Industries we cater currently for data analytics services

Finance and Banking

In the finance and banking industry, data analytics is crucial for risk management, fraud detection, customer segmentation, and personalised marketing. Analysing financial data helps identify trends, predict market movements, and optimise investment strategies.

Healthcare

In healthcare, data analytics is used for patient care management, disease prevention, treatment optimisation, and resource allocation. Analysing medical records, patient data, and clinical trials helps improve patient outcomes, reduce costs, and enhance healthcare delivery.

Retail and E-commerce

In retail and e-commerce, data analytics is essential for understanding customer behaviour, optimising pricing and promotions, managing inventory, and improving supply chain efficiency. Analysing sales data, customer demographics, and shopping patterns helps retailers personalise marketing efforts and enhance the customer experience.

Manufacturing and Supply Chain

In manufacturing and supply chain management, data analytics is used for demand forecasting, inventory optimisation, predictive maintenance, and quality control. Analysing production data, supply chain logistics, and sensor data helps manufacturers improve efficiency, reduce downtime, and ensure product quality.

Telecommunications

In the telecommunications industry, data analytics is critical for network optimisation, customer churn prediction, targeted marketing, and service personalisation. Analysing network performance data, customer usage patterns, and demographic data helps telecom companies improve network reliability, retain customers, and drive revenue growth.

Insurance

In the finance and banking industry, data analytics is crucial for risk management, fraud detection, customer segmentation, and personalised marketing. Analysing financial data helps identify trends, predict market movements, and optimise investment strategies.

Marketing and Advertising

In marketing and advertising, data analytics is essential for campaign optimisation, audience targeting, and performance measurement. Analysing customer data, campaign metrics, and market trends helps marketers identify the most effective strategies, optimise advertising spend, and maximise return on investment.

Energy and Utilities

In the energy and utilities sector, data analytics is used for grid optimisation, asset management, predictive maintenance, and energy efficiency. Analysing sensor data, consumption patterns, and weather forecasts helps utilities improve reliability, reduce costs, and meet regulatory requirements.

Services offered

1. Financial Analytics Services
Drive Financial Intelligence with Advanced Analytics

Introduction: Explore how financial analytics can improve risk management and maximise returns.

Key Features

  • Risk assessment and management for informed decisions.
  • Fraud detection and prevention for secure transactions.
  • Investment portfolio optimisation for higher returns.

Deliverables

  • Risk assessment reports.
  • Fraud detection alerts.
  • Investment portfolio optimisation strategies.

Benefits

  • Enhanced risk management and compliance.
  • Improved fraud detection and prevention.
  • Maximising investment returns.
2. Healthcare Analytics Services
Transform Healthcare with Data-Driven Insights

Introduction: Discover how healthcare analytics can improve patient outcomes and optimise operations.

Key Features

  • Patient population analysis for personalised care.
  • Clinical outcomes prediction for better treatment plans.
  • Resource optimisation for efficient healthcare delivery.

Deliverables

  • Patient population insights.
  • Clinical outcome predictions.
  • Operational efficiency recommendations.

Benefits

  • Enhanced patient care and outcomes.
  • Cost reduction and resource optimisation.
  • Compliance with regulatory standards.
3. Supply Chain Analytics Services
Optimise Supply Chain Efficiency with Data Analytics

Introduction: Explore how supply chain analytics can streamline operations and enhance efficiency.

Key Features

  • Demand forecasting and inventory optimisation for efficient planning.
  • Logistics and transportation analysis for cost-effective distribution.
  • Supplier performance monitoring for enhanced partnerships.

Deliverables

  • Demand forecasting reports.
  • Inventory optimisation recommendations.
  • Supplier performance insights.

Benefits

  • Improved supply chain efficiency and agility.
  • Cost reduction and inventory optimisation.
  • Enhanced collaboration with suppliers.
4. Marketing Analytics Services
Unleash Marketing Potential with Data-Driven Insights

Introduction: Discover how marketing analytics can drive targeted campaigns and maximise ROI.

Key Features

  • Customer segmentation and targeting for personalised marketing.
  • Campaign performance analysis for optimisation.
  • Marketing attribution modelling for effective strategies.

Deliverables

  • Customer segmentation reports.
  • Campaign performance insights.
  • Marketing attribution models.

Benefits

  • Enhanced customer engagement and retention.
  • Improved ROI on marketing campaigns.
  • Data-driven decision-making for marketing strategies.
5. Predictive Analytics Services
Unlock Future Insights with Predictive Analytics

Introduction: Explore the power of predictive analytics in anticipating future trends and making informed decisions.

Key Features

  • Advanced predictive modelling techniques.
  • Forecasting future outcomes with accuracy.
  • Identifying patterns and trends for proactive strategies.

