7 Big Data Trends expected in 2018

Innovation in each technological trends is developing each and every day. It was decades back when syncing structured and unstructured data and analyzing it for the betterment and growth of the business was a tedious job, but with advancement in the big data analytics solutions and tools along with business intelligence solution this process now has become way smoother and simpler.


Fast-forward to today. In the year 2018, innovation is propelling each day in regards to execution, security and speed in the tech-world. Major paces in big data analytics came up in 2016, and it extended beyond the expectations in the year 2017. For this year, few of deviation and modifications are predicted to be in trends. Here are seven of it.


In the present age, cloud-based data centres have turned out to be extremely vital. Companies are moving to cloud management because of the incredible revenue. There is a healthy competition raising between traditional providers and cloud suppliers as the new cloud valuing model can increase the enterprise development almost 5 times as compared to traditional providers. Fate of information servers is unquestionably drawing nearer to the cloud this year and in the upcoming ones.


Intelligence is advancing nowadays like recognizing fingerprints or face just like human perceptual abilities, so is the cognitive technologies. Big data experts are actively trying to implement such technology in the solution so that analysing, planning and strategizing process easier. Systems will now be capable understanding each captured and stored data with a comprehensive approach.


Time has gone of integrating a number of applications with data centres to deliver a big data-based application. Now with hybrid infrastructure, days are not away when big data analytics will be infrastructure-as-a-service with end-to-end integration instead of using inhouse, flash or cloud storage.


Artificial Intelligence is the hype since few years. This year with big data it will delve further into the security space. Machines will soon have the capacity to foresee human psychology precisely and with be able to comprehend data that are incorporated into cognitive technologies. Advancement in AI technology certainly needs a good cyber security shield that can keep sensitive information confidential from hackers and another malignant system without any human management.


IoT has been the greatest revelation in the time of innovation. Utilizing and joining Internet of Things (IoT) information is a future undertaking for all firms. Sooner IoT will turn into the essence of the data analysis. Sensor-based analytics has availed greatly to the organizations. 2017 trends and stats clearly shows that IoT will have a striking effect on big data this year even for all business domain.

Because of the developing use of Smartphones, tablets and laptops, developing companies are figuring out new ways to analyse data through interconnectivity of devices. This will directly need to improvise security, dependability and stronger connectivity to leave beneficial footprints for cloud-based big data development.


Machines functioned to perform particular tasks are a mandatory part of our lives now. Trust it or not, this is a way fact. Machine has limitless learning capabilities due to which Machine Learning is raising at a great speed. This year will undoubtedly witness speedier and enhanced Machine Learning, making more precise and proper forecasts.

Major transformation in business domains can be found because of massive data, modified algorithms and modernized hardware. Development that can cater the precise and accurate data out of the complicated enormous information without any misrepresentation discovery can be implemented using Machine Learning.


Big data will have a more prominent association of prescriptive and descriptive analysis. Predictive Analytics is here to have an enormous effect in the realm of huge information. This will help developers to enhance decision and analysis qualities and increase activity proficiency of the solution. Now big data will help businesses to make better, accurate and informed decisions by optimizing the data with predictive and descriptive analysis.


Experts are expecting for transcending $203 billion by the year 2020. The interest for information accentuation is developing step by step, and the income development originating from data will compensate for 33% of the Fortune 500 organizations. The real answer to why big data matters.

There are colossal volumes of authentic information that is stored in the storage room for years and that are yet to be digitized. Such data is termed as dark data. In 2018, with the ascent of Big Data Analytics, core focus will also be on digitization and recuperation of that information. The disclosure and digitization of such information will depict repeating patterns and item cycles with the goal that exact expectations are made for what’s to come.

Why Big Data Matters

Big data analytics truly matters these days. It helps businesses harness the data they gathered and use it to determine what customers need and to make better products in the future.

Big data analytics checks huge amounts of data to find hidden patterns, correlations as well as other insights. With technology nowadays, it is possible to analyze data and get answers almost right away, an effort that is less efficient and slower with more traditional intelligence solutions.


The big data concept has been around for years. Now, most organizations understand that if they capture all data which streams into their operations, they could apply analytics and acquire considerable value form it. However, even during the 50’s, decades before the term ‘big data’ was uttered, businesses were utilizing basic analytics to uncover trends and insights.


Big data analytics help businesses harness data and use it for identifying new opportunities. In turn, that leads to smarter business moves, higher profits, more efficient operations and happier customers. Big data technologies such as cloud-based analytics and Hadoop bring significant cost benefit in terms of storing big data amounts, plus they could identify more efficient means of doing business. With big data usage becoming more and more important to organizations, it’s even more important to find a way for them to analyze the ever growing data that course through their environment and give it meaning.

Big Data Matters



Even the smallest organizations acquire data nowadays. If a business has a website, accepts credit cards, has a social media presence, even a one-person-shop had data it could collect on its customers, web traffic, user experience and more. This means that businesses of all sizes require a strategy for big data as well as a plan of collecting, using and protecting it. This means as well that savvy businesses would begin offering data services even to very small organizations. Furthermore, it also means that industries and businesses that never considered big data would be for them could be scrambling to catch up.


From sensors usage to tracking the performance of a machine, optimizing delivery products, recruiting top talent and better tracking of employee performance, big data has the capacity to boost operations and financial efficiency for almost any kind of business and in a lot of various departments. Organizations could make use of sensors for tracking machine performance and shipments and employee performance as well. Organizations have begun using sensors for tracking employee stress, movements, health and even who they talk with and the tone of voice used. Additionally, if big data could be used successfully to quantity what it takes to make a good CEO, it could be used to enhance HR and the hiring process. Data is moving away from the IT department and becoming a vital part of each department.


