Turning a Sparse Data Problem into a Big Data Solution: An Overview

The digital universe is expanding by 40 per cent annually and includes all things online and all things Internet. This report suggests data volume double in size every two years. However, sparse data remains a real challenge for organisations. While everybody out there wants to turn their sparse data problem into a big data solution, it is the data mining that exploits the big data for insights, value and analysis.

Besides, not all data is created equal. Some are dirty and incomplete. Maybe some parts or all of it is misleading and is impacting how you see your business.  Data is ever-growing and big data, well, is even bigger. Data mining is the virtual sieve that filters the information and finds out the proverbial needle out of the haystack.

…And it is a lot of work. A data scientist spends 50 to 80 per cent of their time to clean the backyard and clear the bottlenecks out of the system and give you the data that is usable and which, businesses can use to make decisions. Many businesses work in a system that can be defined as a sparse data environment, which impacts machine learning adversely. Data or the lack of it affects the performance of algorithms thus, resulting in the miscalculations, defeating the very purpose of deploying machine learning. It is where data solution such as mining and data scrubbing come into the picture. The information within the dataset is corrected, added, modified and verified.

Why do you need to know about big data?

Because data intelligence and data-driven culture are here to stay and in all probability, your competitors have been early adopters of big data and already on their way to fill the knowledge gap created by sparse data.  The world of big data is expanding and bringing significant changes the way businesses operate and market their products.

If you want to explore the opportunities and benefits offered by big data adoption, scroll down to know it all!

What is data?

(Let’s start with basics!)

Data is an individual unit of information. The dictionary defines it as a term used to denote characters, quantity or symbols with which operations are carried out on a machine. It is stored and transmitted as electrical signals and recorded on mechanical, optical or magnetic media.

What is Sparse Data?

Data is considered sparse when certain information within the dataset is missing.

Jerry Gentry and his colleague John Burke coined the term Sparse Data. According to him, sparse data is less visible because organisations don’t find the need to discover value and extract information within it. It is equally important as big data for a business to make decisions and pave the way forward. Gentry thinks that businesses need to look at both big data and sparse data to see the whole picture and make decisions.

However, businesses often make the mistake of focusing on data solution such as Enterprise Data Architecture to store the big volumes of data but overlook the critical aspect of deriving value from data.  They forego certain elements such as web analytics, customer journey mapping and insights into customer behaviour that can give them a competitive advantage and better understanding of the market. Little do they realise that less is more.

What is big data?

Big data is the data that is ‘big’ in size! The big data grows by the minute and is layered and complicated to the extent that traditional data tools can’t effectively deal with it. When accessed and interpreted, it can be beneficial for companies all over the world.

 The term big data was coined a few years ago, and the concept of big data is still in its nascent stage. However, large data isn’t new. It existed earlier but rarely did people know how to make it valuable. Thanks to the advent of IoT and data solutions such as data cleaning, businesses are waking up to the power of quality and clean data.

Some everyday examples of big data are:

      One terabyte of new data is generated every day at the New York Stock Exchange.

      Facebook alone produces more than 500 terabytes of data every day in the form of messages, videos, images and comments.

      An airport produces data in petabytes from many thousands of flights.

In 2001, Gartner described big data as the data that contains greater variety arriving in increasing volumes and with ever-higher velocity. The Variety, Volume and Velocity are referred to the three Vs of big data.

  • Volume: If we are talking about big data, it has to be big. Working with big data implies huge volumes of unstructured data of known or unknown value ranging from tens of terabytes to hundreds of petabytes. It could come from Twitter feeds, a mobile app, clicks on a web page or sensor equipment. ‘Volume’ is the only criteria that categorise data as big data.
  • Velocity: The rate at which data is being received and processed is called velocity. The processing rate is used to evaluate the usability of the data. It is operated in real-time or near real-time from networks, app logs, business processes, sensor-enabled equipment, smartphones, websites and social media channels.  This data can be evaluated or acted upon in real-time with IoT products.
  • Variety: The types of different data are called variety. Data is no longer limited to a spreadsheet or linear database. It now originates from different diverse sources and poses challenges in storage, data mining, assessing and processing. Big data is found in three different forms, based on their format-

Ø  Structured Big Data: You can define parameters and a pattern for structured data. It can be processed, stored or accessed in a structured manner. A database of the employees in the HR is called structured data that can be classified into parameters like Employee ID, Employee Name, Joining Date, Date of Birth, Gender, Salary, Department, Hierarchy and Salary.

However, the large quantities of data get complex over time and soon grow to be unstructured or semi-structured.

