Data Preparation, Analysis and Visualisation

Preparation of all sources of textual and numeric data to make them available for analysis.

Combining data of different types and from different sources to gain additional insights into operations and behaviours. For example:

• Linking data collected during every-day business operations and administration (operational/administrative data), with aggregate data (such as employment rates, educational attainment, health care outcomes, or geographical location) to better understand outcomes or to target services and marketing.
• Joining sales data with customer/client survey and feedback data to inform service delivery.
• Merging natural language text data from different sources and in different formats (such as electronic documents, call centre logs, database text fields, emails, text messages, web searches, web sites, social media posts) into a single text corpus to gain an overview of communications, the themes or topics in a set of documents, or to automate the analysis of very large collections of text materials.

Mixed method analysis of quantitative and qualitative data providing added insight, depth and causal understanding.

Analysis of operational/administrative data (such as CRM database records, spreadsheets, web feedback forms, questionnaires, surveys, reports, documents, financial records, and emails) for:

• Monitoring and evaluation including real time analysis primary processes.
• Assessing effects of changes in policy and practice.
• Analysing business operations and processes to provide business intelligence and aid decision making.
• Objectively assessing the impact of particular interventions, services or projects.

Extraction of patterns, correlations, trends and anomalies from any available data source.

Using visualisation tools to both explore and analyse data, and to display the results to create clarity and enhance understanding.