5 Big Data Challenges and Solutions

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    The amount of data we produce daily is mind-boggling. Did you know that 2.5 quintillion bytes of data are generated every day? Or that there are 44 zettabytes of data in the entire digital universe?1

    With vast amounts of data being created every second from customer logs, sales figures, and stakeholders, data has become the fuel that drives modern businesses. But this vast amount of data brings challenges as well as solutions.

    While it’s clear organisations can benefit from big data, they need to be cautious and aware of the challenges they’ll likely encounter, particularly around:

    • Collecting, integrating, sharing, and storing data
    • Deriving and utilising valuable insights from data

    Luckily, there are solutions organisations can deploy to handle key big data problems and thrive in a data-driven economy. This article will discuss these challenges and potential solutions. But first, let’s briefly take a look at the concept of big data.

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    What is Big Data?

    Interestingly, no fixed data size defines big data. In essence, big data is a term that describes data that is huge in volume and growing exponentially. It refers to the large volumes of structured and unstructured data that inundates organisations.

    That said, a business has big data if its data stores bear the following characteristics:

    • Volume: Your data is voluminous — so large that your company faces challenges monitoring, processing, and storing it. With trends such as e-commerce, social media, and the Internet of Things (IoT), a lot of data is being generated. As a result, almost every organisation, big and small, satisfies this criterion.
    • Velocity: Does your organisation generate massive data rapidly, and you’re required to respond in real-time? If so, your company has the velocity required to satisfy big data requirements.
    • Variety: Does your organisation deal with data collection and storage in different formats? If it does, then it has the variety associated with big data. Big data is structured and unstructured from diverse sources, including emails, social media feeds, videos, voice recordings, images, search indexes, and many other sources.

    Now that’s clear, let’s dive into the main big data issues and challenges businesses face and the solutions available.

    1. Big Data tools

    When choosing the best tools for massive data analysis and storage, data-driven organisations can sometimes get confused.

    Is Cassandra or HBase the simplest technology for data storage? Is Spark okay, or is Hadoop MapReduce the better solution for data analysis and storage?

    Managers and business leaders often find it hard to answer these questions and find the best solution. They end up making the wrong decision and selecting technology unable to ensure the data quality that facilitates desirable outcomes through big data analytics.


    The solution to this particular problem is working with experts who understand the various big data tools available to businesses and how to utilise them effectively. As a result, many businesses are now looking to hire professionals familiar with these tools, including their use cases and their pros and cons.

    Alternatively, businesses can also seek the help of a data consulting agency. Consultants can recommend the best tools based on various case scenarios, and using their advice, businesses can select the best tools and implement the best strategy for data handling.

    2. Skills shortage

    Companies need data professionals to operate these modern big data tools. However, there is currently a serious shortage of big data professionals. The big data niche is quite new and difficult to master, as it involves working with complex technology and tools.

    A decade ago, the Harvard Business Review named data scientists the “sexiest” job on the planet, and in 2011, the job postings for data scientists increased by 15,000%.2 Today, as big data continues to drive industries, the demand for data scientists continues to grow. However, the research shows there’s a shortage of data professionals across industries.

    In 2020, for instance, consulting firm QuantHub compiled data from LinkedIn, Glassdoor, and Indeed.com and found a data scientist shortage of 250,000.3 This study was based on postings for data scientists and web searches for similar postings and collaborated by McKinsey, Harnham, and Burtch Works. 

    Lack of skills is one of the primary reasons there’s a shortage of data scientists in the market. Data scientists must possess technical skills and have good command over statistics, mathematics, tableau, programming, and big data technologies.

    They also need to possess non-technical skills, such as:

    • Communication
    • Data intuition
    • Data inquisitiveness

    So, what should companies do to find skilled and experienced professionals in an industry experiencing skilled labour shortages?


    One approach businesses can take is to invest in data analytics solutions powered by artificial intelligence (AI) and machine learning (ML). These tools can be run by individuals who are not data scientists but have basic data analysis and management knowledge.

    Organisations should also offer training to their existing staff to make it possible to get the most of them. This can help businesses save valuable resources that could have been spent recruiting, training and retaining new members of staff.

    3. Integrating data from various sources

    Corporate data comes from various sources across a business, including:

    • CRM solutions
    • Financial reports
    • Customer logs
    • Emails
    • Employee reports

    Big data integration centres around combining this data into a single “version of truth” that can be used by everyone in an organisation. Integrating data from diverse sources often poses a challenge to IT teams because in most cases, this data usually comes from different platforms. 

