Data | Synechron

Helping enterprises move to modern data solutions and deliver reliable analytics at scale.

Partner on the journey to build trust in your data and realize its value through accessibility and analytics.

Learn more about Synechron’s Data Capabilities

Data Strategy

Deliver an enterprise vision for data through data architecture, data management and analytics solutions. Our data strategy is supported by practical roadmaps with realistic objectives.

Pre-work

Clear-sighted understanding of developing trends, methods and solution capabilities.

  • Trends in data solutions 
  • Insights on tools, techniques and services 
  • Exploratory research lab
  • Engage in industry forums 

Analysis

Establish vision, determine constraints, and enablers: Where are we right now? Where do we want to be? How do we get there?

  • Establish ambition
  • Deep-dive into existing data & data solutions 
  • Determine Target State
  • Identify issues and constraints

Solution Architecture

Modern data solutions for real-time speed and scale.

  • System Architecture – cloud & on-prem
  • Customer Journey 
  • Design Thinking
  • Implementation considerations

Data Management and Governance

Ensure enterprise data is appropriately managed and controlled, establishing basis for reliable and good quality insights.

  • Current State assessment
  • Governance Operating Model
  • Metadata management
  • Policy, process and procedure review 

Data Engineering

Optimizing information flow through agile data pipelines.

Trust and Reliability

Create and maintain the analytics infrastructure enabling pipelines to transform raw or unstructured data use for analysis in large-scale processing systems.

  • Data architecture & management
  • Data modeling & analysis
  • Engineering

Discipline

Disciplined approach in data engineering management ensures data is legal, well-organized, safe, accessible, and trustworthy.

  • Provisioning
  • Monitoring & Security
  • Policy adherence
  • Disaster Recovery

Data Management

We help clients extract reliable insights, and streamline & automate decision-making by ensuring data is appropriately identified, managed and controlled.

Data Governance

The key to having data that can be trusted lies in empowering its owners and users with clear responsibilities such as training, standards & practices, and tooling.

  • Continuous training and awareness
  • Policies and standards
  • Roles and Responsibilities

Data Quality

Define data quality requirements for critical data elements and implement effective processes to measure, monitor, and report issues that impact accuracy, completeness, and integrity of data.

  • Defined quality criteria
  • Automated DQ checks
  • Strong governance to mitigate DQ issues at the source

Meta Data Management

Create complete glossaries of data, including lineage from record to report, provide users means to easily access and understand business data.

  • Business glossary & data dictionary
  • Data Lineage
  • Master, Reference, and Static data standards

Data Visualization

We extract business insights from complex data, and by representing information visually (e.g. dashboards) we make it easier to understand trends, patterns, and identify anomalies.

Objectives

Through accurate data visualization, the reporting processes can be elevated to new levels.

  • Making complex data accessible, understandable, and usable
  • Transform, improve, and integrate data
  • Combine data from multiple sources
  • Deliver data in a useful and appealing way

Reporting

Effective and fast insights from data using enhanced visualizations.

  • Data cleaning
  • Statistical analysis & modeling
  • Dashboards and enhanced visualization
  • Better and faster insights

Tools

Expertise with best-in-class industry tools.

  • Tableau
  • Qlikview
  • Microsoft Power BI
  • and more...

Data Science & Modeling

Analyze and document descriptive, predictive, and prescriptive models that can generalize beyond already observed examples.

Exploratory Data Analysis

Initial investigation into available data to understand what analytics are possible.

  • Data distribution histograms 
  • Data completeness assessment 
  • Outlier detection & Class (im)balance
  • Feature importance, sensitivity analysis

Data Science

Combine domain knowledge, mathematical skills, and expert architectural & programming skills to extract robust, repeatable, and meaningful insights from data.

  • Scientific literature review 
  • Data cleaning feature engineering 
  • Dimensionality reduction 
  • Model proof of concept 

Modeling

Applying different modeling approaches to determine best fit model(s).

  • Natural Language Processing, translation, understanding 
  • Computer Vision 
  • Speech detection, recognition 
  • Audio/image noise reduction 

AI Platform Development

Becoming a data-driven organization means embracing automated decision-making as an integral part of everyday operations.

AI Design & Implementation

Bridging gaps between solution architecture and technical implementation in code.

  • AI Architecture 
  • Choosing the right tools 
  • Model benchmarking 
  • Model implementation 

AI Maintenance & Improvement

Maintaining AI models is a challenge, requiring constant monitoring, understanding of limitations and changes to the world. We take a holistic, iterative approach to model maintenance.

  • Deployment & Monitoring
  • Training data enhancement
  • Model enhancement
  • Performance metric refinement

AI Use Cases

The most common types of decision-making systems per application are:

  • Prediction & Forecasting 
  • Intelligent search and Conversational AI
  • Fraud & Risk management
  • Process (re)Engineering and Knowledge maps
  • Decision support systems

AI Audit

Assess whether the AI solution delivers against expected business goals, and its behavior while being attacked.

  • Data drift and concept drift estimation 
  • Adversarial model analysis
  • AI impact on privacy assessment
  • Hardening models against adversarial attacks

Cognitive & Intelligent Automation

Improve business efficiency by deploying Artificial Intelligence (AI), Business Process Management (BPM), and Robotic Process Automation (RPA) in the right proportions, and in the right places.

Robotics

Solutions/Bots specialized in interacting with humans and computers alike with deep ties to specialized systems downstream and can help reduce friction between someone looking for a piece of information and them finding it. Discover patterns and recommend improvements to processes across large bodies of humans, and machine communications.

  • Read, comprehend, and extract facts from documents 
  • Interact with documents in Question-and-Answer fashion  
  • Use linguistic features to express an idea/concept 
  • Analyse and improve decision making process across services 

Intelligent Automation

Discover patterns and recommend improvements to processes across large bodies of humans, and machine communications.

  • Specialized integration with well-known communication systems 
  • Forecast failure rate and recommend remediation strategies 
  • Track all paths followed in completing a transaction 
  • Determine various (controlled and otherwise) causes leading to failure 

Process Automation

Reduce time to market, turn-around time and business inefficiencies by automating decision making.

  • Track all paths followed in completing a transaction 
  • Determine various (controlled and otherwise) causes leading to failure 
  • Faster and more intelligent self-help portals 
  • Automated decisions based on heuristics and management preferences 

Data Case Studies

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