The Ruby Differentiator
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.
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
- Strategic Big Data Analytics Consulting
We will help you keep your data environment cost under control thereby enabling your enterprise to access more data and scale up to expanding business requirements. We will help you understand how Big Data technologies, tools and processes can transform your organization. You can reap rewards by leveraging on our expertise in Hadoop-based platforms, MPP databases, cloud storage systems, and other emerging technologies.
- Solution Delivery
Our data delivery is fully automated, high performance, low maintenance, enterprise-ready and responsive.
- Managed Services
We take end to end ownership of service delivery from consulting, implementation, migration, infra-sizing and provisioning to platform support. We also offer packaged services with committed SLA and clear upfront pricing.
- Training in Big Data Analytics
Get trained by us in Big Data Analytics. We employ an innovative, experiential learning methodology that blends classroom lectures with virtual trainer sessions. These are designed to equip you with a thorough knowledge of data science tools and exposure to business perspectives and industry best practices.
Real-time detection of fraud, money laundering and compliance with anti-money laundering regulations
- 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.
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.
- 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.
Improve demand forecasting so that cost of goods, shipments and inventory levels can be maintained at accurate levels.
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.
Improve life in campaign metrics and enable micro-targeting
- 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.
- We had never heard of Yotabites and so were initially unsure whether to engage them or not. Their name was mentioned in passing by our technology vendor. We decided to give them a session to present themselves. Within 30 minutes, their expertise and depth of knowledge was obvious. Our team is highly technical and whoever we selected to develop and manage Big data needed to be experts and needed to be close to us. Since then, they have been a true partner and have been delivering and managing our Big Data solutions like true Ninjas!Dmitriy KhotsVice President of Strategic Insights. West Corporation.
- Yotabites helped us understand the potential of big data and data science. Their knowledge and expertise provided us with a whole new perspective and we have a better understanding of how to harness the full power of data.Lonnie JohnsonVice President of Information Technology, KVC Health Systems
Services that we offer
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.
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.
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.