As the year is inching toward 2017, there have been a number of big data trends realized, particularly as the enterprise continues to recognize the importance of data in gaining customer and service intelligence. Let’s take a quick look into what some of these trends have been.
NoSQL to the Rescue
Do you have yourself some unstructured data from emails, text documents (Word docs, PDFs, etc.), social media posts, videos, audio files, and images? As we mentioned before, 80% of data is dark. This unstructured data needs to find a way to be processed, which is exactly where NoSQL databases come to the rescue. NoSQL is a database mechanism that was developed for storage, access, and analyzing of large volumes of unstructured data (i.e. dark data). As the importance to tap into dark data increases, the enterprise needs to adopt database technologies that will be able to precisely and accurately manage such large volumes of data. Forrester stated that 41% of respondents in their latest forecast have implemented and expanded the use of NoSQL and another 20% plan to implement in the next 12 months. The data is directly related to the increase in popularity of NoSQL vendors like MongoDB, Cassandra, Neo4j, and Amazon Web Services.
The problem that many enterprises are running into with adoption of NoSQL is a lack in understanding the benefits it can provide over relational databases. Additionally, some organizations find the adoption difficult because of a nonexistent infrastructure system to support it.
Did You Say Hadoop?
Open source applications like Apache Hadoop and Spark are dominating big data. They also encompass a growing interest in non-relational databases. According to Forrester, 30% of respondents implemented Hadoop in 2016 versus only 26% in 2015, and the unstructured data stored in the cloud using Hadoop increased from 29% in 2015 to 35.4% in 2016. Much of this increase is due in part to new applications and tools that enable faster data exploration and capabilities in data preparation for the end user.
Here Comes the Learning Machine?
We’re not thinking about the machines that terrorize humans here, rather we mean machine learning and artificial intelligence. Going alongside the push to tap into the secrets of dark data, there is a need for applications to generate real-time predictions, recognize patterns, and learn new things from the processing of data. Imagine having an application that digs through customer service requests and responds back to individuals based on what it has learned from emails, knowledge base articles, or any other source of data. According to Allied Research, the global cognitive computing market is expected to generate revenue of $13.7 billion by 2020.