Deliverables

  • Predictive models for forecasting.
  • Insights into future trends and outcomes.
  • Actionable recommendations for decision-making.

Benefits

  • Stay ahead of the competition.
  • Minimise risks and maximise opportunities.
  • Optimise resource allocation and planning.
Our specialisation on data analytics skills include
Data Collection and Integration
  • Gathering data from various sources, including databases, APIs, logs, and streaming sources.
  • Integrating disparate datasets into a unified format for analysis
Data Cleansing and Pre-processing
  • Identifying and handling missing or erroneous data.
  • Removing duplicates and outliers.
  • Standardising data formats and values.
Exploratory Data Analysis (EDA)
  • Understanding the structure and distribution of the data.
  • Identifying patterns, trends, and relationships within the data.
  • Performing statistical analyses and visualisations.
Descriptive Analytics
  • Summarising historical data to gain insights into past performance.
  • Generating reports, dashboards, and data visualisations for decision-making.
Predictive Analytics
  • Building predictive models to forecast future trends and outcomes.
  • Using machine learning algorithms for regression, classification, and time series analysis
Prescriptive Analytics
  • Recommending actions based on predictive models and business objectives.
  • Optimising decision-making processes to achieve desired outcomes.
Data Mining
  • Extracting valuable patterns and insights from large datasets.
  • Utilising techniques such as clustering, association rule mining, and anomaly detection.
Text Analytics
  • Analysing unstructured text data to extract meaningful insights.
  • Performing sentiment analysis, topic modelling, and named entity recognition.
Real-time Analytics
  • Analysing data streams in real-time to detect patterns and anomalies.
  • Implementing event processing and stream processing technologies.
Customer Analytics
  • Understanding customer behaviour and preferences through data analysis.Segmentation, profiling, and targeting of customers for personalised marketing strategies.
Marketing Analytics
  • Analysing marketing campaigns to measure effectiveness and ROI.
  • Attribution modelling, customer acquisition analysis, and campaign optimisation.
Financial Analytics
  • Analysing financial data to support investment decisions, risk management, and financial planning.
  • Forecasting financial performance and modelling economic scenarios.
Healthcare Analytics
  • Analysing healthcare data to improve patient outcomes, reduce costs, and optimise operations.
  • Clinical data analysis, population health management, and predictive modelling for disease prevention.
Supply Chain Analytics
  • Optimising supply chain operations through data-driven insights.
  • Demand forecasting, inventory optimisation, and logistics optimisation.
Fraud Analytics
  • Detecting and preventing fraudulent activities through data analysis.
  • Transaction monitoring, anomaly detection, and predictive modelling for fraud detection.
Social Media Analytics
  • Analysing social media data to understand customer sentiment, engagement, and brand perception.
  • Social network analysis, trend detection, and influencer identification.
Web Analytics
  • Analysing website traffic and user behaviour to optimise digital marketing strategies.
  • Conversion rate optimisation, user journey analysis, and A/B testing.
Geospatial Analytics
  • Analysing location-based data to derive insights about spatial patterns and relationships.
  • Geospatial visualisation, spatial clustering, and proximity analysis.
Data Governance and Compliance
  • Establishing policies, procedures, and controls for data management and privacy.
  • Ensuring compliance with regulations such as GDPR, HIPAA, and CCPA.
Types of resources available with us
Data & AI Project Management Consultant
Project Planning and Initiation
  • Define project scope, objectives, and deliverables in collaboration with stakeholders.
  • Develop project plans, timelines, and resource allocations.
  • Conduct project kick-off meetings and communicate project goals to the team.
Stakeholder Management
  • Identify and engage key stakeholders, including business leaders, data scientists, analysts, and IT teams.
  • Communicate project progress, milestones, and risks to stakeholders.
  • Manage stakeholder expectations and address concerns throughout the project lifecycle.
Resource Management
  • Allocate resources, including personnel, budget, and technology, to support project goals.
  • Coordinate with internal teams and external vendors to ensure timely delivery of project tasks.
  • Monitor resource utilisation and adjust plans as needed to optimise efficiency.
Project Execution
  • Oversee the execution of project tasks and activities according to the project plan.
  • Track project progress, milestones, and deliverables.
  • Manage project risks and issues and implement mitigation strategies as necessary.
  • Ensure adherence to project timelines and budget constraints.
Data Management
  • Collaborate with data engineers and analysts to ensure data availability, quality, and security for project requirements.
  • Define data governance policies and standards to ensure compliance with regulatory requirements.
  • Establish data pipelines and workflows to support data analytics and AI initiatives.
AI Model Development and Deployment
  • Work closely with data scientists and AI engineers to develop machine learning models and algorithms.
  • Coordinate model development, testing, and validation activities.
  • Oversee the deployment of AI models into production environments and monitor their performance.
Change Management
  • Identify and manage changes to project scope, requirements, and deliverables.
  • Communicate changes to stakeholders and assess their impact on project goals.