Ø  Unstructured Big Data: The unstructured data is complex, huge and unknown. It is the uncharted territory that comes with a multitude of challenges in terms of processing, accessing, storing and deriving any output out of it. The unstructured form of data comes from a variety of sources and could be anything such as audio files, text files, images, videos, vectors, and gifs and so on. Another example of big unstructured data is web applications. In today’s data-oriented world, it is the most challenging issue for the organisation as they sit on huge loads of information but are unable to make anything out of it due to its raw and unstructured pattern. For instance, a search request output by Google is the classic example of unstructured data.

Ø  Semi-Structured Big Data: The semi-structured data lies somewhere in between the traditional and unstructured data.  It is complex and layered, however with additional pre-processing and support metadata can be fit into a relational pattern. The data in an XML file is a relatable example of semi-structured big data.

<rec><name>John Doe</name><gender>Male</gender><age>33</age><DOB>2.1.1987</DOB></rec>


The rapid change in data procurement and processing has forced the data engineers to bring another ‘V’ to this mix, which is Variability. It is a significant factor considering data processing has always been inconsistent and there is a difference in the processing and managing of the data.  Two other factors, Value and Veracity, have emerged as two big Vs with time.

Data is of no use unless it has some value. Businesses sit on loads of data that leads to nowhere because sometimes it is duplicated, repetitive and mostly, inaccurate. Having data and making sense of it are two different things and this is why businesses often need data solution to discover its value.

Big data is an asset. It is the capital of the biggest tech companies that are developing new products and have data driving major business decisions.

With technological breakthroughs like data mining, data computing is easier and cheaper than ever. Such data solution techniques help the decision-makers to access and standardise semi-structured and unstructured big data concerning a business process. It adds value and brings gravity to the data-which can be used to enhance a business.

It helps the stakeholders and business leaders with insightful analysis, patterns and predictive purchase behaviour while enabling them to change the existing business processes that are draining money and resources.


What improvements do the right big data bring to your business?

Businesses can reap a whole range of benefits with big data solution such as:

ü  Having access to various streams of ever-flowing data of Facebook, Twitter and other social media channels, apps and web links that help them know what their customers are talking about!

ü  When coupled with new techniques such as CRM data cleaning, businesses open new avenues to insightful learning and operational efficiency. They can learn about customers’ behaviour, purchase triggers and expect to understand the market better.

ü  The quality and standardisation of data across the departments help in seamless operations within multiple business processes.

Big data can help businesses plan a range of decisions. Some of the use cases are mentioned below:

  • Product Development and Analysis: Big data is used by Netflix and P&G to analyse and anticipate customers’ preference. The existing information of customers, current products and commercial success of a product is used to build predictive models. P&G utilises data and analytics to roll out new products. Thanks to it, the Netflix Cassandra model is quite scalable and can start operations anywhere in the world.
  • Predictive Mechanical Models: Both structured and unstructured data can be used to predict mechanical failure in equipment.
  • Customer Experience: Businesses have lots of customer data. Data mining is used to discover useful patterns within large data sets collected from social media, call logs and web visits. This helps a business to identify targeted customers, understand dropouts and deliver personalised offers. Customer intelligence remains the top priority for major businesses, and big data adoption can help!
  • Security Lapses: In this digital world, security is a major concern. Big Data helps businesses identify rough patches that lead to security issues. It also helps to dig deeper into large data sets to understand security loopholes and detect fraud in time.
  • Machine Learning: Machine Learning is the hot keyword these days, and big data is the stepping stone to train machine learning models.
  • Process Efficiency: Big data drives operational efficiency within a business. The tools and methodology of data management tools such as data mining and data cleansing analyse and assess customer feedback, identify targeted customers and their information that let businesses align their processes and optimise for maximum efficiency. Businesses use these actionable insights to predict the market and introduce a new product.
  • Powering Innovation: Big data answers questions like ‘what does your customer want,’ ‘how much they can pay’ and ‘why are they dropping out and not completing the purchase?’ Since data mining services helps businesses to understand markets, targeted customers and financial scenario, the big data becomes a tool to improve business decisions.


In real life, most companies have volumes of data to be dealt with but don’t have access to advanced analytic data solution tools such as CRM cleaning. Or they have access to sparse data, which is not standardised and have zero visibility in terms of driving business decisions.  Often businesses buy syndicated data from different sources that bring in volume but they have little to zero control over the information. The third-party database is one-size-fits-all and isn’t customised to a business’ needs or issues. It is often a standard issue that needs to go through a range of data techniques, tools and algorithms to stay current, updated and relevant.

Neither volume nor inaccuracies help. Businesses don’t need more and more data. Instead, they should focus on better data that has value and which, can be used to derive business decisions. It is the analysis and the quality of data that should be the prime concern of a business!

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