Software development projects, especially data-intensive ones like machine learning, suffer from a high level of uncertainty as requirements and priorities tend to change frequently as more information becomes available. For this reason the field has adopted a collection of approaches known as Agile software development methodologies as an industry best practice.
Through several years of trial and error, Acorn has developed its own approach to Agile methodologies for the specialty that is AI and data engineering.
Acorn works with clients in iterative cycles, with individual cycles referred to as a “Sprint” or “Sprint Cycle.” Part of our onboarding process is to establish who will play what role in the partnership's success. Typical roles include:
The following are some of the examples of the work that we've done for clients using the Acorn Proven Process.
Solve Problems from the Past
Analyze the root cause of problems
Separate Causation from Correlation
Tell better stories with data
Migrate data out of old systems
Make Decisions in the Present
Make more Data-driven decisions
Improve Data Infrastructure
Build Better Dashboards & Scorecards
Automate boring and tedius tasks
Optimize in ways that free up cash
Predict and Plan for the Future
Anticipate Customer Behaviors
Implement Predictive Maintenance
Deploy Anomaly/Outlier Detection
Forecast the Future
Develop Data-Intensive Applications