Big data would enable companies to collect better market as well as customer intelligence. For a customer, the companies that one does business with know a lot about the person and the diversity and quantity of what they know is growing every year. Each organization would get many better insights into what customers want, what they would use, and the channels they use to purchase and so on. The other half of the equation is that businesses would have to be proactive on maintaining and creating their privacy policies as well as security and systems required to protect user data.


In the best of all possible worlds, organizations would use the data collected to boost the customer experience and products. Focusing on the right information by asking what is relevant to the business is a major point to obtain better data context. Plenty of data is gathered by IT, which shares the information that is important to the customer and they use the data to gain an advantage and be successful in the field.


Big data usage has become a crucial way for leading businesses to outperform their peers. In most organizations, established competitors as well as new entrants would leverage data-driven strategies for innovating, competing and capturing value. Truly, there are early examples of such data use in each sector. In the health industry, data pioneers are analyzing health results of pharmaceuticals when they were prescribed widely and determining the risks and benefits that were not evident during more limited clinical trials. Other early Big data adopters use data from embedded sensors in products such as kids’ toys to industrial goods to find out how the products are actually utilized in the real world. The knowledge informs the making of new services and designing future products.

With the continuous technology evolvement and the changing requirements in businesses all over the world, it is without doubt that big data matters a great deal. It helps businesses boost their services and remain competitive.

The when, where and why to use NoSQL

There are many instances of when, where and why one should use NoSQL. The two major attributes of NoSQL is the flexibility and scalability and has drawn a lot of attention and experimentation recently.

Relational databases and SQL Server have been the go-to databases for more than twenty years. Nonetheless, the rising need to process higher varieties and volumes of data at a fast pace has changed the nature of data storage needs for app developers. To enable this scenario, NoSQL databases which enable unstructured and heterogeneous data at scale storing have gained in popularity. NoSQL is a databases category that distinctly differs from SQL databases. NoSQL often is used to mean data management systems that are ‘Not SQL’ or an approach to data management which includes ‘Not only SQL’.


There are many instances when, where and why NoSQL should be used. A general use of NoSQL is if data structures are not defined clearly at the time of making the system. If the model structure is centered largely on one or few model objects and most relationship actually are child objects of the major model objects. In this scenario, there is a fairly little need for actual joins. When it comes to caching, even if one would want to stick with an RDBMS as the main database, it could be useful to utilize a NoSQL database for query results caching or to keep data, like counters.

The two key attributes of NoSQL databases are scalability and flexibility. Although NoSQL databases have not quite reached the hype of Hadoop data management framework, they are drawing a lot of experimentation and attention. It’s important to choose wisely among the various NoSQL options, or trade-offs required to acquire flexibility and scalability. The databases are simple and affordable than their relational counterparts. The simplicity contributes to rapid development and performance at scale. Most, although not all NoSQL databases are open source, thus one could begin with a community software and adding a commercial support and also helpful commercial add-on modules as the deployment progresses. Since the biggest dissatisfaction with existing, databases arise from licensing terms and costs, open and free would look appealing to most Information Technology teams, particularly those that bootstrap a pilot project.

Typically, NoSQL is good for unstructured or schema-less data. NoSQL typically favors a denormalized schema because of no support for JOINS per the RDBMS environment. Thus, one would normally have a denormalized, flattened data representation. NoSQL use does not mean potentially losing data. Various DBS have different strategies, one could choose what level to trade off performance against a possible loss of data. Often, it’s very easy to scale-out NoSQL solutions. The system is seen as a key part of a new data stack supporting. When something is so massive that it should be distributed massively, NoSQL is the answer, although not all systems are targeting big. Bigness could be across a lot of dimensions, not only using plenty of disk space.

NoSQL provides fast key-value access. When latency is paramount, it is difficult to beat hashing on a key and reading value direct from memory or in as little as a single disk seek. Not every NoSQL product is about rapid access, there are some that are more about reliability, for instance. Nonetheless, what people wanted for a long time was a better cached and a lot of NoSQL systems provide this. NoSQL products support an array of new types of data. This is a major area of innovation of the system. Objects that are complicated could be stored easily with no need for plenty of mapping. Developers love to avoid complex schemas as well as ORM frameworks. Lack of structure enables more flexibility.

The schema-less-ness makes it easier dealing with schema migrations without much worry. Schemas are in a way dynamic, as they are imposed by the app at run-time, thus various parts of an app could have another view of the schema. Easier administration, maintainability, and operations are very product specific. However, a lot of NoSQL vendors try acquiring adoption through making easy for easy for developers to adopt them. They spend a lot of effort on usage ease, minimal administration and operations that are automated. This could lead to lower costs of operations since special code need not be written to scale a system that was never meant to be used that way.

NoSQL systems, since they have focused on scale, have the tendency to exploit partitions, not to use heavy stringent consistency protocols, and thus are well-positioned in operating in distributed instances. Generally, NoSQL systems are the only products with a ‘slider’ for selecting where they want to land on the CAP spectrum. Relational databases choose strong consistency, meaning that they could not tolerate a partition failure. In the end, this is a business decision and must be decided on a case to case basis.

Big Data revolutionizes BFSI industry

BFSI stands for banking, financial services and insurance institutions. Furthermore, it is an industry term for businesses and organizations that offer an array of financial services and products like universal banks. When it comes to BFSI, big data plays a very integral step to developing the future of all related industries. Big Data in the industry is a set of consolidated information that is based on behavioural and other trends that people follow.