According to data platform Statista, data generation will exceed 180 zettabytes by 2025. That’s an increase of around 118.8 zettabytes from 2020. Netflix saves $1 billion each year on user retention using big data. What is big data and why is it important?
Big data refers to massive data collections compiled from a variety of sources. Due to their size and complexity, these data sets cannot be collected, stored, or analysed using any of the existing traditional techniques. This data can be used in a variety of ways once it has been examined. It aids in the prevention of preventable diseases by recognising them in their early stages in healthcare. It’s also very beneficial in the financial industry, as it helps detect criminal actions like money laundering. Additionally, it aids in the research of global warming in meteorology.
Despite the fact that it improves decision-making, there are some challenges that businesses encounter with respect to big data . Data quality, storage, lack of data science expertise, validating data, and aggregating data from many sources are among them.
Inadequate understanding of big data: Insufficient understanding causes companies to fail in their big data initiatives. Employees may not understand what data is, how it is stored, processed, and where it comes from. Employees who do not understand the value of data storage, for example, may fail to preserve a backup of important data. They may not be appropriately storing data in databases. As a result, when this critical information is needed, it is difficult to locate.
To resolve this, staff who handle data on a regular basis and are involved in big data projects should receive basic training. All levels of the company must have a fundamental awareness of big data.
Scarcity of data experts: Companies require trained data specialists to run big data solutions. Data scientists, data analysts, and data engineers who have worked with the tools and managed large data volumes must be hired. Companies must invest more in the recruitment of skilled professionals. They must also provide training programmes for current employees in order to get the most out of them.
Scarcity of data experts: Companies require trained data specialists to run big data solutions. Data scientists, data analysts, and data engineers who have worked with the tools and managed large data volumes must be hired. Companies must invest more in the recruitment of skilled professionals. They must also provide training programmes for current employees in order to get the most out of them.
Data security: One of the most difficult aspects of big data is securing these massive data collections. Businesses are frequently so preoccupied with comprehending, preserving, and analysing their data sets that data security is pushed to the back. Unprotected data stores, on the other hand, can become breeding grounds for malevolent hackers. To protect their data, businesses should focus more on cybersecurity. Other measures to protect data include encrypting data, control of identity and access, endpoint security implementation, and security monitoring in real time.
Data growth issues: One of the most pressing Big Data concerns is properly storing all of these massive volumes of data. The amount of data kept in data centres and company databases is continually expanding. It becomes increasingly challenging to manage big data sets as they increase rapidly over time. Most of the data is unstructured and comes from a variety of sources, including documents, movies, audios, text files, and other media. To handle the massive amounts of data, businesses are opting current approaches like compression, tiering, and deduplication. Businesses are also opting for big data tools, such as Hadoop, NoSQL and other technologies.
According to Business Wire, by 2022, global investment on big data analytics solutions would total $274.3 billion. Big data is and will continue to be a force to be reckoned with. This is something that big corporations and industry experts are well aware of. In the long run, business leaders who make use of its numerous advantages will be ahead of their competitors.
Popular use cases of data analytics
Advanced analytics gives you the capacity to “analyse change as it happens.” Businesses will be able to “react, forecast, and plan” in real time thanks to these capabilities. Leaders in data and analytics must assess the potential commercial impact of the trends in data analytics and modify their business models and operations accordingly. Here are some popular use cases of data analytics from various industries:
Manufacturing industry
Predictive maintenance: Big data can assist in the prediction of equipment failure. Structured data (equipment year, make, and model) and multi-structured data (log entries, sensor data, error messages, engine temperature, and other parameters) can be used to identify potential problems. Manufacturers can use this information to increase the uptime of parts and equipment while also reducing maintenance costs.
Retail industry
Customer satisfaction: Retailers may use big data to get a better picture of the consumer experience and fine-tune their operations. Companies can optimize customer interactions and maximize value offered by collecting data from social media, web visits, call logs, and other company interactions, among other data sources. Big data analytics may be used to create targeted offers, lower customer attrition, and address issues before they become a problem.
Healthcare industry
Genomic analysis: In genetic research, big data can be quite useful. Researchers can use big data to find illness genes and biomarkers, which can assist patients identify health difficulties they may encounter in the future. The findings may potentially enable healthcare providers to create tailored medicines.
Oil and gas industry
Exploration and discovery of oil: Oil and gas exploration can be costly. Companies, on the other hand, can use the massive amounts of data created during the drilling and production process to make informed judgments regarding new drilling locations. Data from seismic monitors can be used to discover new oil and gas deposits by finding previously undiscovered traces.
Telecommunications industry
Increase network capacity: For a telco to succeed, it must have optimal network performance. Companies can use network utilisation analytics to discover places with excess capacity and reroute bandwidth as needed. They can use big data analytics to plan infrastructure improvements and build new services that satisfy client requests.
Advanced analytics is more than just a collection of data scientists and high-end analytics software. The ultimate goal of advanced analytics is to train business people to think like data scientists. As a result, in order for users to get the most out of their analytics efforts, the advanced analytics “vision and plan” must first be linked with the overall business strategy.
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