In today’s digital world, data analytics, and AI play a key role in transforming native business operations, creating new business models, and unleashing process improvements.  WCS’s data, analytics, and AI services enable organizations to deliver value across the customers’ journey by empowering users with more agile and intuitive processes.

Our services help organizations use data and analytics to create new business models and revenue streams – all while ensuring security, quality, and regulatory compliance of data. Underpinned by technologies such as the cloud, Internet of Things (IoT), Artificial Intelligence (AI), Machine Learning (ML), and advanced analytics, our solutions help enhance decision making while enabling augmented intelligence and process automation.

Our Capabilities

Six critical capabilities are required to achieve business reinvention from a technical, cultural, and adoption standpoint.

Data Management &

Enable a trusted approach to ensure that critical data is managed, maintained, and governed centrally so it can be responsibly leveraged to develop differentiating capabilities.

Data Platform &

Build a secure data foundation and cloud architecture that can harness data for a deeper view into the organization to meet current needs flexibly to scale for the future.

Organization & Skills

Apply principles of a product-based operating model to structure the right team and processes, facilitate collaboration, and accelerate execution on business priorities.

Business Adoption

Industrialize data and AI across the organization to instill a data-driven culture and analytics mindset for better decision-making and, ultimately, business adoption of strategic priorities.

Value Realization

Measure execution, experience, and the impact on business outcomes constantly and consistently.

Critical Data Elements

Identify data that is not only critical for business change, but also unlocks the greatest value for the organization.

Data Analytics Areas We Cover

Financial analytics

  • Monitoring revenue, expenses and profitability of a company.
  • Profitability analysis and financial performance management.
  • Budget planning, formulating long-term business plans.
  • Financial risk forecasting and management.

Manufacturing analytics

  • Overall equipment effectiveness analysis and optimization.
  • Manufacturing process quality prediction and management.
  • Equipment maintenance scheduling.
  • Power consumption forecasting and optimization.
  • Production loss root cause analysis.

Asset analytics

  • Real-time asset monitoring and tracking.
  • Asset life cycle management
  • Predictive and preventive maintenance.
  • Asset health prediction.
  • Designing asset maintenance and replacement strategies.

Customer analytics

  • Customer behavior analysis and predictive modeling.
  • Customer segmentation for tailored marketing campaigns.
  • Personalized cross-selling and upselling offers for extended customer lifetime value.
  • Predicting customer attrition and customer churn risk management.
  • Customer sentiment analysis for increasing product/service quality.

Supply chain analytics

  • Identifying demand drivers, consumer demand forecasting and planning.
  • Supplier performance monitoring and evaluation.
  • Predictive route optimization.
  • Determining the optimal level of inventory to meet the demand and prevent stockouts, inventory planning and management.
  • Identifying patterns and trends throughout the supply chain for enhanced supply chain risks management.

Brand and product analytics

  • Conducting product performance analysis.
  • Tracking customer interactions with a product to identify pain points leading to churn.
  • Conducting competitor benchmarking.

Data Analytics Tech Components We Cover

Data Warehouse

  • Extract, transform, load (ETL) or extract, load, transform (ELT) design and implementation
  • Data governance (data security, quality, availability) management
  • Data warehouse and data marts design and implementation.

Big Data

  • Big data infrastructure setup and support.
  • Big data quality and security management.
  • Big data analysis and reporting.

Data Science

  • Big Data preparation and management.
  • Development and tuning of ML (including deep learning) models.
  • Development and tuning of data mining models.
  • Image analysis software development.

Data Visualization

  • Interactive dashboarding.
  • Custom and pre-built visuals.
  • Multiple visualization techniques (symbol maps, line charts, bar charts, pie charts, etc.)