Build the business case for analytics
Begin to understand the value buried within your data through a concentrated effort to answer a handful of pressing business questions. Use real results to justify a larger investment in analytics.
Fill the data engineering gaps
The majority of analytic projects are consumed by data prep. We provide the data engineering resources and toolkits required to quickly move from organizing to analyzing your data.
Iteratively build analytic muscle
Boil the ocean approaches are often expensive disappointments. Our sprint-based methodology enables you to gradually uncover and address gaps, allowing you to steadily scale your analytics efforts.
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Identify high-value business questions that can be answered with analytics
Collect required data sources and unify for analysis
Analyze & Answer
Analyze to provides answers and guide further exploration
Analyze the process to assess gaps and improve velocity
Refine and move to more complex questions
Identify high-value business questions
A high-impact analytic exercise starts with understanding the questions that are worth answering. Then collaborating to narrrow down the list to prioritize and execute on questions that will help propel your business and analytic aspirations to the next level.
Organize the required data
First, relevant input sources and attributes are analyzed to determine the source owners to engage. Then we apply best-of-breed tooling to unify the data and make it easily usable for downstream analysis. During this process, we identify gaps that can hinder the scaling of future data engineering and analytics efforts.
Analyze & answer the question
Now that the data has been unified, it can be analyzed to begin answering the question raised at the beginning of the sprint. We provide an initial set of analytics to facilitate the discussion but rely on you to challenge the outcomes and provide context that lives outside of the data. The goal is to answer the question and begin defining business actions, or agree on a set of questions to answer more deeply.
Review and learn from outcomes
Our goal is to enable you to scale up your DataOps and analytics capabilities. We allocate dedicated time at the end of each sprint to review successes and failures, with an emphasis on the bottlenecks that are hindering analytic velocity. This includes assessing the technology & people gaps that exist throughout your analytic process and helping you define an ideal architecture.