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Problems With Big Data that Organizations Fail to Notice

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Problems With Big Data that Organizations Fail to Notice


Problems with big data have been around for a while, and several organizations haven’t noticed them.

It’s about time that companies take note of big data issues and find suitable solutions to these problems.

Big data is known as the information that arrives from numerous sources. Another striking feature of this information is it is prone to continuous change. Today, most companies hold immense volumes of classified data. Analyzing this data can help businesses with actionable insights that help improve their decision-making. Upon analysis, data offers varying insights on market trends, competitor moves, and customer sentiments. By using these insights, authorities can decide what they can do to stay ahead of the competition. Currently, there are numerous sources of big data. These sources include different social media platforms, transactional information, phone call information, and internet activity, to name a few. According to a report, Walmart handles more than a million customer transactions that are analyzed to improve their performance. All the information that Walmart holds sums up to nearly 2.5 petabytes of information. Problems with big data lie not for managing such constantly changing information but in acquiring actionable insights from this information.

Here are the big data challenges encountered by companies:

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Analytical Problems With Big Data

Even though big data is changing businesses by providing actionable insights, there are certain problems related to it. A problem with big data is that it grows constantly and organizations often fail to capture the opportunities and extract actionable data. Companies often fail to recognize where they need to allocate their resources. This failure in allocating the resources results in not making the most of the information. Apart from that, organizations often end up with talent that does not understand how they should use big data analytics. Such a dearth of trained employees who can extract information results in companies not making the most of information held by them. Furthermore, while extracting insights from the big data held by them, companies fail to identify the right objective and end up with insights that are not so helpful for their growth.

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Data Theft is Evident

When organizations store large amounts of data sets, these sets consist of almost every type of information that is even minutely meaningful for the company. As a result, when authorities fail to install proper security measures, they are susceptible to the threat of data theft.

This information theft means that a company is losing out on vital information. Moreover, data theft can also disclose confidential information that the business has hidden over the years. This could mean a lethal blow to the business’ reputation.

Consumer information is the primary target for an attacker. By stealing such information from an organization, attackers can sell it to other companies for monetary benefits.

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Problems with big data are avoidable with proper solutions. CTOs and CIOs can start searching for ways through which they can avoid the problems of big data analytics. Securing big data is also an aspect that companies can take into consideration. Necessary changes in the infrastructure may be imperative to ensure that the data stays safe and usable.



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TECHNOLOGY

Multi-access edge computing spend to reach $23 billion globally by 2027

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Multi-access edge computing spend to reach $23 billion globally by 2027


Duncan is an award-winning editor with more than 20 years experience in journalism. Having launched his tech journalism career as editor of Arabian Computer News in Dubai, he has since edited an array of tech and digital marketing publications, including Computer Business Review, TechWeekEurope, Figaro Digital, Digit and Marketing Gazette.


A new study from Juniper Research has predicted that global MEC (Multi-access Edge Computing) spend will grow from $8.8 billion in 2022, to $22.7 billion by 2027.

This growth of 260% will be driven by increasing requirements for on-premises machine learning and low-latency connectivity; enabled by 5G technology.

MEC is a network architecture that moves processing power and digital content to mobile network edges to provide lower latency and faster processing to end users.

The new research, Edge Computing: Vertical Analysis, Competitor Leaderboard, and Market Forecasts 2022-2027, predicts that over 3.4 million MEC nodes will be deployed by 2027; rising from less than one million in 2022. It identifies autonomous vehicles and smart cities as key beneficiaries of increasing MEC roll-outs, by enabling the handling of data generated by connections in these markets to be processed at network edges. This will reduce network strain by decreasing the physical distance that cellular data will need to travel.

The report found that operator partnerships with agile technology companies, such as AWS, IBM and Microsoft, will be essential in achieving the growth of MEC node roll-outs. It forecasts that over 1.6 billion mobile users will have access to services underpinned by MEC nodes by 2027; rising from only 390 million in 2022. Furthermore, mobile cloud computing is anticipated to be a highly valued MEC service amongst mobile users over the next 5 years. By migrating processing power to the cloud, via MEC nodes, users will benefit from faster processing power and devices with smaller form factors.

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Additionally, the report predicts that the delivery of digital content, including video streaming, cloud gaming and immersive reality, will benefit from the geographical proximity of MEC nodes and increase value proposition by improving video caching and computational offload. It urges operators to maximise the user reach of MEC by focusing on urban deployments first.

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