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In 2021, the size of the big data analytics market was approximately $240 billion, and it is projected to grow to $655 billion in 2029. Big data is everywhere be it in healthcare, marketing, finance or social media.

Our reliance on modern technology means that large unstructured data are being generated and delivered in real time across multiple channels. Without data quality processes like data matching or data scrubbing in place, it can be difficult to keep tabs on big data.

What is big data?

In 2023, the world generated 120 zettabytes, an increase of 23 zettabytes from the previous year. If you were to store this much data in DVDs, you could cover the earth with a stack of discs 152 times!

Organizations and customers generate tons of data every day. Data that is huge in volume and comes in many forms and sources. Hence the name big data.

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Sources of Data Generation

How does your business generate big data?

  • Data created by employees
  • The inventory and timelines on supply chains
  • Your marketing campaigns
  • The finance teams in charge of payroll

But here’s the thing. Big data cannot be analyzed using traditional methods, given the speed, size and complexity of the processes in play. It requires specialized software, like data quality tools, that can help you get your data to make sense and accessible.

Which brings us to big data analytics.

What is big data analytics?

By itself, big data in its raw form can be too much to handle. To be able to make use of it, your data needs to undergo a process of:

big data analytics
The process of turning big data into usable data

Data Collection

No two companies collect data the same way. Depending on your company and industry, you need technology and tools to gather structured as well as unstructured data from multiple sources. These sources could be anything from mobile apps, web traffic, even cloud storage.

Data Processing

Once the data has been collected and stored, it needs to be organized. Doing so will help your prospects get accurate results based on user queries. Since data keeps growing exponentially, companies need data quality tool to ensure that the system doesn’t contain duplicated or redundant records.

Data Cleaning

To improve data quality, big data as well as small data needs to undergo scrubbing. Data scrubbing (or cleaning) is the process of eliminating data duplicates, and irrelevant information, and free of formatting errors. It must be free of dirty data.

Data Analysis

As a rule of thumb, transforming big data into a usable state requires a lot of processing and time. This process gets more cumbersome if done by manual methods. A no-code data quality tool like WinPure can help you make sense of complex datasets faster than ever. It relies on a proprietary data matching algorithm to whip your data into shape.

Once you’ve turned your data into a usable entry, you can plug it into big data analytics software for various applications:

  • Business intelligence solutions
  • CRM analytics
  • Workforce analytics
  • Compliance analytics
  • Credit risk management

What about the role of advanced data matching for big data?

Data matching, the process of identifying and linking records that refer to the same entity across datasets, has undergone a remarkable evolution, particularly in response to the challenges posed by big data’s place in the modern economy. 

From traditional methods to advanced approaches, the landscape of data matching has continuously evolved to address the increasing volume, velocity, and variety of data as it exists today.

Traditional Data Matching Methods

In the early days of data management, data matching primarily relied on manual or rule-based approaches. Human intervention was often required to manually compare records and identify matches based on predetermined rules or criteria. While these methods were sufficient for relatively small and structured datasets, they quickly proved inadequate as data volumes grew and became more diverse.

Limitations of Traditional Matching Techniques

Traditional data matching techniques faced several limitations, especially when confronted with large-scale and diverse datasets characteristic of the big data era. Some of the key challenges included:

  • Scalability: Manual or rule-based matching methods are not scalable to handle the massive volumes of data generated in today’s world. Processing large datasets requires significant time and resources, often leading to inefficiencies and delays.
  • Complexity: As datasets become more varied and heterogeneous, traditional matching techniques struggle to handle the complexity of matching records with different structures, formats, and levels of granularity.
  • Accuracy: Manual matching processes are prone to human error, leading to inaccuracies and inconsistencies in the matching results. Moreover, rule-based approaches lack the adaptability to account for nuances and variations in data patterns.
  • Performance: The performance of traditional matching techniques deteriorates as data volumes increase, leading to longer processing times and diminishing overall efficiency.

The Need for Advanced Data Matching Algorithms

In response to these challenges, there has emerged a pressing need for advanced data matching algorithms capable of handling big data efficiently. Advanced data matching techniques leverage cutting-edge technologies and methodologies to overcome the limitations of traditional methods. 

Some key advancements include:

  • Machine Learning: Advanced data matching algorithms leverage machine learning techniques to automatically learn matching patterns and adapt to evolving data patterns. By analyzing historical data and feedback loops, machine learning models can improve matching accuracy and efficiency over time.
  • Probabilistic Matching: Unlike deterministic matching, which relies on exact matches, probabilistic matching algorithms calculate the likelihood of two records referring to the same entity based on various similarity metrics. This approach allows for more flexible matching criteria and improves the accuracy of matching results, particularly in scenarios involving noisy or incomplete data.
  • Parallel Processing: With the advent of distributed computing frameworks like Apache Hadoop and Apache Spark, advanced data matching algorithms can be parallelized and executed across distributed computing clusters. This parallel processing capability enables efficient matching of large-scale datasets by distributing the workload across multiple nodes.
  • Scalable Architectures: Advanced data matching solutions are designed with scalability in mind, utilizing scalable architectures and distributed storage systems to accommodate the growing volumes of data. By leveraging cloud computing resources and elastic scaling capabilities, these solutions can seamlessly handle increasing data loads without compromising performance.

How advanced data matching algorithms facilitate big data analytics?

Advanced data matching plays a pivotal role in big data analytics by enabling organizations to extract actionable insights and unlock the full potential of their data assets. 

Here’s how:

Holistic Customer 360-degree Views

What does your customer want? The answer to this question becomes clear once you have a 360-degree customer view powered by big data. This is only possible once you gain better data quality by linking related records across different data sources.

Resolving multiple identity challenges

In big data environments, where data may be fragmented and distributed across multiple systems, you get duplicated data and multiple entries for a single entity. With WinPure’s advanced data matching you can achieve accurate entity resolution, even in the presence of variations or errors in data entry.

A unified data ecosystem

WinPure’s advanced data matching solution integrates seamlessly with your existing data infrastructure, allowing you to consolidate customer data from various sources into a single, cohesive platform. With a unified view of your data, you can uncover hidden insights, identify cross-selling opportunities, and optimize operational efficiency.

An accessible no-code data matching solution

WinPure is designed to be used by anyone. With its proprietary data-matching algorithm, you can prepare, merge, clean and fix data errors without spending hours manually sifting through every data entry.

Take advantage of optimized operations and decision-making

By leveraging unified views of your data, your organization can make informed decisions and optimize operations across various functions. Whether it’s identifying supply chain inefficiencies, optimizing inventory management, or improving resource allocation, reliable data matching enhances decision-making capabilities.

To conclude – Big data analytics benefits from no-code solutions like WinPure

Big data occupies a lot of space, which means that stakeholders have to spend a lot of time searching for the right answer. Having the ability to analyze more data quickly and efficiently can mean avoiding looking for a needle in a digital haystack.

With a data quality tool like WinPure, you can analyze, clean, and match it efficiently which means that your organization can move quickly and improve bottom lines.

Apart from this, you gain these benefits:

  • Cost savings – Your organization identifies ways to do business more efficiently
  • Product development – You gain a better understanding of customer needs with actionable feedback
  • Market insights. You can track purchase behavior and market trends.

Try out WinPure today with a free trial.

Written by Samir Yawar

Samir writes about data quality challenges faced by businesses and how it impacts their day-to-day operations. His end goal - help businesses make sense of their data with WinPure's no-code platform.

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