Big data deals with large data sets which include both structured or unstructured data that traditional software is inefficient to deal with. Different businesses, organizations, and even governments use this data to make better strategic moves. Big data doesn’t rely on the amount of data you are working with but focuses on what you do with it. Big data in today’s world is changing the way organizations manage and use business information.
Major advances in big data analytics were made in the year 2016 and 2017 is expected to be much better and bigger. According to the Gartner survey in 2016, nearly 48% of organizations invested in big data and nearly 3/4th of those have already planned to invest or have invested in 2017. As big data technologies and techniques are growing rapidly in the market, big data project managers and CIOs should be aware of the emerging big data analytics trends in 2017. Here is a list of top 7 big data trends for 2017. Let us have a look.
Big Data helps fulfill the customer needs
Improving customer satisfaction is very important today. As customers are growing rapidly and leading to a tough competition, it is difficult to have the upper hand always. But big data helps you achieve it by analyzing the data such as what customers wish to purchase and what they purchased previously. This helps businesses gain the accurate and deep understanding of what their customers are looking for, which in turn helps to lead among competitors.
So many companies are using big data analytics to study and predict consumer behavior and American Express is one among them. By browsing past transactions, the firm makes use of modern predictive models instead of traditional BI-based hindsight reporting. This empowers more accurate forecast of customer loyalty. American Express, with the help of big data, predicted that 24% of accounts will be closed within the next four months in their Australian market.
Combining IoT, Big Data, and Cloud
IoT, Big Data, and Cloud are dependent on each other. Almost every IoT device includes a sensor, which collects a large amount of data (Big Data) and then delivers the data to a server for further analysis (Cloud). As people communicate with devices directly, a large volume of data will be generated by IoT. Hence, big data is required to handle such large variety of data.
Gartner stated that IoT has now officially considered big data as the most popular and hyped technology. According to a report, the smart city project by Indian government will use about 1.6 billion IoT devices by 2016 and smart commercial buildings are expected to be a giant user of IoT until 2017. These two departments will use more than 1 billion connected devices by 2018. It also predicted that by the end of the decade, tens of billions of devices will join the global network, offering a number of opportunities and concerns for policymakers, regulators, and planners. IoT technology is still in the beginning stage, but the data from the connected devices have become and are continuing to become more for the cloud. So we will see the leading data and cloud firms fetching IoT services to the real world where data can run smoothly to their cloud analytics engines.
Big Data platform built only for Hadoop will fail
Big data and Hadoop are identical to each other. Hadoop together with big data technologies analyses and delivers data at the right time to the right place. Organizations with different and complex environments are focusing on gaining a deep understanding of data analytics from both Hadoop and non-Hadoop sources varying from systems of records (SOR) to cloud warehouses, structured and unstructured. In the year 2017, big data platforms that are data and source agnostic will succeed and the ones that are developed only for Hadoop will fail to deploy across use cases. This is because Hadoop is developed only for large amounts of data and it is pointless to use Hadoop clusters for small data. Most of the businesses that have a small amount of data used Hadoop because they felt that it is mandatory to be successful. After a long period of research and working together with data scientists, they came to know that their data can perform better in other technologies.
Big Data offers high salaries
The growth of big data analytics will result in high salaries and high demand for IT professionals with high standard big data skills. Robert Half Technology predicted that the average payment for data scientists in 2017 will increase by 6.5% ranging from $116,000 to $163,500. In the same way, big data engineers are expected to see a salary hike of 5.8% ranging from $135,000 to $196,000 by next year.
“By 2015, 4.4 million IT jobs globally will be created to support big data, generating 1.9 million IT jobs in the United States,” said Peter Sondergaard, Senior Vice President at Gartner and global head of Research. “In addition, every big data-related role in the U.S. will create employment for three people outside of IT, so over the next four years a total of 6 million jobs in the U.S. will be generated by the information economy.“
Self-Service analytics analyses the data effectively
Gartner described self-service analytics as, “a form of business intelligence (BI) in which line-of-business professionals are enabled and encouraged to perform queries and generate reports on their own, with nominal IT support.”
As big data experts are demanding high salaries, many companies are looking for tools that help normal business professionals to meet their own big data analytics requirements by reducing the time and complexity, especially when dealing with different data types and formats. Self-service analytics helps businesses to analyze the data effectively without the need for big data experts. Companies such as Alteryx, Trifacta, Paxata, Lavastorm etc are already in this field. These tools are reducing the complications with Hadoop and will continue to gain popularity in 2017 and beyond.
Qubole, a startup company offers a self-service platform for big data analytics that self-optimizes, self-manages and enhances the performance automatically resulting in outstanding flexibility, agility, and TCO. It helps businesses concentrate on their data, but not the data platform.
Apache Spark strengthens Big Data
Apache Spark has now become the big data platform of choice for many companies. According to a research conducted by Syncsort, 70% of IT managers and BI analysts recommended Spark over Hadoop MapReduce, due to its real-time stream processing. Spark strengthens big data as it is more mathematical, convenient, and natural. Its big computing big data capabilities have improved the platforms promoting artificial intelligence, machine learning and graph algorithms. It doesn’t mean that Apache Spark replaces Hadoop, it just improves the big data computing capabilities of Hadoop. Companies that are using Spark and Hadoop together are gaining greater value from big data.
Hadoop MapReduce using 2100 EC2 machines took 72 minutes to sort 100TB of data on disk, while Spark took 23 minutes using 206 machines. This shows that Spark could sort the same data 3 times faster using 10 times fewer machines.
Spark designers using a 29-GB dataset on 20 “ml. Xlarge” EC2 nodes with four cores each, compared the logistic regression implementation performance on Hadoop and Spark. Each iteration took 127s with Hadoop and 174s with Spark. But from the second iteration, Spark took only 6s which means that it runs up to 10x times faster, because of the reusability of the cached data. Results are shown in the below graph.
Fig: Logistic Regression Implementation in Hadoop and Spark
Growth of Cloud-based Big Data Analytics
Cloud computing and big data are the two hottest technologies trending in the IT department today and the current trends show that both of them will be even more combined in the future years. Some of the services such as Google BigQuery, Microsoft Azure SQL Data Warehouse and Amazon Redshift are depending on cloud computing, enabling customers to change the volume of storage and processing for which they depend on the data warehouse. According to IDC, by 2020, investments for cloud-based BDA (big data analytics) technologies will grow 4.5x faster than investing for on-premises big data analytics solutions.
Cloud computing reduces the cost engaged in big data analytics. According to a recent survey, currently, 18% of small enterprises and 57% of medium enterprises are using analytics solutions. And those numbers are predicted to escalate in the coming years because of the importance of the cloud. Businesses using Big Data and Cloud can accelerate their product development cycle, react quickly to changing market conditions, and unveil new markets that they were not aware of earlier. It is clear that big data and cloud computing can be a break point for smaller enterprises.
Big data is a technology that is moving quickly in terms of importance. It serves as a backbone for organizations to make sense of the crazy and fast world we live in. Gartner predicted that by the year 2020, IoT and big data will be used together to update and digitize 80% of business processes. To use the complete power of big data, first, figure out how you can use your company’s strategic data and master data to build analytics and reporting that represents your core strengths and operations.
Also Read: DevOps for Big Data