Top 8 Big Data Challenges in 2021

While Big Data has taken the industry by storm, it faces the biggest challenges in volume, velocity, and variety. Further, there are several other contributing factors that add to the hitches and complications surrounding Big Data. For organizations to capitalize on Big Data while tackling these Big Data challenges, commitment is key.

Top Big Data Challenges
Updated on 24th Nov, 20 173 Views

Organizations are, most of the time, unaware of the problems that come with Big Data and will be unequipped to tackle them. Let’s take a quick look at what we will be covering in our blog today:

This blog attempts to answer the question, ‘what are the challenges of Big Data?’ But, before getting into that, let’s quickly have an overview of Big Data to help you understand the challenges that stem from its implementations.

 

Big Data Overview

An interesting point to note is that there is no fixed data size that defines Big Data. Big Data can be relative in terms of the organization that is handling it or the experience people have with it. Objectively, Big Data is more characterized by the following:

  • Volume: Big Data can be a dataset that is challenging and big for an organization to handle or process. Nowadays, social media, e-commerce, IoT, mobility, and other popular trends are generating a wide gamut of information that almost every organization has to deal with.
  • Velocity: Any organization that is rapidly generating data has Big Data. Companies dealing with popular trends mentioned above fall under this category as well.
  • Variety: Grouping and processing data can be a task when it is in a variety of formats. This variety characterizes Big Data in an organization. For example, email messages, presentations, word processing documents, images, videos, and data in relational database management systems (RDBMS).

Check out this video by Intellipaat on Big Data certification:

 

Big Data Challenges

Big Data has seen normalcy in most businesses today, but that doesn’t mean that the journey is always smooth. Gartner reported in 2016 that companies had been bogging down right in the pilot phase. According to a 2017 survey by NewVantage Partners, only 48.4 percent out of the 95 percent of the Fortune 1000 companies that adopted Big Data had generated any valuable result for their investment. Evidently, there are major Big Data challenges faced by organizations. Let’s discuss some of these challenges that most organizations are bound to face during their Big Data initiative and how some of them can be resolved.

Check out these Big Data Analytics Courses provided by Intellipaat

 

Data Sources

Literally, everything in the world can be a part of data. So, you can imagine the possibility of all kinds of sources that generate data aligning with a company’s goals or objectives. This inevitably results in Big Data integration challenges when it comes to combining data from sources such as social media pages, financial reports, documents by employees, customer logs, presentations, e-mails, etc. to create insightful reports.

Often neglected but very crucial, data integration plays a significant role in furthering analysis, reporting, and BI. A number of integration tools and ETL are available in the market for this purpose. An IDG report mentioned that most companies on the survey planned on investing in integration technology which was listed second in demand after Data Analytics software, which had the highest demand.

Some popular integration tools are:

  • Microsoft SQL
  • QlikView
  • IBM InfoSphere
  • Talend Data Integration
  • Centerprise Data Integrator
  • ArcESB
  • Xplenty
  • Informatica PowerCenter
  • CloverDX
  • Oracle Data Service Integrator
 

Go through our Talend Interview Questions and Answers to crack your job interview.

Data Growth

One of the most pressing Big Data challenges is storage. Data is growing exponentially with time, and with that, enterprises are struggling to store these huge sets of data. Much of this data is extracted from images, audio, documents, text files, etc. that are unstructured and not in databases. It is difficult to extract and analyze such unstructured data. These issues are a part of the Big Data infrastructure challenges.

Dealing with rapid data growth can be facilitated through converged and hyper-converged infrastructure and software-defined storage. Additionally, compression, tiering, and deduplication can reduce space consumption, as well as cut costs on storage. Enterprises also use tools such as Big Data Analytics software, Hadoop, NoSQL databases, Spark, AI, Machine Learning, BI applications, etc. to deal with this issue.

 

Real-time Insights

Datasets are a treasure trove of insights. However, the datasets are of no value if not insightful in real-time. Now, some may define ‘real time’ as instantaneous while others may consider the time taken between data extraction and analysis. However, the core idea is to generate actionable insights to bring about efficiency in result-oriented tasks such as:

  • Establishing new avenues for innovation and disruption
  • Speeding up the process of service deployment
  • Cutting costs through operational cost efficiencies
  • New product launches and service offerings
  • Encouraging a data-driven culture

One of the Big Data challenges is the generation of timely reports and insights. To achieve that, enterprises are looking to invest in ETL and analytics tools with real-time capabilities to have a level playing field with competitors in the market.

