11 Major Trends of Big Data and Analytics in 2018
Today Big Data is no more a buzzword. Our perception about Big Data, even a few years ago, has also changed. The term dissolved from Gartner Curve in 2015. Today Big Data is more of a concept about managing large data repositories in all sorts of ways. Companies too are shifting from the departmental data approach to business-oriented data approach.
The convergence between Big Data and technologies such as Machine Learning, Artificial Intelligence (AI), and IoT is the next big story. All these trends made ‘Data management and predictive analysis’ the next big thing to watch in 2018 and going forward.
Before we look into the trend of Big Data analytics, let us understand when data becomes Big Data and what the use of that Big Data is. According to Gartner, when Volume, Variety, and Velocity (the 3Vs) of data flow is too big, it is called Big Data. The objective of Big Data is to find templates and patterns within unstructured data flow that can be used to simplify data for various purposes and detect anomalies.
In this article, we will learn the pulse of Big Data trends and its developments in industry, academics, and technology. When asked the domain experts, here is the summary of what came out from overall discussions.
Big Data Analytics and Data Science, 2018 Predictions and Trends
1. Technological Amalgamation is on Rise
The applications of Big Data are becoming bigger. With the increased adoption of Machine Learning, Artificial Intelligence, and IoT, Big Data analytics are gaining more traction among various enterprises. Data analytics will continue to support more such technological capabilities so that people and companies can take advantage of combined innovations.
2. Hybrid Data Management is on Radar
As compared to 2017, we will see more focus on data management, data visualization, and hybrid cloud and less on Hadoop. Since data science started picking up in 2017 followed by new data platform releases, there was already some progress on hybrid data management and data visualization. In 2018, Big Data projects will see more real case studies. As experts say, 2018 will be the year of NoSQL as NoSQL will be finally established as a realistic solution for both structured and unstructured data.
3. New Position of CDO Opens
On the business front, enterprises are expected to invest more effort in the governance of data projects. Therefore, a new CXO position of Chief Data Officer will roll out to drive all major data-based businesses. Data engineers deploying Big Data technologies will find newer roles and higher position with time.
4. More R&D Investments by Big Data Analytics Companies
The market for Big Data is continuously evolving. It is expected to reach $46.34 billion by 2018. Therefore, more research and development related expenditure will be there in the Big Data analytics field. This R&D will primarily be focused on three areas such as IoT, Data Cloud, and Machine Learning.
5. Impact of IoT in Big Data market
In 2018, more and more people will use smart devices, smartwatches, smartphones, smart homes, and even smart infrastructure. This will lead to massive amount of real-time and sensor-based data generation by the smart devices. Therefore, the demand for stream-based platforms and real-time system will continue to grow. This effectively means that companies will be busier in developing data management tools that can scale to the demand for Volume, Velocity, and Variety.
6. Impact on Machine Learning Models
Automation being at the forefront this year, companies will put more effort in devising effective and advanced Machine Learning (ML) techniques. With a steep increase in the amount of data being stored and processes across devices, the ML-based services need to be faster, smarter, and precise. New platforms and architecture are required to support the huge data processing needs of Machine Learning technology.
For example, with newer applications being used in gaming products, healthcare systems, and automobiles, ML algorithms need to learn more from the massive amount of structured and unstructured data in the form of text, voice, speech, video, images, body language, and facial expressions.
7. Impact on Data Cloud
A recent survey by Oracle says that over 80% of businesses aim to move their business data and Big Data analytics into the cloud. Since privacy and security issue has been a concern for cloud-based data management, a new concept of Hybrid Cloud came in place. This trend of Hybrid Cloud will require existing cloud-based data management systems to apply new architecture, superior to data warehouses. Companies dealing with cloud-based technologies for big data analytics will eventually see more growth and innovations in coming days.
8. Cloud Storage and Cloud Computing Get More Traction
With Big Data becoming bigger, the Cloud storage capacities will also need to be scaled up. Since smaller companies cannot afford a high investment in setting up the data centers, the dependencies on cloud storages such as Google, Amazon or MS Azure will continue to increase in 2018 and beyond. The same applies to the Cloud computing capacities as more computation power will be required to process high volume data. This signifies that cloud computing giants such as Google and Amazon would continue to make more business this year.
9. Fair Trade-off in Data Science
2017 was a remarkable year for Big Data. One new theme that came up was the impact of Big Data on society. The agenda was to identify fake news and misuse of social media in unregulated digital platforms. 2018 will see discrimination-aware data science which means a lot more research will be there to ensure accuracy and fair social and technical trade-off. Novel algorithms will enable systems better at viewing, reading, managing unstructured data which increases accuracy. A huge progress is also going on in the area of autonomous vehicles, language processing, and emergence of intelligent systems that can interpret and translate videos, images to learn algorithms.
10. Clean Data Rises for Security and Stability
With Big Data accelerating across business, science, and government sectors, maintaining the stability of critical systems and fair governance have become the highest priority. Removal of dirty data from the system can save huge data transformation cost and time. Today, with at least 5 to 8 analytics tools being consolidated, the data analytics industry will also focus on knowledge consolidation and standardized analysis process. Companies like IBM, SAS are already pioneering this drive.
11. New Tools Introduced for Neural Networks
In 2018, there will be a steep rise in the use of data analytics tools or platforms such as TensorFlow, Microsoft Cognitive Toolkit 2.0, Scikit, and Data Science Workbench by Cloudera. Developers and data analysts will be less likely to follow algorithm-specific approaches such as Decision Tree Method, CNN, DNN, FNN, Random Forest Method, and so on. Instead, these platforms will be used to solve specific tasks by following particular approaches. For example, CNN platform will be used to identify images in the picture; decision tree will be used to measure credibility and reliability of financial organizations and so on.
Though Machine Learning and cognitive computing are already in talks, they are yet to be refined in large scale. It is expected that Artificial Intelligence (AI) will be able to explain the reasons for actions and take moral decisions between good and bad. This essentially means that deep learning of custom tools will be of biggest value.
Conclusion
All in all, 2018 is going to experience a big boom in the Big Data Analytics market. There is no sign of slow-down in its popularity in near future. AI and deep learning will continue to mature and it is time AI will produce unbiased outcome, not driven by human data. Overall, 2018 will be an exciting year for Big Data Analytics experts and consultants.
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About The Author
Sarah Clark
Sarah Clark, working in B3NET Inc. & Mobixed as Content Developer. B3NET is leading web design & web development company from Orange County, California, Search Optimal is the Internet Marketing division of it. Sarah is working in web, mobile & Internet marketing domain with her 8 years of experience.