The Analysis of Big Data: A Big Problem?

May 26, 2021

The Analysis of Big Data A Big Problem

The information age is in full swing. If you thought that oil, gold, or diamonds are the world’s most valuable commodity, then you’ve likely falling privy to flashy marketing. Today, with more users using their mobile devices and hooking themselves up to the net than ever, an abundance of data is being generated constantly. But performing an analysis of big data is no easy feat.

Every time you open an email, click on a link, view a page, get tagged on social media, like a post, make an online purchase, like a video, or purchase in-store, your data & behavior is being recorded.

Not only is customer/user data being collected, but b2b is a big proponent of big data also. ERP & CRM systems are able to generate invoices, manage supply chains and monitor price and currency fluctuations, feeding such information back to decision-makers and analysts.

Needless to say, big data is huge (pun intended). The sheer volume of data generated can be hard to comprehend. However, companies cannot understate its value. Big data creates immense value for companies which has only been expanded due to the internet of things (IoT).

Big data holds massive potential for companies since it contains the insights necessary for companies to improve their products and services. In fact, it is thought that the US health service alone could achieve an extra $300B in efficiency and health savings every year through leveraging big data.

But what exactly is big data, and how can companies today leverage it to make key business decisions?

We’ll consider the “Data explosion,” concerns companies have today, big data analysis, and how companies can use data collection and data visualization in analyzing large and big data sets to make predictions and improve their business.

Netflix, for example, was able to tap into long-tail customers to create a competitive advantage over Blockbuster by understanding big data.

The Definition of Big Data

Big data is data that is so massive in volume that the sheer act of collecting and storing it is hard. Big data can also be classified as data that grows exponentially over time. Big data analytics tools are necessary to collect and process such large amounts of data where traditional tools just won’t do.

Such a volume of data can be classified as a Data Explosion where the volume of data increases dramatically to a point where the act of capturing and analyzing said data becomes extremely difficult. It was thought that by 2020, organisations could expect a +4,000% increase in data production. If companies failed to invest in the correct analytical or data collection software, they would be at risk of becoming data-rich but without significant insights necessary for growth or product development.

A data explosion of big data could leave companies that want to collect more information about customer behavior drowning in a sea of data without actually having the capability to use it and understand market trends and improving operational efficiency.

By developing robust data management and an analytical strategy, companies can avoid this to produce smart business insights that allow them to make better decisions.

The major problem that companies have surrounding big data is 3 fold: Volume, Velocity & Variety. Volume holds the largest challenge but also the biggest opportunity.

Since big data cannot be analyzed using traditional techniques, significant quantities of data are often passed through, and potentially lucrative hidden patterns are ignored.

Using Big Data Analytics

While data is a key starting point for any business, understanding such data is where companies can develop a competitive advantage.

Data analytics allows companies to crunch specific data about their business, from inventory to employee sales performance, from customers reacting to different advertisements to targeting long-tail customers through niche marketing.

Big data analytics allows companies to improve their decision-making skills and allows them to improve leadership training, employee education, and target the right customers with pre-tested advertising campaigns.

Big data analytics can allow companies to enter markets that were previously unavailable while also providing a better understanding of how to improve their products, supply chain, operations, services, human resources, and more.

See What’s Next

Netflix, for example, was able to tap into long-tail customers – a large number of forgotten niche customers that collectively make up a big market – to create a competitive advantage over Blockbuster by understanding big data.

Big data analytics allows companies to do the same. Through using analytics, simulations, and data sets, companies can run tests on a variety of different customer types to provide profitable niche services.

However, it’s not just corporations that can have all the fun with analytics since analysing data has also been used to achieve great success in sport.


Analysing player data is a common concept today. The Major League Baseball team – the Oakland As – were one of the first professional teams to use big data analytics to upset the balance of the sport.

A lowly team with a budget 10x smaller than the bigger teams, the Oakland As accessed and analysed thousands of data points on players across the league to build a highly competitive team at a fraction of the cost of the bigger franchises.

So how can companies collect, process, and analyse large amounts of data to improve their business intelligence and customer experience?

Let’s find out.

Predictive analytics of large data sets using large amounts of data science

Collecting Big Data

Organisations can collect big data in a variety of different ways. There are a number of different sources of data rather than one single process to collect data.

Data sources include;

  • App Downloads
  • In-Store Traffic Monitoring
  • Surveys
  • Online Tracking
  • Social Media Monitoring
  • User Behavior
  • Transactional Data Tracking
  • Ad Monitoring
  • And more

When collecting data, it’s necessary to understand the two different types of big data.

Structured and Unstructured Data

Structured data is collected in a predefined format. Highly specific and stored in data warehouses, structured data includes things such as spreadsheets, point of sales systems that use barcodes and collect information on quantity.

One of the biggest advantages of structured data is that it can be used by machine learning and artificial intelligence development algorithms.

Given the way that structured data is stored in data warehouses, it allows for easy manipulation and querying of such big data.

However, data warehouses do not have the advantage that data lakes do in that they are easily manipulated and updated if necessary.

