Big Data Trends to Look Out for in 2019
Learn what challenges and opportunities lie ahead for the field of big data in the coming months.
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Data is what moves forward digital innovation in innumerable and diverse areas, and it is small wonder that advancements and developments in big data are among the most influential in business and beyond. Organizations that are the first to find solutions to the most important data challenges gain significant advantages over their competition. In this article, we will take a look at some of the most prominent trends in big data that are worth watching this year.
1. Data Management Remains Challenging
The principle behind big data analytics has always been and remains rather straightforward: you collect a lot of data, find meaningful patterns in it, train machine learning algorithms to notice them, and create models that automatically detect such patterns. However, the practical implementation of this approach remains problematic. You have to use data coming from a number of silos, clean it up, and label it for the purposes of machine learning and implement such a system so that it works securely and stably. There are still no easy solutions to all these problems, which means that experienced data engineers are still going to be among the most high-demand IT specialists.
2. Augmented Analytics
So far, the most important and trustworthy analytical insights have been the result of manual work of data scientists and analysts. Human specialists study a huge quantity of data, make connections between different factors and draw conclusions based on their own experience and knowledge. They can use AI tools to organize the data available to them, but connecting the dots is still theirs to do. With the development of augmented analytics, we will be able to create systems that use AI and machine learning to make accurate predictions and provide insights pre-emptively, without human participation. In the long run, this means that insights based on big data will become more and more available. We already see particular search and analytical tools that provide valuable insights for small businesses and even individuals. For example, with the help of NoxInfluencer, YouTubers can analyze the performance of their channels, and brand owners have an easier time finding influencers to promote their products. This gives even small players who normally don’t have access to such tools the opportunity to gain smart insights that can be useful in their work.
3. Combining Streaming Analytics and IoT
Traditionally, machine learning operates in a controlled environment and uses previously stored data for the purposes of training. The idea behind combining streaming analytics and IoT is that we can achieve a much greater degree of flexibility and more relevant results by providing real-time input from the devices connected to the Internet of Things in a less controlled environment. Ideally, this will allow for a more open training system working with more complex algorithms.
4. The Growing Use of Dark Data
Dark data is the information that organizations collect and store as a part of their everyday business activities but fail to use in any other way. Usually, they gather it for compliance purposes, without intending to utilize it. Currently, in most cases, it occupies a significant portion of the storage of any company, lying fallow and not bringing its owners any advantages. However, with more and more organizations understanding the importance and potential uses of their information resources, they are going to be less likely just to allow this mother load to remain unused.
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