The Challenges of Implementing Big Data

In today’s society, data makes the world go round. Big data takes management to the next level by unlocking better predictions, more effective solutions, and improved production. In theory, the more data businesses collect, analyse, and use, the better the results, but in reality, the innovative technology is much more complex. 

According to Statista, 149 zettabytes of data was generated in 2024, compared to only 83 zettabytes in 2021. Even though more data is being generated than ever before, only a small percentage is actually stored, analysed, and correctly utilised. In 2020, the global data storage capacity was only 6.7 zettabytes. Statista  expects this to grow to 16 zettabytes by 2025. So, why is it so difficult for companies to implement big data, and why should they invest in this technology?

Difference between big data and data analytics

Data analytics and big data are related but differ in scope and application. Here is a breakdown of the two concepts:

Big data 

Big data refers to extremely large and complex datasets that cannot be processed by traditional data technologies, such as data analysis, due to their nature. Big data is often described using the 3 Vs:

• Volume: Refers to the vast amounts of data generated every second from various sources, such as social media, sensors, transactions, and more.
• Velocity: The speed at which new data is generated and the pace at which it needs to be processed and analysed.
• Variety: The different types of data, including structured, unstructured, and semi-structured data, such as text, images, videos, and more.

Recently, new additions have been made to the traditional 3 Vs:

• Veracity: Due to the chaotic nature of big data, it can be challenging to produce qualitative and accurate data. The higher the veracity of the data, the more trustworthy it is.
• Variability: The goal of big data is to interpret large datasets for use within a company, but the meaning of the collected data changes constantly, leading to inconsistency.
• Value: Refers to the usefulness of the data. The goal of big data analytics is to extract meaningful insights that add value to decision-making, business strategies, or scientific discoveries.

Data analytics

By utilising a variety of tools, technologies, and techniques, data analytics transforms raw data into valuable insights that drive informed decisions and actions. This process helps businesses uncover trends, solve problems, and enhance performance, ultimately streamlining operations and fostering growth. 

The biggest difference between big data and data analytics is that the latter can be used for both small and large datasets. This means that more traditional data technologies can be employed to gain actionable insights, such as machine learning.

Advantages of Big Data

When used correctly, big data helps businesses discover new patterns in large datasets, unlocking more opportunities than traditional data technologies can. Because big data can provide real-time or nearly real-time information due to its velocity, companies can be more agile and accelerate planning, production, updates, and new launches, leading to a competitive advantage. This continuous intelligence is crucial for driving business decisions, optimising risk management, and understanding consumer behaviours and markets.

Companies can opt for data analytics, which is more cost-effective and accessible than big data, as big data requires significant investments and IT specialists with niche skills. However, by 2012, around 2.5 exabytes of data were being created daily, and this figure continues to double approximately every 40 months. Not using big data would mean that companies miss out on huge opportunities. An example is Walmart, which processes over 40 petabytes of data every day.

Examples of Big Data Use

• Tracking consumer behaviour and shopping habits to tailor product recommendations to individual customers.
• Collecting and analysing different types of data, such as weather, flight schedules, and other publicly available data, to calculate plane arrival times with great precision.
• Assessing past patient data records to plan staffing needs and resources, optimising patient care and hospital resource allocation.
• Tracking real-time payments to detect patterns and flag any fraudulent activity.

Challenges of Big Data

Although big data is a breakthrough for organisations, there are several challenges for companies to face. 

Talent and Expertise

Implementing big data solutions requires specialised skills in data engineering, data science, and machine learning. However, there is a shortage of qualified professionals with the required expertise. In the UK, for example, 46% of companies recruiting for roles requiring data skills struggled in 2019 and 2020 to fill these positions.

This problem will only grow with the increasing importance of big data in every sector, which further fuels the war for talent. Organisations can attract and retain talent by partnering with specialized recruitment companies, hiring freelancers, and offering good working conditions and corporate benefits.

Data Quality

Data is chaotic, unstructured, diverse, and (semi-)unstructured, which can lead to issues like inconsistencies, inaccuracies, and incomplete records. This can result in unreliable insights and flawed decision-making if not properly addressed.

