The Future of Big Data

example of big data

The Internet of Things(IoT) devices and sensors are now taking the lion’s share of global big data, reaching more than $49 billion in revenue. Social media, search engines, SMS, and media file growth have created the need for improved cybersecurity.

Big data powers the world, and businesses seek data analytics experts to handle complex data processing.

Boosted data volumes

Data volumes will continue to increase as they are migrated to the cloud. The global datasphere is predicted to reach 175 zettabytes, a unit of measurement used to describe a computer or other device’s storage capacity, by 2025.

From business communications to e-commerce and social media use, big data’s rapid growth stems from the explosion of internet users and connected devices. These embedded systems create, collect and share loT data analytics.

Likewise, individual consumers also have a role in data growth. About 75% of the world’s population, or six billion internet users, will be connected to online data by 2025. That will translate to one data interaction every 18 seconds.

The world is now waking up to the challenge of storing and processing expansive datasets. Once solved by open-source ecosystems, like Hadoop, open-source technologies are now needed for manual configuration and troubleshooting the ecosystems.

Companies have begun turning to the cloud for more elasticity when storing and processing big data. The cloud infrastructure offers agility and ease of use to scale. Companies are now harnessing hybrid and multi-cloud environments.

Hybrids allow companies to keep sensitive info safe on-premises and non-sensitive info in the cloud. A multi-cloud environment enables businesses to address their needs by storing information in public and private clouds.

Expanded machine learning

Rapidly developing machine learning is essential to big data and is expected to impact the future as it grows more sophisticated. Most only see it now as the tech behind self-driving vehicles, fraud detection and retail trend analysis. However, machine learning helps develop technology and augment daily operations and processes. More funding has been put into machine learning than every other artificial intelligence system combined.

Open-source platforms were more popular than machine learning until recently. These platforms allow businesses that do not have configuration skills to develop their solutions.

Artificial intelligence vendors changed the course of tech history by building connectors between open-source artificial intelligence and machine learning platforms — without expensive and complex configurations. Allowing machine learning free reign over big data advances personalization and cognition. Devices can then interpret more emotion and explore literal roads and space. Machine learning and big data can understand natural language processes to uncover new patterns and make decisions.

Increased demand for data scientists and chief data officers

Social media managers, influencers, UX designers, and driverless car engineers do jobs that did not exist even 10 years ago. Data scientists and chief data officers are still emerging but the demand for these brand-new positions is already booming. Increased data volumes mean a need for these data professionals or a company will suffer.

Companies are already struggling to manage the skills gap in:

  • Data
  • Cybersecurity
  • Artificial intelligence

New gaps have been created with the need for:

  • Machine learning engineers
  • Big data engineers
  • Data scientists

Skills needed to remain relevant in the workforce today include:

  • Knowledge of data platforms, tools, and algorithms
  • Fluency in programming languages
  • Creativity with data manipulation techniques

Data scientists are expected to improve operations, create advanced analytical models and give companies a competitive edge. In return, data scientists have high salaries and job security. They don’t even need a data science degree. With the demand, many companies accept certifications in data and cybersecurity.

Chief data officers (CDOs) are part of the C-suite. They are responsible for governance, accessibility, integrity, and security. More than 67 percent of big box/digitally mature companies have a CDO employed.

Questions about privacy

Data intrusions and cyberattacks challenge privacy and can snowball with increased data volumes. Data protection is swamped by data growth rates, resulting in:

  • Security skills gaps
  • Advanced cyber attacks
  • Inconsistent standardization of security protocols

Mid-size companies have been known to lose up to $2 million in a cyberattack, and larger companies risk losing more than $5 million. States have passed laws to punish companies that are out of compliance with data privacy. Consumers demand data ethics with transparency and user-level control.

Prominence of fast and actionable data

Big data uses databases like Hadoop and NoSQL to batch data analytics. Fast data processes in real-time streams so big data analytics can be done in milliseconds. This allows fully informed decisions to be made quickly.

Actionable data connects big data and business value. However, big data is only valuable when it can be appropriately analyzed, given how complex and expansive it is. Customers and companies can now count on real-time interactions and accessible, personalized data with qualified data experts.

ThriveDX is the #1 global leader in cybersecurity education, and an expert in providing cybersecurity training to upskill and reskill lifelong learners. For more information, visit


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