According to a Gartner report, in 2021, big data will be used in businesses as diverse as energy production and driving. Organizations will begin leveraging this information to provide new insights into healthcare, education, the environment, government, and defense.
However, despite the pervasiveness of big data technology on such a global scale by 2021, several challenges need to be resolved before businesses can truly make a dent in their organizations’ analytics initiatives.
Here are the Top 10 challenges Big Data faces in 2021
1. Data volume growth
The widespread growth of IoT devices means an exponential increase in connected devices, generating up to 163 zettabytes (163 trillion gigabytes) by 2025. That’s more than 17 times the amount of all the data created in 2011.
2. Data velocity growth
The year 2020 will see a doubling of real-time information generation, and it’s estimated that the world could be producing around 1.6 million gigabytes per second by 2020. Much of this data is unstructured, making it more difficult for analytics to extract value according to RemoteDBA.com.
3. Skill shortages
According to research, companies are using over half their big data projects internally. However, they still struggle with making their firms more agile even though they have access to large amounts of raw data through third parties or cloud providers. The result: investment in big data is projected to reach $300 billion by 2017 (Gartner), a steep increase from $130 billion in 2014.
“We believe that one fundamental reason for this is companies are not able to extract value from the data due to lack of skills and experience in advanced analytics,” says Gartner analyst Svetlana Sicular.
To keep up with big data demands, Gartner recommends that global organizations increase their workforce by 40 percent, or 1.9 million employees, and develop skills on how to derive meaningful insights from raw datasets. Additionally, executives must educate themselves about the different use cases and applications for big data across a business’ various departments. This will help them make more informed decisions when implementing new strategies while also putting forth practical ways to address issues like security, privacy regulation.
4. Integration of new data
As more companies like GE start exploring what big data can do for them, executives still have to account for huge amounts of unstructured information and integrate it with their structured data for analysis.
“Consequently, we believe that the real power of big data analytics will be seen at the intersection of different types of data (i.e., structured, unstructured) coming from both internal and external sources,” said Jon Iwata, senior vice president of GE Global Growth & Operations.
The future is looking bright for innovative big data solutions that can help businesses derive more value from their datasets in multiple ways. But this technology needs to become a priority among those implementing these types of strategies. These tools act as a safeguard against security threats and help companies overcome some of the challenges they face as more businesses start embracing big data.
5, Privacy and security concerns
Companies need to balance big data’s benefits to an organization with potential privacy breaches surrounding sensitive client information. They’ll have to address changing regulations regarding how long they are allowed to store datasets, which could seriously impact their decision-making capabilities. Additionally, sophisticated hackers will always pose a significant threat online, and with so much data at stake, enterprises need strategies to keep cyber-attacks from disrupting business operations.
6. Lack of talent
According to Gartner’s research, 66 percent of organizations say it is somewhat very challenging to find qualified staff with the necessary data analysis skills.
This is due to an overall lack of education and experience with more complex analytic tools and a limited number of qualified professionals in the field.
“The result: investment in big data is projected to reach $300 billion by 2017 (Gartner), a steep increase from $130 billion in 2014,” Sicular said.
7. Storing exponentially growing datasets
Enterprises need to determine how they want to structure their data before it becomes problematic for storing large volumes of information. Depending on the business’ needs, they’ll be able to store their information via cloud services or on-premises solutions that can handle this volume.
Gaining new insights will also depend on how well they cleanse their datasets and prepare them for analysis. For example, if companies find inaccurate or incomplete data, it could skew the insights they gain from these analytics tools.
8. Lack of automation
“As the number of big data sources continue to increase exponentially, enterprises will look for more automated ways to discover and extract value from all available sources,” Sicular said. “It is critical that big data analytics solutions support easy integration with real-time streaming data.”
According to Gartner’s estimates, by 2018, 40% of new big data initiatives will still be in pilot mode as businesses try out various strategies that work best for them. This means choosing the right technology is just as important now as when you started experimenting with these solutions.
9. Security challenges
Their security concerns extend beyond individuals gaining unauthorized access to their data. Because of the proliferation of mobile devices and embedded technologies, the business now has many ways that hackers can get into their systems and cause serious damage. For example, the industrial Internet of Things (IoT) is expected to grow exponentially in the next few years as more businesses embrace connected products that work together seamlessly within this ecosystem.
According to McKinsey & Company, there will be roughly 30 billion interconnected devices by 2020 compared to 6.5 billion today. As such, companies need strategies in place for protecting these endpoints from being compromised before they become exposed entry points for cyber attackers looking to acquire sensitive information.
10. Organizational resistance
Regardless of how much a company spends on achieving these goals, it must have organizational support to succeed in its big data initiatives. Moving forward means establishing trust within the organization for making new investments in technologies that may not be widely understood. “The industry will continue to see IT leaders and chief executives taking greater accountability for defining business-relevant use cases for analytics,” Gartner said. “This focus on specific applications of big data will help drive better results and more effective long-term investments.”
Big data is revolutionizing the way we live and work. However, there are some challenges big data faces in 2021 which will need to be addressed by those who use it for business purposes. For example, how can businesses remain competitive when they have access to information that their competitors do not? What measures should companies take if they want to maintain the confidentiality of private customer records? How can a company make sure that employees don’t abuse confidential user or client data? These questions demand answers from all stakeholders involved – including customers.
Watch this space for updates in the Technology category on Running Wolf’s Rant.