An important initial step in data projects is to plan out a data strategy in order to meet the business’s long-term goals, scaling and performance needs, and compliance requirements.
We help create a business-driven data vision that is aligned to the goals of your organization and we help you develop a roadmap to get there. This can include
- planning and architecture
- data security and access control
- compliance with laws; industry best practices
- data governance
- needs assessment, business transformation design
- architectural recommendations, product evaluations, organizational strategy,
legacy migration plans
- change management and driving towards a “data-driven organization”
Building resilient data systems is the backbone of all strategic data initiatives.
Depending on the engagement, Qbiz typically can help with:
- planning, architecting and building data pipelines, data lakes, data warehouses, data marts
- ETL/ELT (Extract-Load-Transform)
- cloud infrastructure (upgrade, migration from legacy systems)
- access control and retention policies
- tool selection and best practices
- Spark, Hadoop, Hive, Presto, Kafka, Apache Airflow, Pig, Yarn
- Amazon Web Services (AWS), Microsoft Azure and Google Cloud Platform
- PostgreSQL, MySQL, Redshift, Athena, Aurora, Snowflake, BigQuery, Oracle, Vertica, Microsoft SQL Server, Teradata, Aster data, MongoDB, Apache Cassandra, DynamoDB
- SQL, Python, Scala, Java, Clojure, Ruby, C++/C#, R coding
Data analytics and visualization
When it comes to data projects, organizational results is everything. Developing impactful data analytics and visualization requires a combination of technical expertise, knowledge and insight.
Qbiz has an unrivaled track record of building systems that deliver consistent and authoritative business function reporting – enabling self-service across the enterprise.
We are not married to any one platform. Instead we have expertise across many toolsets and we regularly help our clients make informed technology choices. Our consultants have hands-on experience working with Tableau, Looker, Qlikview, Omniture, Google Analytics, Shiny, Power BI, Microstrategy, Cognos, Salesforce, and many other platforms.
When it comes to large volumes of data, it is essential to understand and to extract vital information hidden in it. Example engagements include applying statistical and predictive models and machine learning algorithms to generate business insight, such as:
- Customer journey analysis, engagement and predicting churn
- Recommendation engines for up-sell/cross-sell
- Anomaly detection to detect fraud and errors
- Automated manufacturing sensor analysis to preempt line failures
- Product optimization and experimentation
- NLP (Natural Language Processing) for media analysis, product feedback, or combing chat logs