Should changes need to be made to the existing structure of data warehouses, then the entire data set may need to be updated, which can take up a lot of time and resources.

Unstructured data, however, is an amalgamation of many different types of data. Unstructured data is stored in its native format, often in a data lake. Unstructured data is not processed until it is used, which is known as schema-on-read.

Appearing in various file formats such as email, social media posts, and chats, unstructured data allows companies to collect valuable data information to be processed later.

The evolution of cloud computing has allowed cloud-based data lakes, which generate massive data storage capabilities and cost savings since a cloud-based data lake allows companies to pay-as-you-use, assisting companies to scale.

When it comes to collecting structured and unstructured data, companies should consider the pros and cons of each, along with their specific data sets and abilities to process such data.

While unstructured data can have cost-saving advantages and massive opportunities hidden within, analysing such data takes a keen eye and a high level of skill.

Structured data, on the other hand, can be analysed by the average employee.

Processing Big Data

Big data processing can be defined as “a set of techniques or programming models to extract useful information from big data sets for supporting and providing decisions.” Often characterised using the three Vs.:


Defines the amount of data produced or processed. Traditional data is measured in bytes (with most personal devices needing space for multiple terabytes of data).


The speed at which data are generated and processed (bytes per second).


Gives information on the diversity of data that are collected. This covers data format and structure.

However, besides these three Vs., two more characteristics have evolved that are frequently referred to when discussing big data processing;


Denotes the quality or actual trustworthiness of the data sets. For example, damaged data or incorrect values may harm the validity and authority of data sets.


Corresponds to the actual meaning of big data. For example, data on customer satisfaction are very valuable for a company.

Can We Process Now?

Yes. Now that we understand how we collect data, we can consider 2 major processing options.

The first is batch processing.

Batch processing looks at large data blocks over time and is most useful when a longer turnaround time is available between collecting and analysing big data.

The other processing option is Stream processing, which considers smaller batches of data, which reduces the delay between collection and analysis.

Stream processing is responsible for quicker decision-making. However, it comes with a price and is more complex than batch processing.

Big Data implies typically high volume that is frequently updated and involves a variety of data formats. Therefore cleaning and analysing need to occur before big data can provide value to organisations.

Cleaning Big Data

Big data, without cleaning, is ultimately a jumble of noise that does not make sense. It’s impossible to truly understand the value of data if the data quality is low. Raw data, especially unstructured data collected in real-time, is useless.

Data cleansing or scrubbing, therefore, is the necessary procedure of correcting or removing inaccurate and corrupt data.

Data scrubbing and formatting allow companies to get better results. All duplicate, irrelevant or unnecessary data must be removed since “bad” data can lead to poor insights and business intelligence being misrepresented.

When it comes to cleaning data, organisations can also consider eliminating such data that is not necessary for certain business decisions. The more streamlined and clean data sets are, the fewer opportunities arise for distractions or bad decisions made using dirty data.

When it comes to cleansing big data, given the vast majority of unstructured data that comes into companies collecting big data, it is useless for data scientists unless it is all formatted correctly.

Big data and analytics are unequivocally important for business, but data science is where business owners can find the real value. If you avoid cleaning your data, you can forget the science, even if you have the very best data analytics tools at your disposal.

Wrong data can force a business to make wrong decisions, conclusions, or poor analysis, especially when huge quantities of big data are considered.

The history of failed businesses is littered with companies that have lost money due to the volumes of bad big data.

Analysing Big Data

Now that we’ve collected, processed, and cleaned our data, we can start to analyse. Big data analysis occurs through using advanced analytics processes, which can turn that all-important big data into even bigger insights. Three major big data analytics processes are;

1. Data Mining

The process of sorting through large datasets in order to find hidden patterns and recognise relationships. This process works by turning raw data into information that is useful.

Data mining is often done by software that looks for patterns in large data sets to learn more about a company’s customers.

This allows a business to improve their marketing strategies, decrease their expenditure by removing irregularities or anomalies, and increasing sales.

2. Predictive Analysis

Predictive analytics uses the historical data of an organisation in an effort to make predictions about the firm’s future.

This is especially valuable when performing a SWOT analysis (Strengths, Weaknesses, Opportunities, and Threats).

Through using a combination of data mining, statistics, matching learning, and artificial intelligence, organisations can perform data analysis to make predictions.

Predictive Analytics, therefore, is a vital component to understanding market trends and allows companies to optimise their resource management.

3. Deep Learning (Machine Learning)

A part of machine learning in artificial intelligence, Deep Learning is able to process data sources whether such data is unstructured or unlabelled.

With specific knowledge needed to truly harness the application and understanding of big data, the question could be asked then, is it worth it?

Big Data Analytics Tools

Because of the sheer amount of data available to companies, it is impossible to choose one tool to perform big data analytics.

Alternatively, companies will often use several tools to collect, process, clean, and analyse big data.

Step up big data technologies.