  • Implement change management processes to ensure smooth project execution.
Quality Assurance and Testing
  • Develop and implement quality assurance processes to ensure the accuracy and reliability of data analytics and AI solutions.
  • Conduct testing and validation of data analytics tools, models, and algorithms.
  • Monitor and evaluate project outcomes to measure success and identify areas for improvement.
Documentation and Reporting
  • Maintain project documentation, including project plans, status reports, and meeting minutes.
  • Generate regular progress reports and performance metrics for project stakeholders.
  • Document lessons learned and best practices for future project initiatives.
Continuous Improvement
  • Identify opportunities for process improvement and optimisation within the data analytics and AI project lifecycle.
  • Collect feedback from stakeholders and team members to identify areas for improvement.
  • Implement lessons learned and best practices to enhance project outcomes and efficiency.
Other resources and roles
Data Analyst Consultant
  • Collecting, cleaning, and transforming data from various sources.
  • Performing exploratory data analysis to identify trends, patterns, and insights.
  • Developing and maintaining dashboards, reports, and data visualisations.
  • Conducting statistical analyses and interpreting results to support decision-making.
  • Collaborating with stakeholders to understand business requirements and provide data-driven insights.
Data Scientist Consultant
  • Designing and developing predictive and prescriptive models using statistical and machine learning techniques.
  • Conducting advanced data analysis to uncover hidden patterns, correlations, and insights.
  • Building and deploying machine learning models to solve business problems.
  • Collaborating with cross-functional teams to identify opportunities for data-driven initiatives.
  • Communicating complex findings and recommendations to non-technical stakeholders.
Business Analyst Consultant
  • Gathering and analysing business requirements for data analytics projects.
  • Translating business needs into technical requirements for data analytics solutions.
  • Collaborating with stakeholders to define key performance indicators (KPIs) and success metrics.
  • Analysing business processes and workflows to identify areas for improvement.
  • Providing recommendations for data-driven strategies to achieve business objectives.
Data Engineer Consultant
  • Building and maintaining data pipelines for collecting, processing, and storing data.
  • Designing and implementing data warehouses and data lakes to support analytics and reporting needs.
  • Developing and maintaining ETL (Extract, Transform, Load) processes for data integration.
  • Optimising data infrastructure for performance, scalability, and reliability.
  • Collaborating with data scientists and analysts to ensure data quality and accessibility.
Data Architect Consultant
  • Designing and implementing data architecture solutions to support business requirements.
  • Defining data models, schemas, and structures for efficient data storage and retrieval.
  • Developing data governance policies and standards to ensure data integrity and security.
  • Evaluating and selecting appropriate technologies and platforms for data storage and processing.
  • Collaborating with stakeholders to align data architecture with business goals and objectives.
Data Visualisation Specialist Consultant
  • Designing and developing data visualisations, dashboards, and reports.
  • Creating interactive and engaging data visualisations using tools such as Tableau, Power BI, or D3.js.
  • Collaborating with data analysts and scientists to translate complex data findings into visually appealing stories.
  • Conducting user research and feedback sessions to improve the usability and effectiveness of data visualisations.
  • Staying updated on industry best practices and trends in data visualisation techniques.
Machine Learning Engineer Consultant
  • Designing, implementing, and deploying machine learning models and algorithms.
  • Conducting data pre-processing, feature engineering, and model training.
  • Evaluating and optimising model performance using techniques such as cross-validation and hyperparameter tuning.
  • Deploying machine learning models into production environments and monitoring their performance.
  • Collaborating with data scientists, engineers, and stakeholders to integrate machine learning solutions into business processes.
Quantitative Analyst Consultant
  • Conducting quantitative research and analysis to support investment decisions.
  • Developing financial models and algorithms to predict market trends and assess risk.
  • Analysing financial data and economic indicators to identify investment opportunities.
  • Building and back testing trading strategies using statistical and machine learning techniques.
  • Communicating findings and recommendations to portfolio managers and stakeholders
Some of the tools our consultants are well-versed with
Microsoft Power BI
  • Power BI incorporates AI capabilities like AI visuals, which automatically highlight insights in data visualisations, and AI-powered natural language querying for easier data exploration.
  • Power BI is a business analytics tool by Microsoft that allows users to create interactive reports and dashboards with data visualisation capabilities.
Google Data Studio
  • Google Data Studio offers AI-driven features like automated insights, which use machine learning to surface meaningful trends and outliers in data.
  • Google Data Studio is a free tool that enables users to create interactive dashboards and reports using data from Google products and other sources.
Tableau
  • Tableau utilises AI-powered features such as Explain Data, which automatically analyses data sets to provide explanations for observed trends and anomalies.