 

Data Validation

Data validation on a Big Data scale can be rather difficult. An organization can get similar sets of data from different sources but the data from all these sources may not always be on the same page. Getting these data to agree with each other and looking out for accuracy, usability, and security fall under a process called data governance. According to a 2016 survey by AtScale, the fastest-growing concern was data governance.

Tackling Big Data management challenges and data governance can be complex with all the policy changes combined with technology. Special teams are assigned to handle data governance and invest in ad-hoc data management solutions that ensure data accuracy.

Certification in Bigdata Analytics

 

Data Security

Security can be one of the most daunting Big Data challenges especially for organizations that have sensitive company data or have access to a lot of personal user information. Vulnerable data is an attractive target for cyberattacks and malicious hackers.

When it comes to data security, most organizations believe that they have the right security protocols in place, which are sufficient for their data repositories. Only a few invest in additional measures exclusive for Big Data, such as identity and access authority, data encryption, data segregation, etc. Often, companies are more immersed in activities involving data storage and analysis. Data security is usually put on the back burner, which is not a wise move at all as unprotected data can fast become a serious problem. Stolen records can cost millions for a company. Overall, companies should surely overcome the Big Data privacy challenges and security challenges that are like a hurdle to them.

The following are how an enterprise can tackle the security challenges of Big Data:

  • Recruiting more cybersecurity professionals
  • Data encryption and segregation
  • Identity and access authorization control
  • Endpoint security
  • Real-time monitoring
  • Using Big Data security tools like IBM Guardium
 

Big Data Skills

Running Big Data tools requires expertise that Data Scientists, Data Engineers, and Data Analysts possess. They have skills to handle Big Data challenges and come up with valuable insights for the company they work in. The problem is not the demand but the lack of such skills that, in turn, becomes a challenge. Big Data salaries have drastically increased over the years. As of January 2021, the average annual compensation offered to Big Data Specialists in the United States is US$107,892, according to ZipRecruiter. Although organizations are spending on recruiting such skills, they are also investing in training their existing staff as well.

At the rate data handling tools evolve, data professionals aren’t able to keep up. Hence, organizations invest in AI/ML-powered Data Analytics solutions. This allows even non-experts to easily run the tools with basic knowledge. This way, the companies can cut costs on recruitment, as well as achieve their Big Data goals.

Register for our Big Data Course and start a career in Big Data.

 

Resistance to Big Data Adoption

It is not only about the technological challenges of conventional systems in Big Data but also the resistance that Big Data adoption faces. While many want to introduce the data-driven culture in their organizations, only a few successfully get through with it. Why does it become a challenge? It is observed to come down to three reasons:

  • Business resistance due to lack of understanding
  • Lack of organizational alignment
  • Lack of middle management understanding and adoption

Primarily, due to the lack of understanding of Big Data, companies fail in their initiatives. Most employees may be unaware of what data is, let alone have any idea of its importance. If employees do not understand the importance of Big Data, they may not follow the correct procedures or protocols that are necessary for handling Big Data and, as a result, introduce unseen setbacks.

Introducing Big Data can bring about a tremendous change in any organization, which can be difficult. Workshops, seminars, and training programs are a great way to introduce employees at all levels to the world of Big Data. Decision-making will improve with strong leadership who knows how to capitalize on the opportunities that Big Data provides. Thus, adoption along with Big Data implementation challenges still continues to hamper the organization’s progress.

Aware of the challenges of Big Data? Let’s get you started in Big Data in our blog at Big Data Tutorial.

 

Conclusion

In this growing data-driven economy, it is essential to stay in the competition. While Big Data challenges can pop up at any step, it is essential to understand that everyone has their own way of tackling them. The scope of Big Data is endless, which makes it ever-evolving. Even experts are up to figuring out new ways around these Big Data challenges.

Visit our Big Data Community and start a discussion with our experts.

Course Schedule

Name Date
Big Data Architect 2021-04-24 2021-04-25
(Sat-Sun) Weekend batch
View Details
Big Data Architect 2021-05-01 2021-05-02
(Sat-Sun) Weekend batch
View Details
Big Data Architect 2021-05-08 2021-05-09
(Sat-Sun) Weekend batch
View Details

Leave a Reply

Your email address will not be published. Required fields are marked *

Associated Courses

Subscribe to our newsletter

Signup for our weekly newsletter to get the latest news, updates and amazing offers delivered directly in your inbox.