Big Data – Smart Insights

ValueLabs helps clients take control of their data and build smart insights

Today’s hyper connected world with its technological innovations and their increased adoption is generating huge volumes of data. Explosion in the quantity and diversity of real-time digital data holds great potential for driving innovation, competitiveness and productivity. We at ValueLabs would like to reveal how you can unlock the potential of Big Data and convert it to ‘smart insights.’

Big Data offering

ValueLabs’ Big Data practice is at the forefront of this data-driven technological disruption. We believe in partnering with our clients to help them ride this data wave, and stay a step ahead with our unique service offerings.

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At ValueLabs, we have developed strong technology skill sets across the spectrum of Big Data implementations as depicted below:
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Our solution

ValueLabs’ Big Data team has a convergence of the right mix of resources with varied technical exposures, domain experiences and partner ecosystems. With this backdrop, we help build innovative solutions that leverage Big Data technologies and deliver and transform clients’ data landscape.

ENTERPRISE DATA LAKE

A solution that is tailor-made for enterprises to ingest data from traditional (transaction systems, ERP, finance, inventory) as well as new age data sources (social media, sensors data). The most common manifestations of Enterprise Data Lake include the ability to act as a platform that enables real-time streaming analytics, Workbench for data scientists to mine and explore data from multiple sources, and a data hub for a matured enterprise data warehouse implementation. The typical path taken while building a Data Lake involves the following:

  • Ingest – Connect to different data sources (both structured and unstructured) and get the data onto a common landing / staging zone
  • Integrate – Comply with the defined change data policy, and integrate the data across multiple sources to create a master copy
  • Process – Harmonise the data and perform any cleansing that is needed. This step will also take care of all summary and aggregation requirements
  • Persist – Store the data and also cater to the data lineage
  • Discover – Query and use the data on any interface – expose through API or a BI connector, extract and export to other data sources
  • Analyse – Use the Enterprise Data Lake as an analytic workbench, and derive inferences based on data flowing in and accumulated historical data
  • Archive – Replace the traditional mechanism of heavy investments in data backup and archival, and rely on Hadoop to take care of the process. Enables availability of greater volumes of historical data

DIGITAL AUTOMOTIVE INTELLIGENCE

A Data Automotive Intelligence (DAI) platform delivers predictive, actionable insights from high-velocity sensor streams emitted through OBD devices enabling real-time alerts and immediate actions. The platform offers an integrated solution that increases customer stickiness, optimises cost and increases efficiencies.

  • Intelligent after-sales platform for dealers as a part of Dealer Management System implementation
  • Me and My Car portal – Easy-to-use and personalised dashboards with alerts for end customers
  • Highly efficient and optimised implementation as a part of Fleet Management System

As a solution, DAI focuses on data management (both real-time and batch processing), integration of data, and value addition through pattern discovery and analytics.

DATA MASKING TOOL

Data Masking is a technique to anonymise data, while continuing to retain its sanctity. All enterprises that use copies of production data for multiple needs like testing in development environments, trainings and analysis need masked data to work on.

At ValueLabs, we leverage our Big Data capability to provide a cost-effective and highly responsive solution for Data Masking. Our solution caters to both static as well as dynamic data masking.

  • Static Data Masking: The data that needs to be masked is, or can be, moved to a landing zone and then anonymised
  • Dynamic Data Masking: Masking is performed real-time by intercepting the data while in transition before it moves to a destination

The core features of the platform that supports both these types of masking include:

  • Web-based interface to configure source, destination. Schedule masking tasks
  • Algorithms that form the basis for applying masking rules at an individual column level

Key differentiators within the ValueLabs Data Masking platform courtesy the underlying Big Data architecture include being Cost-Effective, High on Performance and Compatible with multiple sources.