Big Data & Analytics Trends to Watch Out for in 2018

by | Dec 21, 2017 | Big Data

8 Min Read. |

Big Data & Analytics Trends 2018

Let’s have a look at what the industry leaders have to say about the Big Data & Analytics trends to watch out for in the year 2018..

Shweta Gupta, Vice President, Technology Vertical, Digital Vidya

Business and Skills Trends

1 – The enterprises will start to bring governance to their Data projects
2 – Enterprises are shifting towards new CXO position – Chief Data Officers to unify their data-based business drivers
3 – Data Engineer Job Title will become more used by businesses as they deploy teams to work on Big Data technologies

Technology Trends

1 – Python for Data Science will continue its prominence in language-of-choice for Machine Learning and Deep Learning

2 – Natural Language Processing based applications will continue to grow with focus on processing more text data for both analytics and real-time conversations and assistance. Deep Learning will play a prominent role in making this happen.
3 – Increased adoption of Big Data Technologies: Hive and SparkSQL for Data Engineering and Spark for real-time streaming with its Machine Learning and Graph library.

Bhavuk Chawla, Founder,

Open source world has taken industry by storm. Almost every company started to adopt it. Frameworks like Spark and Kafka have become kernel of Big Data Analytics applications. Kafka frameworks have gained lot of traction due to many reasons i.e. offering excellent runtime performance at scale, near real time data ingestion/processing/analysis and ease of implementing complex use cases.

Being a Coach/consultant on Big Data Analytics related technologies, I get a holistic view of evolution of various technologies across globe which I have summarised as below.

Over last 4-5 years, I see below common trend in various organizations across globe –

2014 – Big Data PoC Phase

2015 – Big Data Production Phase

2016 – Multi-tenancy, Hardening and Optimization Phase

2017 – “Big Data Analytics on Cloud” Phase

2018 – Looks like more companies will implement –

  • Big Data Analytics using frameworks/platforms like Tensorflow, Cloudera Data Science Workbench
  • Big Data Analytics on Cloud with Multi-Cloud and Hybrid Cloud Strategies
  • Automating end to end Analytics pipeline using DevOps
  • Ensuring there is seamless integration with IoT devices

I am quite excited to be part of this Big Data Analytics revolution.

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Rohit Kumar, Data Science Practitioner & Big Data Researcher

In the age of Machine Learning and AI, companies are racing towards better services and innovative solutions for better customer experiences. Businesses realize the need to take their big data insights further than they have before in order to server, retain and win new customers. 2017 has been a big year for Big data analytics with lots of companies understanding the value of storing and analysis huge stream of data collected from different sources. Big data is in a constant mode of evolution. It is expected that the big-data market will be worth $46.34 billion by 2018. Let’s look into three major domains where big data and analytics research and developed will be focused in upcoming year:

IoT Impact:

Internet of things (IoT) will become the crux of the data analysis in 2018. With more and more people moving towards smart devices such as smartwatch, mobile phones etc and a massive increase on smart homes and infrastructure has created an unprecedented surge in real time IoT data generation. Both the companies and consumers have benefited from this massive surge of real-time sensor-based data. Stream-based platforms for big data processing and real-time trend analytics is becoming one of the most popular tools. With the increase in adoption of these new smart devices and sensors around the world will demand more such real time systems and platforms to scale to the challenges of volume, velocity and variety.

Data Cloud:

A relatively small amount of data management and processing is happening in cloud today. However, according to the recent survey by Oracle around 80% of the businesses wants to move their big data analytics into the cloud. 2017 saw a continuous growth and investment by some of the major players in the development of cloud-based technologies for big data analytics. Cloud data management systems will ultimately enable new data architectures beyounds today’s data lake and warehouses. Though, many companies worry about the privacy and security aspects of moving towards cloud-based data management. The cloud-based systems are providing unique features and capabilities to handle all such issues. In 2018, use of Hybrid cloud is also expected to grow significantly, as this provides best of both world.

