Big Data Processing

Big Data Processing – Use Cases and Methodology

The introduction of big data processing analytics proved revolutionary in a time when the quantity of data started to grow significantly. One scale to understand the rate of data growth is to determine data generated per second on average per head. While it is true that a proportion does not have access to the internet, most internet users generate more than this average. Thus, the net generation currently stands at 1.7MB per second per person.

Why Choose Big Data Analytics over Traditional Data Mining?

Traditional mining involving data warehouse (DWH) was the approach used for data analysis of all scales before the advent of big data. The introduction of frameworks, technologies, and updates in them are making big data analytics the best approach for data analysis on datasets whose size amounts to terabytes.

Data analysis time reduction

Traditional data analysis using extraction, transformation, and loading (ETL) in data warehouse (DWH) and the subsequent business intelligence take 12 to 18 months before the analysis could allow deducing conclusive outcomes. In sharp contrast, big data analytics roughly take only three months to model the same dataset. Big data also ensures excessively high efficiency which DWH fails to offer when dealing with extraordinarily large datasets.

Data analysis cost reduction

Traditional data analysis costs three times as much as big data analytics when the dataset is relatively large. Besides cost, big data also ensures significant return on investment because big data processing systems used for analytics including Hadoop and Apache Spark are proving to be highly efficient.

Notable Use Cases and Industries for Big Data Applications

Understanding loopholes in business

It is often the case with manufacturers as well as service providers that they are unable to meet targets despite having immaculate products and unparalleled efficiency. Determine why some of the areas in your business model lack expected output while others continue to generate more than anticipated.

By utilizing big data processing for large scale businesses, companies can perform quantitative as well as qualitative risk analysis with far less resources of time, money, and workforce.

Future forecast

Predict with high precision the trends of market, customers, and competitors by assessing their current behavior. It is notable that this prediction is not speculative. Rather, it is powered by real-world records. There are various channels used for data sources depending on the underlying industry. Social media is one of the top choices to evaluate markets when business model is B2C.

Strategic decision making

Crucial corporate decisions should not be based on hit-and-trial methods. Instead, you need to analyze market and streamline future goals accordingly.

Business landscape is changing rapidly in the current corporate sector owing to the growing enterprise mobility technologies and shrinking cycle of innovation. Big data processing analytics provide insightful and data-rich information which boosts decision making approaches.

Understanding customers

The amount of new and retained customers in a time period projects the potential of a business. Customers carry various motivational factors to prefer one product over another. Instead of interviewing the potential customers, analyzing their online activities is far more effective.

Thus, big data management and processing allows you to determine the path that a customer chooses to reach you – or, for that matter, to reject you.

Realigning Marketing

Before big data was a thing, the enterprises used to perform post-launch marketing. However, this strategy involves significant risks because the product or service might not be as appealing to customers as to you. The leverage of big data analytics in support of decision making process enables companies to perform marketing prior to the launch. Consequently, they can introduce need-based products and services which are highly likely to ensure achieving targeted revenues.

Sentiment Analysis

The big data does not only provide market analysis but also enables service providers to perform sentiment analysis. Using this technique, companies can identify context and tone of consumers in mass feedback.

Optical character recognition in combination with big data processing in image processing also assists in sentiment analysis. Apart from social media, the public relation sites are also sources to collect data for such analysis.

Intelligent algorithms are capable of performing this analysis by themselves – a technique usually referred to as supervised machine learning. In other words, companies no longer require multiple human resources to evaluate each feedback.

Fraud Detection

Big data enables banks, insurance companies, and financial institutions to prevent and detect frauds. The traditional methods to detect financial frauds occurring with credit cards present a dilemma here. A company can either provide unhindered and streamlined experience to its customers or it can ensure security at the cost of miserable experience.

Big data analytics allow ensuring seamless customer experience as well as security at the same time. Using big data analytics, companies have been able to markedly bring down fraudulent transactions and fake claims.

Disease Detection

It would be astonishing if you are still unaware of the revolution that big data is causing in the healthcare industry. The technology in combination with artificial intelligence is enabling researchers to introduce smart diagnostic software systems. Big data medical image processing is one of the most mentionable examples.

Besides, it also allows software to prescribe medicine by assessing patients’ history and results of relevant tests. These capabilities are significantly bringing down the cost of operations.

Mob Inspire Methodology for Big Data

Mob Inspire uses a comprehensive methodology for performing big data analytics. The experience of working with various industries enabled our experts to work on a range of tasks. The variety of tasks posed occasional challenges as well when we had to solve a problem which never occurred before.

However, the professionals did not only remain successful but developed enterprise level big data framework too. This framework allows them to revisit documented cases and find out the most appropriate solutions.

Data Extraction

Big data often requires retrieval of data from various sources. While the sources vary depending on the project, yet social media and search engine queries are the most widely used sources. Banks use transaction records for fraud detection whereas healthcare companies use data regarding patient’s medical history to train software for intelligent diagnosis and prescription.