Some of the bigger players in the big data analytics tools industry can be found below;

  • Hadoop: The kingmaker when it comes to big data processing and collecting. An open-source framework that is known for efficiently storing and processing huge amounts of data. For software that can handle vast amounts of structured and unstructured data, this one is a no-brainer for any company looking to use big data analytics.
  • Tableau: As one of the world’s leading analytics platforms, Tableau allows organisations to perform data analysis and predictive analytics while also allowing users to collaborate and share their big data insights, important especially when working with large amounts of data. What sets Tableau apart is the visual data analysis that comes with the platform, which promotes collaboration across organisations allowing people to ask questions of big data and easily share their insights using big data analytics.
  • YARN stands for “Yet Another Resource Negotiator.” This cluster management tech is great at analysing data that helps companies with job scheduling and resource management.
  • Spark: Another open-source cluster computing framework. Spark can handle both batch and stream processing for fast and efficient computations when working with large data sets.
  • NoSQL databases: These are great options for collecting big, unstructured, and raw data. NoSQL stands for “Not Only SQL” and is great at handling low data quality from a variety of data models.
  • MapReduce: As part of the Hadoop (see above) framework, MapReduce is known for serving two key functions. Firstly, Mapping, which is the process of filtering data into various models. Secondly, reducing or the art of organizing and reducing results from a node in order to answer a query.
big data technologies used to process data through advanced analytics and involved in data storage

Benefits & Advantages of Big Data Analytics

Big data analytics can provide a large barrier to entry for any startup or small to medium-sized business.

With specific knowledge needed to truly harness the application and understanding of big data, along with business decisions being potentially led astray due to uncleansed, unprocessed, and unfiltered data, the question could be asked then, is it worth it?

The benefits of managing, collecting, processing, and analysing big data are unquestionable if done right. Let’s look at 5 major benefits;

  1. Cost reduction. Big data technologies, such as those discussed above, along with the introduction of cloud-based analytics, bring business users significant cost-reductions when it comes to storing large reams of big data. A lower-cost is associated with storage, but big data can identify more efficient ways of doing business, which can have significant impacts on the bottom line.
  2. Better decision-making. The ability to make faster, better decisions are becoming more valuable in a shrinking world. Customers and business users expect instant results. Therefore, businesses can analyse key information immediately and make big decisions based on knowledge acquired from data collected, processed, and analysed.
  3. Develop new products or services. Big data gives organisations the ability to gauge changing and developing customer desires. The application of big data analytics has allowed companies to create more products in line with what the customer actually wants. One could argue that big data is what has given rise to tech behemoths today who seemingly generate successful products and services frequently with ease.
  4. Focused & Targeted Campaigns. The concept of sending targeted ads to internet users today is nothing foreign. However, such campaigns are gold mines for companies who use big data analytics to deliver tailored products to their targeted market. Gone are the days of spending millions of dollars on advertising campaigns that don’t work. Buy a billboard for a hundred thousand dollars? Why, when you can target your ideal customer on Facebook or Google for a fraction of the cost? Big data insights allow companies to create successful, focused, and targeted campaigns that help improve brand loyalty.
  5. Risk Management. Companies today are able to withstand and operate even in high-risk environments. However, big data has been an instrumental reason why organisations are able to do this. Big data analysis allows firms to improve the effectiveness of their risk management models and therefore develop better and smarter strategies.

Big Data Challenges

Despite the fact that big data can provide companies with a wealth of opportunities, it does not come without its challenges;

1. Lack of proper understanding of Big Data

Big data can be intimidating for organisations that do not have the resources to properly collect and perform an analysis of it. Bringing in dedicated data scientists and business analysts who know what patterns to look for and how to use big data to the company’s advantage is key.

2. Big Data Tool Selection Confusion

Ah, the age-old issue of analysis paralysis. Choosing a big data analytics tool is no easy feat since it depends on what you plan on doing with your big data. However, hiring a dedicated analyst with experience in a particular tool can easily help solve this issue.

3. Securing data

As we’ve spoken about above, collecting the right data is often part of the problem. Do you collect structured or unstructured data, and how do you do it? Surveys, statistics, in real-time? Work to understand what type of data is going to be more beneficial for your business before you try and collect it.

4. Integrating data from a variety of sources

Big data can come from all angles, which can be overwhelming. However, while collecting data is important, being able to integrate big data from different sources is the real issue. Work with your big data analytics team to understand how you can solve this.

Why You Should Start With Big Data Analytics

Big data is already here, and companies that want to get ahead and develop a competitive advantage must develop a strategy to not only collect such data but interpret and analyse it as well.

Any company with plans to scale and remain competitive cannot afford to let critical customer data float by unnoticed. By understanding how to analyse and use big data to make better decisions, companies and leaders can consistently build a sustainable competitive advantage. 

Big data management & product development using terabytes of data

About the author: Joe Silk -

Joseph is a Start-up Consultant, Copywriter & Business Owner with 9 years of PQE. He is extremely client-centric, able to work on a wide range of topics and deliver high-quality standards on projects of all sizes for clients all over the world. View on Linkedin