DATA ANALYTICS +
17 years helping organizations recognize the most critical and valuable business metrics—revenue, profitability, and risk metrics—by extracting insights from data to make informed decisions, therefore identify opportunities to build innovative business strategies.
Machine Learning solutions
Power Your Company with
GLF main areas applying Data Science in Business.
- Digital Marketing Strategy
- Data Management Consulting
- Data Cleasing
- Reputation Management
- Website Design
- Protection of Digital Copyrights
- Enterprise Risk Management Assessment
Our multidisciplinary consultants have more than 25 years of experience working on business strategy for data-driven companies.
Olga Gould. Instructor at Gustavson School of Business, Continuing Studies Department and the Faculty of Engineering and Computer Science at the University of Victoria
Andres Guzman. Partner at Schälli Law Firm
How IT Works
Data Science Solutions
For Your Company
Depending on the Data Science project we use a hybrid between CRISP-DM + Agile Scrum, which brings benefits such as fast stakeholder feedback and flexibility for change in the solution delivery requirements.
CRISP-DM is the Cross-industry standard process for data mining, an open standard process model that describes common approaches used by data mining experts. It is the most widely-used analytics model.
This life cycle project model consists of six phases with arrows indicating the most important and frequent dependencies between phases. The sequence of the phases is not strict. In fact, most projects move back and forth between phases as necessary. CRISP-DM guides data science projects in what to deliver and in what sequence, while Agile help us on how to deliver working product successfully in terms of both efficiently and stakeholder satisfaction.
Assessing: Business Backgrounds. Business objectives. Business success criteria. Competitors. Industry insights. Stakeholders. Resources. Risks. Scope. Determine appropriate analysis method.
Gathering data. Describing data. Exploring data. Verifying data quality and availability.
Selecting data from multiple sources. Cleaning data. Constructing data. Integrating data. Formatting data.
Exploratory Analysis and Modeling
Selecting modeling techniques: Designing tests. Building model. Assessing model.
Evaluating results. Reviewing the process. Determining the next steps. If results are valid proceed to step 6. If results are invalid return to step 1 to 4.
Deployment and Visualization
Communicate results. Data Storytelling. Determine best method to present insights based on analysis and audience. Planning deployment. Planning monitoring and maintenance. Reporting final results. Reviewing final results. Documentation. Recommendations.
What Kind of
Services We Provide
We produce financial, operational and market intelligence by querying data repositories and generating periodic reports. We devise methods for identifying data patterns and trends in available information sources.
We design advanced data visualizations with Business Intelligence tools or Decision Support Systems such as ad-hoc reports, dashboards and data integration with relational databases in real-time processing through frameworks such as Hadoop in Data Warehousing processing.