More accurate and evolving Machine Learning models:

Using Machine learning or ML techniques to provide some unique solutions are increasing at a lighting speed. Companies are using ML on their data lakes to get new insights and provide better predictive models. With the increase in amount of data being stored and processed it is expected that in 2018 ML-based services will be much more smarter, faster and accurate. Ronald Van Loon, the Director of Business Development at Advertisement, said:

“Your digital business needs to move towards automation now, while ML technology is developing rapidly. Machine learning algorithms learn from huge amounts of structured and unstructured data, e.g. text, images, video, voice, body language, and facial expressions. By that, it opens a new dimension for machines with limitless applications from healthcare systems to video games and self-driving cars.”

Platforms and new architecture to support huge data processing for ML and AI needs both offline and real-time is growing and will be a major trend in 2018.

Mani (Subramanian M S), Head of Analytics, Big Basket

AI – Buzz vs Reality

Commercial application of AI will continue to be stymied by
a) the science of AI lagging the engineering of AI and
b) lack of expert talent to build high-quality products.
Read to know more.

Data Science – Time to Market

It will increasingly get easier to build good quality data science models with very limited knowledge of data science. AWS, Azure, Google Cloud and other platforms will continue to make it easier to build go-to-market/easy-to-deploy models with great UI and a host of prepackaged algorithms. 
Read to know more.

To do or not to do a data science degree, that is the question

Universities will continue to find attracting students to pursue formal degrees in data science a challenge. Platforms such as MOOCs (Coursera, edX, Udacity, etc) and Kaggle will become cost- and time-effective media for acquiring data science knowledge. Industry acceptance of these non-traditional sources of learning will soon follow.

Dilnoor Singh, Consulting CTO,

The change in data analytics trends this year will occur due to an often overlooked but critical factor in data analytics, which is of Computational Power. Since the rate of adoption of new infrastructure will always be slower than the innovation rate in Data Analytics, the new trends which appear this year too are a result of how the big business powerhouses are able to adopt the new infrastructure available to them.

The Dark Data Rises

It was an opportunity waiting for its time, which now from 2018 onwards will take the center stage as it comes into mainstream. “Dark Data” is essentially the data which is overlooked because either its not collected, or its is collected but is not network connected as yet, or unorganized for lack of better software or the data which lacks the proper mainstream understanding. The Big Companies as always will hold the leadership baton, with Facebook and Google already working overtime to define and analyze what is true news on their platform and what is false. Big Insurance companies are also moving towards a telematics device model of issuing insurance policies to their customers. This will definitely require a new generation of Data Analysts to cater to it.

The Dirty Data Declines

Standardization is a term which the data analytics industry loves to hear.  For its the only thing whose nonexistence prevents them from making universal products which will work across all types and all varieties of data.“Dirty Data” is the data which has to go through extensive data transformation before it can be analyzed for any worth. This transformation costs a lot to an organization, not just in terms of money but even valuable time. Owing to a recent trend of the data analytics industry consolidating towards 5 to 8 tools for analysis, the market will head towards knowledge consolidation too, leading to standards as to how to directly collect and synthesis data which is ready for analysis. The organizations like SAS and IBM will be at the frontiers of it.

Fast Data Heats-up

“Real-time” has been a buzz word for many years now. But it has lacked general applications in aspects that matter in our lives, most importantly in healthcare. As Dark Data rises and dirty data declines, stage is set for a lot more “Fast Data” Analysis, which by definition is the data that can be analyzed in real time. Owing to more accessibility of affordable Fitness bands, its newest application will be in healthcare, as we will see for at least some common diseases, the time between getting a disease to having been cured of it will be shortened to hours from the usual days as it is now.

This was all about the Big Data & Analytics Trends in 2018. We have another set of Data Science Hiring Trends in 2019 that is laser focusses on bridging the gap between the demand and supply in the Data Science landscape.

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