The companies providing video on-demand (VOD) services acquire data about users’ online activity. This data enables providers to determine consumer’s choices so that they can suggest them the relevant video content. Companies utilize their own enterprise data to make strategic corporate decisions.

For instance, a construction company aiming to optimize resources would acquire data of a range construction project and process them to find out the areas where cost and time consumption can be minimized.

Thus, data extraction is the first stage in big data process flow. The retrieved data is placed in a repository technically referred to as Data Lake. It is notable here that big data analytics require unstructured data – the kind whose data does not exist in schema or tables. Instead, it is stored in flat hierarchy irrespective of data type and size.

Data Cleansing

A data lake is a container which keeps raw data. The process of data cleansing provides appropriate filters to ensure that invalid, relatively older, and unreliable data filter filters out before latter stages big data processing.

Data reliability implies the sources from which you acquire datasets. For instance, you may require electronic healthcare records (EHR) to train software for automatic prescription and diagnosis. A collection of fake EHR would spoil the training of AI resulting in exacerbating the automation process.

Data currency indicates how updated is the dataset. Data has to be current because decades-old EHR would not provide appropriate information about prevalence of a disease in a region. For instance, only 1.9% of people in the US had macular degeneration. This percentage is projected to grow beyond 5% by 2050. Using the data from 2010 to perform big data analytics in 2050 would obviously generate erroneous results.

Validity of data explains its relevance in the problem at hand. For instance, a taxi business aiming to determine consumer behavior would assess people who travel by taxi or another ride-hailing service. It would be inefficient to consider people who commute by public transport. Developing and placing validity filters are the most crucial phases at data cleansing phase. Thus, cleansing is one of the main considerations in processing big data.


This phase involves structuring of data into appropriate formats and types. The data acquired and placed from various sources into Data Lake is unstructured. There is no distinction of types and sizes whatsoever. Many analysts consider data cleansing as a part of this phase. However, Mob Inspire treats data cleansing separately due to the amount of tasks involved in it.

The cleaned data is transformed with normalization and aggregation techniques. Transformation makes the data more readable for the big data mining algorithms. For instance, if the data has a broad range, it is plausible to convert the values into manageable equivalents. This transformation process is performed again once the mining is done to turn the data back into its original form.

Training with Machine Learning

This phase is not an essential one but applies to a range of cases making it significant among big data technologies and techniques. Machine learning involves training of software to detect patterns and identify objects. However, ML is must when the project involves one of these challenges. ML can be either supervised or unsupervised.

Supervised Machine Learning:

It refers to the approach where software is initially trained by human AI engineers. They ensure to place certain bounds (bias) so that the outcome does not exceed the logical range. Supervised ML is the best strategy when big data analysts intend to perform classification or regression.

Classification is the identification of objects. Software trained to perform this recognition has to decide, for instance, if an object visible in a frame is an apple or not. The system would generate a probability based on the training provided to it making it a crucial phase in big data processing pipelines.

Regression is performed when you intend to draw pattern in a dataset. For instance, determining the behavior of financial stocks by analyzing trends in the past ten years requires regression analysis.

Unsupervised Machine Learning:

Unsupervised ML implies the approach where there are no bounds and the outcome can be as unusual as it can. This ML provides more flexibility is pattern identification because it does not have limitations on the outcome. Unsupervised ML also considers extremely unusual results which are filtered in supervised ML making big data processing more flexible.

Clustering is one significant use case of unsupervised ML. The technique segments data into groups of similar instances. Thus, members of the same group are more similar to each other than those of the other groups. There are usually wide ranging variables for clustering. Association is the other instance which intends to identify relationships between large-scale databases.

Many projects require reinforcement learning which refers to the technique where a software system improves outcomes through reward-based training.

Segmentation and Visualization

The outcome of ML provides distinctive groups of data regardless of the technique you use. These groups are run through more filters, at times, if needed. The phase of segmentation nurtures data to perform predictive analysis and pattern detection. One notable example of pattern detection is identification of frauds in financial transaction.

The segmented results essentially take the form of relational databases. At this point, data scientists are able to visualize results. Datasets after big data processing can be visualized through interactive charts, graphs, and tables. The result of data visualization is published on executive information systems for leadership to make strategic corporate planning.

Tool, Technologies, and Frameworks

Mob Inspire uses a wide variety of big data processing tools for analytics. Our experts use both Hadoop and Apache Spark frameworks depending on the nature of problem at hand. They have expertise on big data programming and scripting languages including R, Python, Java, and NoSQL. Mob Inspire use SAS and Tableau for visualization.

Big data technologies

Big data analytics take your enterprise to unimaginable heights in incredibly short time – provided the analysis is correctly performed. We utilize multiple big data processing platforms depending on the nature of tasks. Contact us to share the specific business problem with our experts who can provide consulting or work on the project for you to fulfill the objectives.