Big Data Engineering

In today’s digitally connected world there is a huge amount of structured, unstructured and semi structured data (so called Big Data) carrying mission critical information and the data size is now growing beyond proportions. This growth is leading to a situation where in extracting the useful data from such voluminous information and delivering meaningful business insights has become a challenge. Also traditional business intelligence systems fail to see the bigger picture and are unable to deal with the data deluge there by giving way to Big Data Analytics

Standav’s Big Data Engineering and Data Science practice enables organizations to effectively aggregate, Integrate and Validate data and uncover relevant business insights in real time. Standav believes that Big Data Engineering is not about using every bit of available data coming from all possible sources but it is more about making smart decisions that accelerates business growth. Standav’s experienced Big Data Engineering and Analytics consultants have led some of the technologically challenging and compelling projects leveraging proven methodologies, tools and best practices to bring insight and innovation.

Standav’s Big Data Engineering services enables customers to understand, plan and implement a robust Big Data strategy by helping them hand in hand in providing the strategy road map, evaluation of the right technology, developing the proof of concept and then ensuring that the most suitable Big Data solution is implemented and managed.

Standav’s Big Data Engineering services spans across the following three focus areas

  • Business Consulting

    We advise based on proven methodologies a vendor neutral recommendation, Architecture blue prints and Implementation Road maps. Our consulting covers defining the Big Data Strategy, suggesting the right technologies based on customer specific requirements ensuring that the suggested approach complements existing investments in Data Warehouse / BI areas.

  • Solution Implementation

    Once we have completed our consultation in choosing the right Big Data strategy and solution, our team of Big Data experts will help in end to end implementation of the Solution which covers the process of creating a working prototype, validating the prototype and then deploying the full blown Big Data solution on production environments.

  • Applying Data Science

    With the help of our highly experienced and qualified Data scientists, we help organisations in providing meaningful predictive insights of their Big Data by performing Data cleansing and validation, analysing data patterns etc. Our teams also have reasonably good experience in Machine learning, NLP, Enterprise search and advanced decision science.

There are a number of proven use cases for adopting Big Data Engineering. Below are the top 3 use cases that we feel are relevant to most organizations generating huge data.

360 Degree Customer View

Building a 360 degree customer view using Big Data (collating data from multiple sources) gives lot of business insights to Customer service / Sales personnel across Organizations. Best use case of such a 360 degree view would be to understand customer sentiment and push offers. This can go further by applying Advanced Analytics to do necessary predictions and perform up-sell / cross-sell.

Reduce Data Warehouse Costs

Organizations have been long using Data Warehouses to facilitate their Business Intelligence efforts. With Data Warehouse technologies being costly to buy and run along with business stake holders asking for more in depth insights it becomes increasingly difficult for BI teams to deliver desired results. Such situations can be easily solved by adopting to open source Big Data solutions like Hadoop

Price Optimization

Across the world all B2B and B2C companies have adopted Price optimization strategies using Big Data. The ideal goal for all is to set a price that maximizes their income. This is now possible by understanding which price points yielded the best results under different market conditions along with segmenting their customer data and see what customers are willing to pay under different circumstances.

Featured Case Study

Customer has a huge transaction volume of approx. 150 million a day and not able to understand spend patterns

Architected a Big Data Analytics application to understand customer behaviour, share of wallet and spending patterns
Analyzed Metrics of credit card spend by state/city and also by Industry

Technology Stack

Cloudera Hadoop distribution, SQOOP, Flume, Hive / Impala

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