According to research by Forrester, almost 40% of firms are implementing and expanding the adoption of Big Data technology. Another 30% are planning to adopt the technology in the next 12 months. In fact, a Big Data Executive Survey by NewVantage Partners is indicative of its increasing adoption rate way back in 2016. The survey discovered that 62.5% of the firms had at least one Big Data project in production and that only 5.4% of organizations had no planned Big Data initiatives. The figures might differ now, but one can get a preview of the trending demand of Big Data and its testing-related requirements.
Big Data is an important pillar for enabling digital transformation and designing solutions to solve business-critical issues across various industries, globally. In fact, with the advent of digitization, big data analytics has become an integral part of doing business globally.
So, what is Big Data?
It is a conglomeration of a massive volume of both structured and unstructured data that cannot be processed by traditional methods. In most cases, the volume of data are either too big or they exceed the processing capacity.
Big Data has the potential to help big organizations make critical business decisions. The data are collected from various sources including emails, mobile devices, applications, and servers. These data are captured, formatted, manipulated, stored, and then analyzed. In fact, these can help a company gain useful insight to increase revenues, get or retain customers, and improve operations.
Advantages of Big Data
The importance of Big Data does not lie in the data that an organization has, but in how it uses the data. Every company has a unique way to use data and its ability to grow depends on how efficiently it puts the data to use. The company can take data from any source and analyze them to:
Save costs: Tools like Hadoop and cloud-based analytics help in determining the cost advantages when large amount of data need to be stored. These tools also help in identifying more efficient ways of doing business.
Save time: Hadoop and in-memory analytics can easily identify new sources of data, which helps in analyzing them successfully and quickly. This also helps in making quick decisions, based on the learning.
Develop new products: By knowing the trend of customer needs and their level of satisfaction through analytics, one can create products accordingly.
Maintain online reputation: Big Data tools can also do sentiment analysis. Sentiment analysis provides insight into what customers, potential customers and other influencers feel about the company. Big Data tools can also help in monitoring and improving the online presence of businesses.
Understand market conditions: By analyzing Big Data, one can get a better understanding of market trends. The customer purchasing behaviour can be tracked and businesses can understand which products are popular.
Top Big Data Trends 2019
Faster Growing IoT networks: It is expected that IoT will grow $300 billion annually by 2020. According to the recent trends and industry standards, the global IoT market will rise at a Compound Annual Growth Rate (CAGR[GO1] ) of 28.5%. Enterprises will rely on more data points to gather information for more detailed business insights.
Dark Data: This is the digital information available which is not used for any business analysis. The data is gathered through numerous computer network operations, which are not used for decision making. There is an increasing need to understand that any unexplored data is an opportunity lost and may lead to potential security risk.
Chief Data Officers (CDOs[GO2] ) to be in demand: The importance of a chief data officer is increasingly being realized, thus leading to their evolution. Incidentally, human resource personnel are opting for this role. CDO is still a new concept for many companies.
Open Source: 2019 will witness more free data and software tools in the cloud. Both small organizations and start-ups will benefit from this data trend. Open source analytical languages like R, which is a GNU project associated with statistical computing and graphics have seen a huge adoption, credited to the open source wave.
Predictive Analytics: This offers customized insights that lead organizations to generate new customer responses or purchases and promote cross-sell opportunities. This helps technology to integrate into diversified domains like finance, healthcare, retailing, hospitality, etc.
Quantum Computing: Quantum Computing enables seamless data encryption, solving complex medical problems, predicting weather, real conversations, and better financing modelling to make organizations develop quantum computing components, applications, algorithms, and software tools on qubit cloud services.
Edge Computing: This has been into the technological space, streaming network performance for quite a while now. It is because of this that data analytics is reliant on the network bandwidth to save data locally close to the data source. It enables data to be handled and stored away from the silo setup, closer to end users with processing taking place either in the device or in the fog layer or in the edge data center.
According to recent researches, the adoption of Big Data technologies will continue. International Data Corporation (IDC[GO3] ) predicts that the Big Data and business analytics market will increase from $130.1 billion this year to more than $203 billion in 2020.
As per Dan Vesset, group VP, analytics and information management[GO4] , IDC, “The availability of data, a new generation of technology, and a cultural shift toward data-driven decision making continue to drive demand for big data and analytics technology and services.”
Qualitest Group is the world’s largest software testing and quality assurance company. QualiTest offers quality assurance and software testing services, and is committed to the highest standards of quality in every projec
[GO1]Please explain what this acronym is.
[GO2]Please make sure you spell out all acronyms the first time you use them.
[GO3]Please spell out
[GO4]Of what company?
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