Big Data projects needs a solid execution team and a sound technical strategy. These are two important factors we bring into the table with our Team based out of Kansas City USA.

Raj Nair – Partner	Mani Raman – Partner
Raj has a distinguished 20 year career in the database software industry . Over the years Raj has helped customers break down data silos and re-invent business value by unlocking the full potential of their data. He also believes in the power of community. He sits on the advisory board for multiple universities in the Kansas City metro to help develop Big Data engineering skills much needed in the workplace. Previously he held various leadership roles at IBM and EMC. He holds a Bachelors and Masters degree in Engineering as well as an MBA.	Mani is obsessed with getting data right. He is always looking to innovate and to help customers realize their full ROI using data.He has over 17 years of IT industry experience and has held technical leadership roles in large enterprises. He has extensive experience building data warehouse and BI solutions. He is also certified on multiple Big Data distributions and has implemented several large scale Big Data deployments. He holds an MS in Mathematics.

Our Architecture teams are repositories of technical wisdom. They are actively involved in shaping all our project engagements. The teams constantly strive to raise the efficiency bar through innovation.

Delivery teams are often as good as the leadership they inherit. Our leadership team combines knowledge, experience, empathy, and self-motivation to steer the projects on course while maintaining high employee morale and productivity. Over the years we have continuously innovated and improved our processes. Adherence to the same has ensured that our projects are delivered on time and to superior quality. Knowledge transfer is smooth and risk management is given utmost importance. This also means that our customers are never in for nasty surprises.

Our firm is reputed for its entrepreneurial thinking. We, therefore, get under the skin of the customers as we identify ourselves with them. We do not just seek solutions, we seek optimal solutions. In the process, we make new discoveries every day which transform into business benefits for our partners.

We have strong relationships with the key leading product vendors in Big Data. We have proven expertise in bridging the gap between raw data and impactful analytics cost-effectively. Our certified Hadoop administrators and developers are amply rewarded by our midwest brand recognition and credibility.

Our technology arm Yotabites is Cloudera Silver Partner, RStudio Partner/Reseller as well as Microsoft Partner.

Ruby Software’s team consists of onshore and offshore consultants  who bring the following to the table:

• Over 20 years experience in the Data industry
• Thought leadership in Big Data Advisory Boards
• Subject Matter Expertise in Big Data Architecture and technology in the Midwest
• Hold patents in the field of Data Warehousing solutions.
• Several successful implementations of large data warehouses and data marts

Financial

Real-time detection of fraud, money laundering and compliance with anti-money laundering regulations

Challenges

• Identifying, flagging and reporting suspicious transactions in real time
• Identifying the different patterns of suspicious behavior to identify organizations, accounts and account holders who are likely to commit a crime
• Reducing monetary liability from non-compliance

The traditional approach to AML and fraud detection has been rule-based and reactive. Many suspicious transactions fall through the cracks of these rules. To shift from rule-based to pattern detection requires the application of data science (machine learning algorithms), a variety of different sources (such as watch lists, news feeds) and large amounts of historical transactional data. To analyze potential suspicious activities in real time requires a stream processing infrastructure that can ingest and analyze billions of transactions as they happen.

Transportation

Analyse of Telematic data from delivery vehicles for route optimization to help save millions of dollars through a reduction in fuel consumption and miles traveled.

Challenges

• For best results, combine telematic data with other data sources like weather data, data from transportation department such as route maps, construction, and traffic.
• Real-time events and trends need to mesh with historical data for real-time route optimization.
• To improve accuracy and confidence of prediction, multiple models will have to be constructed and compared against as much data as possible.

The above is not practical with traditional technologies. Using Big Data technologies and through building an integrated analytics platform, companies are now able to save top dollars and set up their competitive advantage.

Supply Chain

Improve demand forecasting so that cost of goods, shipments and inventory levels can be maintained at accurate levels.

Challenges

Loss of unsold inventory and perishable goods amounts to tens of millions of dollars

Demand forecasting not much improved

Demand forecasting is a critical process in the supply chain due to the drain on capital from unsold inventory stock. Even a 1 % change in accuracy and confidence levels in the forecast can result in a reduction of millions of dollars. However, it requires the ability to build an ensemble of models that forecasters can choose from. This is not possible with traditional processes. Adding observations and variables will delay processing time rendering results useless.

Marketing

Improve life in campaign metrics and enable micro-targeting

Challenges

• Scoring and classification of customers (based on predictor variables like propensity for an offering and historical buying behavior).
• Identifying new customers similar to existing profiles (using external demographic data as needed).
• Next best offer for existing customers (based on historical purchasing behavior and changing demographics).
• Personalized/ highly targeted campaigns.
• Identifying customers at risk of flight.

Most marketing analytics is based on static data in data warehouses. Micro-targeting (personalized ads and campaigns based on individual preferences) is not possible using existing data and analytics technologies.

Time and Material

In a time & material (T&M) contract, Ruby Software and the customer agree to unit rates that have been predetermined by both parties in advance for executing the project. senior engineers. The contract has no definite end date. Therefore the full value of the contract cannot be defined at the time of awarding it. T&M contract can grow in value over the period that it is in effect. The inherent flexibility of the time and material (T&M) contract makes it an attractive option from a project management perspective for both Ruby Software and the customer.

Fixed Price

In a Fixed Price or Lump Sum contract, Ruby Software and the customer agree on a fixed price for the project while embarking on it. The payment does not depend on resources used or time expended. Given that risk management is of utmost priority in this type of contract to take care of budgetary overruns, more detailed specifications, checklists and scope documents are required from the customer side to ensure a smooth execution and delivery of the project.

PoC

Ruby Software engages with customers by  executing a Proof of Concept to help the latter gain invaluable business insights and make the right moves Clear goals are identified by asking these questions:

• Who? What? When? Where? Why? How?
• What criteria will we use to measure our progress?
• Is the goal achievable or realistic?
• Is the goal relevant to the business?
• When will this goal be accomplished?

Next step is to perform a Data Assessment to determine:

• What data you are currently collecting, such as purchasing history.
• What data you need to start collecting.
• The quality of the data

Finally, the Big Data Initiative is deployed taking into account the layers of Big Data deployment: infrastructure, data management, and application. Virtualizing is the key to get to the third and most important step of executing the application layer.