HashCash Big Data
HashCash Big Data Services •
Infrastructure Set-up and Support
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Development, Maintenance and Support
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Analytics and Insights Services
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Consulting
Benefits •
Reduces Cost: Big data tools such as Hadoop and cloud-based analytics provides cost advantages in storing large amounts of data – additionally they can present more efficient ways of conducting business.
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Effective Decision Making: With Hadoop & in-memory analytics along with the ability to analyze new sources of data, organizations evaluate information immediately and make decisions on what they’ve learned.
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New Offerings: Big Data empowers corporates to provide what customers seek by
analyzing the data. Big Data Analytics assists organizations to create and offer new and multiple products to customers based on their habits.
Use Cases
Use Case #1 Implementing Big Data Solutions for a reputed
Oil and Exploration support
engineering company
Scope The central idea was to map the available mining equipment data to
demarcate points where the machines would be operating at optimal efficiency level. The core objective was to reduce the burden of the accumulating cost of oil and natural gas exploration services.
Key Challenges The data coming from the machine logs come in different formats. Some data comes in digital format while the others do not come in direct digital formats. Converting the data sets and filtering the data for analysis was one of the biggest challenges.
Solution •
Mapping data with the machine’s operating parameters to identify, peak, moderate and downtime to measure the efficiency of the instruments
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Map data sets of machinery which recorded a relatively higher amount of oil yield to mark the biggest resources
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Map data sets to optimize oil production and minimize overproduction in select locations
Benefits •
Operational efficiency
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Minimize production costs
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Optimize oil production
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Minimize the risk of accidents
Use Case #2 Implementing Big Data solutions for a
Pharmaceutical Company to make significant
progress in a genomic research project.
Scope HashCash helped the client get accurate data mapping to identify disease causing
genes. The actual process involves a complex algorithmic process to refine the data and use it to come to conclusive results.
Key Challenges •
Formulate the right computational problems to solve a biological challenge
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Develop the right algorithms to solve different computational challenges
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Evaluate the algorithms created by us for the different set
cases of data and analyze the accuracy in detecting the concerned problems
Solution
The approach taken was meticulously designed to accommodate the entire paradigm of DNA analysis •
Deciphering the messages within the DNA ecosystem
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Examining the DNA replication process which will help in designing an intelligent algorithm to refine it further down the line
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Take a step towards motif locating genes since the motifs encased in the DNA of genes needed to be located
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Roll dice to find the designs in DNA structures which was otherwise
impossible to comprehend otherwise •
Preparing the bio-informatics report for a detailed structure to assist
our client
Benefits •
The end reports provided deep insights on major health problems
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The entire paradigm for DNA genome got a boost with this program using quality algorithms
Use Case #3 Deploying Big Data Analytics Solutions for a Global electronic devices
Scope To drive clustered data sets towards
Retailer of quality
Key Challenges •
meaningful analytics through Big Data
The company was finding it very difficult to map, integrate and analyze
the data coming from multiple sources. •
They were finding it very difficult to manage data clusters collected
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The data also lacked insights which would be usable when compartmentalized properly. It was difficult for their analytics team to analyze and take meaningful business decisions based on the same.
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The clustered data sets were of no use but with tremendous potential
Solution •
HashCash offered a BI Intelligence tool capable of churning the Big Data and compartmentalize the entire data set. The segments were based on various factors like Acquisition path, First purchase, Device type, Geographical location, Income, Gender, Age, Occupation, Marital status, etc.
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Then we filtered the data on the basis of customer behavior*
Benefits •
Enhanced customer satisfaction and loyalty
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Improved bottom line
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Faster decision making
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World class product designing and segmentation
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Enhanced new market exploration
Thank you