learning Studio. However, it has its limitations, as it is not powerful enough to use for deep neural networks.
Neural networks let you go beyond the obvious
All these trends to analyze unstructured and semi-structured data preemptively and in real time require a type of data analytics that can handle non-linear and layered information.
Neural networks in data analytics use a complex algorithm that mimics the way the human brain processes information, although to a very basic degree. The main idea is to do analyses that go beyond the obvious, much like the way humans intuitively know when something is wrong with a picture, or when something “feels” right. This is not some sort of magic or extra perception as some people believe, but a subconscious acknowledgement of data or information received that was processed and categorized without conscious effort or thought. Neural network algorithms do not work with that degree of richness, but well enough for business needs.
The typical neural network algorithm has three layers, the most obvious of which is the input layer, or data as it appears to the user. The value of neural networks is in the hidden layer, which often contains mathematical functions or neurons hidden from the user. These neurons often have a significant effect on future outcomes, which is the third layer.
Deep dive for better understanding
Machine learning and neural network algorithms are the basic concepts driving data analytics today. However, they often have to be made to dive deeper into the data for a better understanding of it. Whereas a standard neural network has a few layers, a deep neural network goes down the hidden layers as far as it can go.
It may alternate up to 20 layers of nonlinear and linear processing units, recognizing more patterns and connections as it goes. It takes more time to learn the framework to collect and analyze the data, but you have much more robust predictions. You can create deep neural networks using several packages, the most popular of which are TensorFlow and MXNet. Other packages include Microsoft Cognitive Toolkit, Theano, Caffe, and Torch.
Data analytics has been in the works ever since the first store owner realized that putting candy on the counter generated extra income as it prompted little kids to nag their parents about buying them a little something. It has become a lot more complicated than that in the last two decades or so, and it has been speeding up in the last few years.
The interesting thing is that the seven top analytics trends of 2017 demonstrate an increased emphasis on creating machines that think along the same lines as humans do. With big data getting bigger every day, these technologies will only grow in complexity to meet increased demands for more computing power. How closely these machines will mimic human thinking remains to be seen.