Big Data: Future, Growth and Challenges

2018

Big Data: Future, Growth and Challenges

Big data is associated with numerous challenges. A professional data entry company can provide the right support to manage this data efficiently.

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Today, all industries are aware about big data and its significance in making business decisions. With the advent of the Internet, a huge amount of data is being generated everyday and data conversion services are used to convert these huge volumes of data into digital format. Big data can be stored, processed, and comprehended so that it can be used to predict the future with great precision. Today, big data is growing strong and enterprises are seeking real value from it.

According to

SNS Research, by the end of

2017,

combined revenue

from big data

related hardware, software and professional services would fetch vendors over $57 billion in 2020. In 2020, it will leap beyond $ 76 billion at a CAGR of 10 percent. IDC states that big data and data analytics will surpass $210 billion in just two years at a CAGR of 11.9 percent. According to Grand View research, the big data market will rise upto $123.2 billion by 2020.

Changes Big Data Is Likely to Usher in ✓

Chief Data Officers (CDO) will gain prominence: One of the key trends in the wake of big data is the growing importance of CDOs. Gartner estimates that by 2019, 9 out of 10 companies will have a CDO.More than three-fifths or 62.5% of firms have appointed a CDO, according to New Vantage Partners, strategic advisors in business innovation to Fortune 1000 business and technology executives. In the future, CDOs will have greater authority within the enterprise and more and more businesses will expect them at the forefront of monetization.

Assigning CDOs highly responsible roles can result in positive output as they can be instrumental in connecting business data assets with the line of business users.

✓

Dark data will grow in importance: Dark data refers to the information assets organizations collect, process and store during regular activities but fail to use for other purposes. It is the data from non-digital sources and digital data that are untapped, untagged, and unstructured. Dark data, or dusty data is expected to gain

prominence

in

the

near

future

and

work

to

further

revolutionize

technology.According to IBM, by 2020 93 percent of all data will fall under the category of dark data and efforts to convert dark data into valuable data havealready started in 2018. So, in 2020 dark data will be used widely. ✓

The growth of quantum computing: Quantum computing is going to be amajor development in 2020. This type of computing takes advantage of the unique ability of subatomic particles to exist in more than one state at any time.

Operations can be done much more quickly and also use less energy than traditional computers because of the way the smallest of particles behave.

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Although it will take about half a decade for this new technology to hit the main stream, it will push traditional computing to the background and perform analytics of huge proportions. ✓

Good data governance strategy will become indispensable: To utilize big data in an effective way, you need aninfallible governance framework that gives an

accurate

description

of

the

sources

of

data,

fosters

democratization

andefficiently manages accessibility. The EU GDPR will have a huge impact on how businessesdeal with data of persons living in any EU member country.

Businesses guilty of malpractice will be held responsible by the strict rules of GDPR andthe penalties can be in millions. Forrester Research details published in January showed a bleak picture of GDPR preparedness, with only 26 percent of European companies being compliant. So, data governance strategy remains highly relevantwith increased use of big data. ✓

Proliferation of artificial intelligence and machine learning: Machine learning and artificial intelligence are two powerful technologies thatcan help you better manage unwieldy big data. These advanced technologiescan be used in various practical applications like video analytics, pattern recognition, customer churn modelling, fraud detection, dynamic pricing and so on. According to the IDC, spending on machine learning and artificial intelligence will increase from 12 billion dollars in 2017 to 57.6 billion dollars in 2021. Companies are heavily investing in AI and expecting an increase of revenue by 39 percent in 2020.These technologies will help enterprises predict events with utmost precision.

✓

Edge analytics will become popular: The rapid increase in the number of IoT devices demands different types of analytics solutions and edge analytics is the right solution. Edge analytics refers to conducting real-time data analysis at the edge of a network or a point where data is captured without sending that data to a centralized data store. It provides benefits like reduction in bandwidth requirements, minimization of the impact of load spikes, reduction in latency, and good scalability. Edge analytics will find more corporate takers in future. A survey finds that between 2017 and 2025, the total edge analytics market is expected to grow at a CAGR of 27.6 percent and will pass $25 billion and this will have an impact on big data analytics as well.

✓

Popularity of data lakes willlessen: Now, databases that hold all raw data of enterprises in their native formats have been the soul of enterprises. A major advantage of a data lake is that it helps avoid information silos. However, issues of quality, consistency, lack of alignment with business teams or excessive governance are acting as stumbling blocks when it comes to achieving actionable

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insights. As data sources, data lakes are challenging to many organizations and unless they live up to their promise, they will fail. ✓

Challenges of Big Data

•

A major challenge of big data is volume. With huge amount of data, companies are under stress without having the space to store the data for accurate analytics.

This is due to the aggressive growth of unstructured data.

•

Another challenging is the widening gap between the requirement of skilled big data professionals and their availability.

•

The problem of generating insights in a timely manner is also a challenge.

Enterprises are not supposed to keep big data idle in their repositories.

•

Continuous threat from cybercrimes, data security and privacy are major concerns for enterprises. Business have to be cautious and take necessary steps to prevent them.

•

Bid data comprises a wide range of data sources and integrating all these sourcesis a challenge.

To efficiently manage big data challenges, business can consider using outsourced solutions such as data entry services for data entry and management. A good data entry company that has long-term experience in the industry can meet a business’ changing requirements efficiently and in a timely manner. They can help organizations manage their data better and also derive valuable insights in a timely manner.

www.managedoutsource.com

(800) 670 2809