  • Tableau is a leading data visualisation tool that enables users to create interactive and shareable dashboards and reports from various data sources.
IBM Watson Analytics & Cognos Analytics
  • IBM Watson Analytics employs AI and machine learning algorithms to assist users in uncovering insights from data, providing features such as predictive analytics and natural language querying.
  • Cognos Analytics is a business intelligence and analytics platform by IBM that offers reporting, dashboarding, and data visualisation features for analysing and sharing data insights.
SAS Visual Analytics
  • SAS Visual Analytics utilises AI and machine learning to automate analytical tasks, recommend best practices, and uncover hidden insights in data.
  • SAS Visual Analytics is a data visualisation and reporting tool that allows users to create interactive reports and dashboards with data exploration capabilities.
Qlik Sense
  • Qlik Sense employs AI capabilities such as cognitive engine and natural language processing to offer intuitive data exploration and generate insights from data.
  • Qlik Sense is a self-service data visualisation and discovery tool that allows users to create interactive and personalised reports and dashboards.
Jaspersoft
  • Jaspersoft is an open-source business intelligence and analytics platform that offers reporting and dashboarding features for analysing and sharing data insights.
Our deliverables include based on clients’ requirements
Data Requirements Document
  • Document outlining the data needs and specifications for the analytics project, including data sources, formats, and quality requirements.
Data Collection Plan
  • Plan detailing how data will be collected from various sources, including databases, APIs, sensors, logs, etc., and stored for analysis.
Data Cleaning and Pre-processing Report
  • Report documenting the process of cleaning and pre-processing raw data, including handling missing values, outliers, duplicates, and standardising formats.
Exploratory Data Analysis (EDA) Report
  • Report summarising the findings from exploratory data analysis, including data distributions, patterns, correlations, and initial insights.
Data Modelling Plan
  • Plan outlining the approach and methodology for building predictive or descriptive models using the data, including algorithms and techniques to be used.
Model Development Report
  • Report documenting the process of developing predictive or descriptive models, including feature selection, model training, and evaluation metrics.
Model Evaluation Report
  • Report evaluating the performance of predictive or descriptive models using validation techniques such as cross-validation or holdout validation.
Dashboard Design and Development
  • Design and development of interactive dashboards to visualise and communicate data insights to stakeholders, including key performance indicators (KPIs) and metrics.
Report Generation and Distribution
  • Generation of reports summarising data analysis findings and insights, including visualisations and explanations of key findings, distributed to stakeholders.
Data Visualisation Design and Development
  • Design and development of data visualisations, including charts, graphs, and maps, to facilitate data exploration and communication of insights.
Data Insights Presentation
  • Presentation of data analysis findings and insights to stakeholders, including visualisations and explanations of key findings, to facilitate decision-making.
Recommendation and Action Plan
  • Recommendation of actionable insights based on data analysis findings, along with an action plan for implementation, including prioritised recommendations and timelines.
Post-Implementation Review Report
  • Report summarising the outcomes and lessons learned from the data analytics project, including recommendations for future improvements and enhancements.
Data Analytics Roadmap
  • Roadmap outlining future data analytics initiatives, including prioritised projects, timelines, and resource requirements, aligned with business goals and objectives.
Why choose us
Expertise and Experience
  • Significant expertise and experience of team in data analytics.
  • Various domain expertise.
Innovative Solutions
  • Commitment to innovation and staying ahead of industry trends.
  • We use industry standard methodologies, algorithms, and technologies to deliver cutting-edge data analytics solutions.
Customised Approach
  • We offer tailored solutions that are customised to meet the unique needs and objectives of each client.
  • We adapt to different business contexts and deliver personalised insights and recommendations.
End-to-End Services
  • Provide comprehensive data analytics services covering the entire project lifecycle, from data collection and pre-processing to modelling, analysis, and reporting.
  • Offer a one-stop solution for all data analytics needs, minimising the need for multiple service providers.
Collaborative Approach
  • Collaborative approach to working with clients and involving them in the project every step of the way.
  • Listen to client needs, provide regular updates, and incorporate feedback to ensure project success.
Data Security and Compliance
  • Ensure the highest standards of data security and compliance with regulations.
  • Commitment to data privacy and security, including encryption protocols, access controls, and compliance measures.
Scalability and Flexibility
  • Offer scalable solutions that can adapt to the changing needs and growth of clients' businesses.
  • Ability to handle large volumes of data and complex analytics projects with ease.
Transparent Pricing and ROI
  • Provide transparent pricing structures and clear ROI metrics to demonstrate the value of your services.
  • Our services can help clients achieve their business goals and improve their bottom line.