According to a 2023 survey by Statista, the majority of businesses prioritised data and analytics. However, only 37 percent said that their efforts to improve data quality had been successful. As data continues to grow in scale and variety, the chance of noise also increases, leading to inaccurate and irrelevant information, and ultimately to poor decision-making.

Investment in infrastructure

Before big data can be analysed, it first needs to be stored and processed by a robust infrastructure. This can be tricky, as big data is constantly changing and growing. The immense demands of big data in terms of storage, processing, and network resources are too high for traditional systems. Organisations need to seek scalable and distributed options, such as cloud platforms and data lakes, which can be costly. That's why some companies focus more on BI improvements,cloud improvement and automating processes. Nevertheless, if the data environment of a company is big enough, investing in big data is the best next step.

There are also additional costs associated with big data infrastructure, such as data security measures and high-performance computing resources, like distributed networks. Implementing big data begins with the proper tools and infrastructure, which can be a challenge for companies that lack the right specialists or resources to manage it.

Data Storage

Even if a company has the right infrastructure, data storage presents another challenge, such as data silos. Data silos occur when data is fragmented across different departments, systems, or platforms, making it difficult to consolidate into a unified view. This fragmentation leads to inefficiencies in accessing and using data, while the lack of interoperability between systems and tools further hinders the ability to extract insights across diverse datasets.

Specialists such as data engineers can guide the company through complex data silos, creating better collaboration and improved access to insights across departments and systems.

Security and Privacy

Big data often includes sensitive personal or business information, which increases the risk of data breaches and cyber-attacks. Implementing robust security measures, such as firewalls, data security audits, and access control, is crucial to reducing the risk of data breaches. According to IBM’s Cost of a Data Breach Report, a data breach in 2023 cost $4.45 million, which is an increase of 15% over three years.

Many countries and institutions have implemented data protection laws to safeguard individuals' personal information. Examples include The General Data Protection Regulation (GDPR) and Principal data protection legislation. Nevertheless, big data elevates cybersecurity to the next level due to the complexity and size of the data.

Complexity and Interpretation

Because of the nature of big data (the 3 V’s), it’s impossible to implement it with traditional data analytics. The massive volumes, real-time processing, diverse data types and formats, quality issues, storage needs, and complex data laws demand specialised tools and algorithms to interpret big data.

Inappropriate models can lead to inaccurate insights. Even with the right tools, the volume and complexity of the data can make analysis overwhelming. Techniques like machine learning and artificial intelligence require careful tuning and validation to ensure they provide useful insights. Extracting actionable insights from large and complex datasets is often not straightforward and requires advanced statistical and data mining techniques.

Consequences of the IT Skills Gap

The war for talent has made it difficult for employers to find the right fit for big data job roles. A recent International Data Corporation (IDC) survey revealed that nearly two-thirds of North American IT leaders reported that the lack of skills has led to missed revenue growth objectives, quality issues, and decreased customer satisfaction.

This number is expected to increase by 2026. IDC predicts that by 2026, over 90% of organisations worldwide will experience the impact of the IT skills crisis. This skills gap is projected to result in losses of approximately $5.5 trillion, caused by product delays, reduced competitiveness, and lost business.

Find the Right IT Specialist

Although big data comes with many challenges, the technology is crucial for the growth and innovation of a company that wants to be at the forefront of its sector. However, to gain that advantage, big data needs to be implemented and managed by specialists with the right skills and knowledge.

IT recruiters from specialised recruitment organisations like Amoria Bond have access to a large global pool of technology experts in big data, which can be helpful for companies struggling to attract and retain specialists with the necessary data skills.
 With an NPS score of 63 and an award-winning strategy, our recruiters find niche IT specialists for both contract and permanent roles! Because our purpose is to progress lives everywhere, here are some key numbers:

• 360° staffing service solutions
• 48-hour contract delivery
• 14-day permanent delivery
• 90 awards and accolades won

Do you want to implement big data but don’t have the right specialists? Contact our global team to discuss how we can help you with your hiring needs.