Engineering Analytics
Considering that we are all drowning in this ocean of data, it is critical for global enterprises to implement a well-defined analytics roadmap that will yield optimal business results. At LTTS, our combined understanding of business, technology and people enables us to bring in a holistic perspective. Our analytics engineering answers the following questions:
- What data to collect
- How much data to collect and at what intervals
- How to transmit the data
- Where to analyze it and when
- Building the right analytical models to derive insights,
- And finally, delivering the right insights to the right people at the right time
We have helped global conglomerates make new product design decisions, dramatically reduce or even eliminate machine downtime, revolutionize service support, enhance workforce productivity and engineer new revenue models.
What We Do
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Consulting Services
- Study of existing systems, processes, and infrastructure
- Identification of data sources and storage means
- Definition of engineering problems along with business implications
- IoT and connectivity consultancy
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Data Engineering
- Data preparation
- Data mining – cleaning, structuring & storing
- Definition of KPI
- Detailed approach to application of statistical/analytical modelling
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Analytics Modelling
- Creating an analytical model
- Refining the model using live data sets to improve accuracy
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Validation & Testing
- Deployment of model on live system to verify model accuracy
- Evaluate, monitor and fine-tune model to align with expected outcomes
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Deployment & Business Operations
- Productize data model
- Derive business insights for product R&D, operations, service support and new revenue models
What Stands Us Apart
- Strong ecosystem of academia and partners. Technology partnership with companies such as Splunk, Tableau, SAS, IBM, Microsoft, and Amazon.
- Vendor neutral system integrator with experience in multiple platforms:
- Big data platforms such as Hadoop, Spark, Kafka, Splunk, and non-Hadoop systems
- NoSQL data stores such as Mongo DB and Hbase
- Analytics tools like R, SAS, Python, and SPSS
- Visualization tools such as Tableau, QlikView, and Spotfire
- Readily deployable data models that can enable deep insights in areas such as demand forecasting, condition-based maintenance and warranty analytics.
Success Stories – Turning Data to Gold