    IT teams often find handling data originating from various different software and hardware platforms difficult to manage. As a result, this task is neglected by many organisations.


    When integrating data from diverse sources, many teams and organisations tend to go manual. This may seem easy and cheaper but can prove costly down the line. That’s why the use of automated tools to perform this task has become a popular solution.

    Many software automation tools come with hundreds of pre-built application programming interfaces (APIs) for a broad data spectrum. While you may have to hand-develop some APIs on a case-by-case basis, these tools can do most of the work.

    4. Security and integrity

    Securing massive data sets is one of the major challenges organisations face. Often, companies are so busy understanding, sorting, and analysing their data sets that they don’t give security the attention it deserves.

    In an age where more than 80% of companies have experienced some form of a cybersecurity threat, focusing on data security is vital.4 Not adopting data security best practices can subject your business to all sorts of external and internal attacks, including ransomware. However, when it comes to data security, many organisations believe they have the right protocols to thwart any attack.

    The truth is, that only a few companies invest in additional measures exclusive to big data, such as data segregation, data encryption, and identity and access authority. Many organisations invest time and money in deriving meaningful information from the data and end up putting security on the back burner.


    Fortunately, there is a wide range of measures organisations can take to ramp up data security. This includes, but is not limited to:

    • Real-time monitoring
    • Endpoint security
    • Identity and access authorisation control
    • Recruiting more cybersecurity professionals
    • Outsourcing data security and management to a data security firm
    • Leveraging big data security tools, such as IBM Guardian

    5. Storing Big Data

    Data storage is a critical component of big data management. Because organisations handle massive amounts of data, it’s vital to invest in storage systems that support multiple platforms and offer unlimited connectivity.

    However, organisations often encounter challenges when it comes to data storage. These may include:

    • Scalability
    • Data security
    • Data protection
    • Data accessibility

    For instance, in the event of a disaster, it’s crucial that organisations can securely recover the data and resources needed to ensure normal operations are not interrupted. But this can pose challenges if a storage system isn’t equipped for business continuity.


    Organisations need to store data digitally to ensure easier access for decision-making purposes. Outsourcing data storage can help you overcome some major challenges of big data, including both security and scalability.

    On top of that, there is a range of benefits businesses can access from outsourcing their data storage requirements, including:

    • Longer uptime: A managed storage service provider is bound by a Service-Level Agreement to provide a solution with a near 100% uptime and minimal downtime in the case of unforeseen events.
    • Rapid implementation: Building or expanding a data centre requires time and money. It can take between six months and two years to get a data centre up and running, but outsourcing lets you reap the benefits of cloud data storage quickly.
    • Flexibility: As the amount of data you process and store grows so do storage needs. Outsourcing gives you the flexibility to match your storage capacity to business needs without having to acquire new assets, whilst cloud backups can now provide accessibility anywhere at any time.
    • Cost reduction: Outsourcing allows you to tap into modern storage technologies and expertise without acquiring hardware, software or staff, and data storage, therefore, becomes a pay-as-you-go operating expense.
    • Enhanced connectivity: Outsourcing partners have access to multiple network connectivity options, and can therefore help you choose the best one to ensure high-speed connectivity.
    • Improved focus: With the peace of mind that data infrastructure is reliable, and that maintenance and updates are taken care of, organisation can focus on their goals and deliver better outcomes as a result.

    Start outsourcing your data storage

    In today’s competitive business landscape, no organisation can function effectively without data. However, as we have covered above, there are multiple problems associated with big data, including:

    • Decided which data technology to use
    • Skills shortage
    • integration problems
    • Security
    • Storage issues

    Fortunately, organisations can mitigate most of these challenges by their outsourcing data storage requirements. With the right solution, businesses can reap the benefits of secure and accessible data systems at a reduced cost.

     At Nexstor, we work with you to make the data outsourcing process as smooth and painless as possible. We’ll help you build efficient big data solutions — from figuring out your requirements to migration and storage. Contact us today to start a conversation around your data storage needs.

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    Troy Platts

    Troy has spent over 20 years helping organisations solve their data, storage and compute conundrums. He is a regular speaker at vendor events and spends any free time he has keeping abreast of advances in data platform technologies. He also makes a mean curry.

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