With Big Data already been a $150 Billion marketplace and soon going to cross $200 Billion figure, it’s time to start taking it seriously. According to Statista, the market value of Big Data has increased by over 500% in last 5 years, and will continue to grow at the same pace.
The past few years have already proven the significance of big data in the digital world. In this perspective, 2017 could be the revolutionary year for Big Data with this technology expecting to touch $200 Billion mark. The path seems clear due to no clear competition, and the developers continuously working to make the technology easy to use and implement.
Many trends can be forecasted and witnessed by the end of 2017, including a few listed below:
- Moving To the Cloud:
Clouds could be the next home for Big Data by the end of the year. In the past years, the effects of cloud computing on businesses have grown in imminent. The flexibility of infrastructure and access to the Big Data is crucial to its success.
Companies have already started finding ways to take Big Data to the cloud. The most groundbreaking step was taken by Oracle when the tech giant announced to expand their cloud portfolio.
In 2017, you may witness smarter storage options developed and optimized for storing and managing petabytes of data. This approach will result in reduced spending on data centers and streamline the data sets for enhanced usability. Also, the APIs will be strengthened to release data capabilities in a reusable manner.
- Variety, Volume & Velocity – Three Vs to count on:
According to Gartner, Big Data will entirely depends upon three Vs. While all three are growing rapidly, Variety will remain the most sought-after feature, as shown in recent study by New Vantage Partner.
Organizations will demand for customized solutions instead of one-fits-all. This is also the reason why organizations are looking for the long-tail of Big Data. From schema-free JSON to non-flat data, variety and customization will continue to dominate the industry.
- Enhanced Data Speed:
The speed of Hadoop has always been a matter of concern for machine learning, but now Hadoop is no longer the only game in town. As of July 2017, around 61% users are using Spark on public cloud while the number of Hadoop users has decreased to 36%.
Data scientists are now relying on faster databases that enable faster processing. The Hadoop-based stores and latest technologies using SQL-on-Hadoop and OPAL-on-Hadoop have emerged on this landscape. These are the query accelerators enhancing the speed of Hadoop engines.
But what we may anticipate in the near future is more powerful and efficient alternatives to Hadoop. There are options in the form of Apache Spark, Apache Storm, Ceph, Google’s BigQuery to name a few. Bhaskar Gupta of Analytics India Mag discusses about 10 solid alternatives to Hadoop in this post.
- IoT in Big Data:
Internet of Things and Big Data born together and grown to be the biggest sensations of the past few years. The forthcoming months may see these siblings playing together as an entity. SAS Insights take IoT and Big Data as two sides of same coin, and 2017 may bring them on the same side.
As of 2016, around 6.5 billion devices are running over IOT and a study by Cisco anticipates this number to touch 50 billion mark by 2020. The data sets generated by connected ‘things’ are massive and analysis of this data can explore new opportunities for the organizations. With these initiatives, you can anticipate data aggregation in the companies for enhanced visualizations.
- Data Security will become Critical:
IoT witnessed first Big-Data-enabled DDoS attack in 2016, and Cisco’s 2017 Midyear Cybersecurity Report confirms the DeDoS attach will be bigger than its predecessor.
Security is a big threat for Big Data, and it will become worse with the blend of IoT and Big Data. This situation can be averted effectively by regulating data access permissions.
In 2017, companies and the governments can be seen working together to find strong regulations for the hardware manufacturers and software programmers.
- Big Data for Improving Customer Interactions:
The core motive behind introduction to Big Data was to improve decision making and customer interaction. With most of the cutting-edge gadget employing Artificial Intelligence, Big Data is expected to play a critical role in improving customer interaction.
The possibility of AI in Big Data is endless, with intelligent applications like Siri, Google Home, Alexa and Viv. These apps have already allowed you to play music or even order pizza, 2017 may end with more powerful functions.
The potential of big data can be efficiently harnessed for improving the experiences for the customers and users. It can be done by moving to vendor systems and upgrading the core systems. The valuable insights gained from this data can be used for analyzing the trends and competition lingering in this space. This will result in attracting new customers and retaining the existing clientele.
Soon we can see AI and Big Data controlling everything in your life, including controlling your car from inside your home or even controlling everything within your organization over voice.
- Mixed Reality For Better Management:
Virtual Reality, Augmented Reality, and now, Mixed Reality are making giant strides in the digital landscapes. These technologies are changing the ways work can be done.
Virtual reality will play a crucial role in achieving this goal. As we have seen with popular game PokémonGo that reached over 100 million users within one week, playing with virtual reality and artificial intelligence may help improve customer interaction.
The impact of VR and AR is primarily harnessed by the gamers and entertainment industry. PokémonGo can be quoted as a great example of it. Now is the turn of C-Suite to embrace these trends and they are looking forward to mixed reality for all good reasons.
Mixed Reality is the convergence of virtual space in the real world to comprehend the data accurately and perform multiple tasks at hand. 2017 may see a high use of virtual reality in various fields. Manufacturers may use it to produce better connected gadgets, service providers may use it to create better services using customer feedback and organizations may use it to empower their employees.
- The Rise in Self-Service Solutions:
After investing hugely in the Big Data and its analytics, the chief stakeholders will seek monetization of these initiatives in 2017. Data analytics lie at the core of their operations.
Since every organization is not financially sound to invest in advanced analytics and hire specialized data scientists, we can anticipate the rise of Big Data as a Self Service solution in the coming years. This development will enable the companies in monetizing the data for gaining better insights and improving their organizational performances.
Monetization will be the next big step that organizations may take to fund their efforts. This can be done by selling their data, charging from customers or creating ready-to-sell products, as shown in chart below:
With agility and cost optimization, these big data services will increase the productivity of the small and medium-sized businesses. And, these apps will dominate the market for many more years to come.
- More Impetus on Dark Data:
Data storage can be seen as a big money-maker for organizations involved in Big Data. IBM announced to provide a data storage service called Spectrum Storage that promises to reduce infrastructure costs up to 90%. Many more tech-giants have come up with similar services.
Currently, there are only a few big players in marketplace offering data vaults and are charging whooping rentals to store the data. With small and medium organizations getting more interest in data accumulation, offering data vaults can be the next game changer.
The data stored in physical formats as company assets have huge potential for its accumulation with big data. Hence, more companies will open their vaults and extract this dark data for analyzing the historical trends and predicting the future trends.
- Deep Learning may take a shape:
Today, we have smart gadgets and technologies able to perform highly complicated tasks with the help of the algorithm learning. China has already developed a system that may identify a criminal by looking at a face. Many similar examples do exist that pave the path of more intelligence knocking the doors.
In 2017, we may witness superior gadgets of deep learning or machine learning replacing common human algorithms. There will be more tasks done with data and there will be more gadgets able to recognize patterns, images & videos to make a decision.
Along with these trends picking up the pace in 2017, the CIOs and Big Data experts are also burdened with the task of explaining the tangible results of this data. Hence, they need to be bootstrapped with robust techniques for delivering anticipated results to the companies.
It will be the year of data intelligence with innovations and technological breakthroughs emerging continually in this domain. Here, it is imperative to note that the effectiveness of these trends will completely rely on the integrity and capability of data collected, assessed, and stored. For experts, it’s time to analyze and come up with stronger data analytics, and for businesses, it’s time to leverage the Big Data benefits by making their businesses Big Data ready.