Center of Excellence

In this blog post

Big Data’s Role

Data Analytics Stages

Focus areas

The Big Data CoE is a team of experts that experiments and builds various cutting-edge solutions by leveraging the latest technologies, like Hadoop, Spark, Tensor-flow, and emerging open-source technologies, to deliver robust business results. A CoE is where organizations identify new technologies, learn new skills, and develop appropriate processes that are then deployed into the business to accelerate adoption.

Leveraging data to drive competitive advantage has shifted from being an option to a requirement for hyper competitive business landscape. One of the main objectives of the CoE is deciding on the right strategy for the organization to become data-driven and benefit from a world of Big Data, Analytics, Machine Learning and the Internet of Things (IoT).

Cloud Migration Assessment Tool for Business — *Triple Constraints of Projects*

“According to Chaos Report, 52% of the projects are either delivered late or run over the allocated. The average across all companies is 189% of the original cost estimate. The average cost overrun is 178% for large companies, 182% for medium companies, and 214% for small companies. The average overrun is 222% of the original time estimate. For large companies, the average is 230%; for medium companies, the average is 202%; and for small companies, the average is 239%.”

Big Data CoE plays a vital role in bringing down the cost and reducing the response time to ensure project is delivered on time by helping the organization to build the skillful resources.

Big Data’s Role

Helping the organization to build quality big data applications on their own by maximizing their ability to leverage data. Data engineers are committed to helping ensure the data:

define your strategic data assets and data audience
gather the required data and put in place new collection methods
get the most from predictive analytics and machine learning
have the right technology, data infrastructure, and key data competencies
ensure you have an effective security and governance system in place to avoid huge financial, legal, and reputational problems.

Data Analytics Stages

Architecture optimized building blocks covering all data analytics stages: data acquisition from a data source, preprocessing, transformation, data mining, modeling, validation, and decision making.

Focus areas

Algorithms support the following computation modes:

Batch processing
Online processing
Distributed processing
Stream processing

The Big Data analytics lifecycle can be divided into the following nine stages:

Business Case Evaluation
Data Identification
Data Acquisition & Filtering
Data Extraction
Data Validation & Cleansing
Data Aggregation & Representation
Data Analysis
Data Visualization
Utilization of Analysis Results

A key focus of Big-data CoE is to establish a data-driven organization by developing proof of concept with the latest technologies with Big Data and Machine learning models. As of part of CoE initiatives, we are involved in developing the AI widgets to various market places, such as Azure, AWS, Magento and others. We are also actively involved in engaging and motivating the team to learn cutting edge technologies and tools like Apache Spark and Scala. We encourage the team to approach each problem in a pragmatic way by making them understand the latest architectural patterns over the traditional MVC methods.

It has been established that business-critical decisions supported by data-driven insights have been more successful. We aim to take our organization forward by unleashing the true potential of data!

If you have any questions about the CoE, you may reach out to them at SME_BIGDATA@gavstech.com

CoE Team Members