Transitioning Beyond BI: Catapult Your Data Team and Business Outcomes with Machine Learning
Advanced Business Intelligence (BI) teams excel in constructing robust data pipelines, data modeling, and empowering data analysts with self-service capabilities. As the modern data stack becomes commonplace, these capabilities are table stakes for data teams. The question emerges: What’s next?
While Machine Learning (ML) has been available for some time, many BI teams have yet to embrace it. Integrating ML not only enhances BI teams’ skill sets but also positions them as proactive agents of change, driving tangible business outcomes.
So how can you get started with ML:
The challenge lies in initiating ML adoption within your business teams, especially when business leaders may be unfamiliar with ML’s potential. To bridge this gap, focus on selecting specific use cases to showcase ML’s impact on business improvements.
Here are a few examples:
Predict the probability of opportunity closure using Salesforce funnel data.
Predict orders by leveraging historical order trends and funnel data.
Predict shipment volumes or time to deliver for manufacturing companies.
You can come up with many such examples for your company based on your industry like churn potential, propensity to buy, inventory predictions, fraud detection etc.
How do you go about building your first model?
Here are some high-level steps on how to build your first set of models -
Understand the Data and Define Predictions:
Thoroughly understand the data and define predictions in collaboration with business stakeholders to align with strategic goals.
Prepare the Dataset:
Cleanse, preprocess, and transform the dataset to enhance its quality and relevance for model training.
Choose the Right Model:
Select ML models based on the nature of the problem, such as decision trees, random forests, or gradient boosting.
Train and Test the Data:
Split the dataset, train the model, and evaluate its performance using testing sets. Adjust hyperparameters iteratively.
Predict the Future:
Apply the well-trained model to new data, evaluating accuracy and making refinements for reliable predictions.
Store the Predictions:
Integrate the ML model into the data infrastructure and store predictions in a database for accessibility and future analysis.
Schedule to Run After Data Refreshes:
Automate the ML model to run regularly after data refreshes, ensuring continuous relevance and accuracy.
Visualize the Data and Predictions:
Create meaningful visualizations to help both data teams and business users understand complex patterns in the dataset and predictions. Help the business users and executives to use the predictions in the form of the data visualizations in their daily processes and decision making.
Snapshot the Dataset and Predictions:
Capture a comprehensive snapshot of the dataset and predictions for showcasing how the prediction is trending over time. Users will ask what the prediction at the beginning of the quarter was. You cannot answer this without capturing daily snapshots.
Socialize your model with Business Users:
Demonstrate the benefits of utilizing ML by showcasing how its predictions can guide decision-making. This includes identifying optimal projects to work on, prioritizing the right customers to focus on, and streamlining resource allocation for maximum impact. Illustrate how ML-driven insights enhance precision in decision processes.
Iterate to Tune the Model:
Gather feedback from business users and iteratively fine-tune the model based on their insights, fostering continuous improvement and collaboration.
What tools can you use to get started?
Keep it very simple to get started.
Python and Jupyter Notebooks: Use these tools for data exploration, analysis, and initial model prototyping.
Matplotlib and Seaborn: Visualization libraries for creating insightful charts and graphs.
Scikit-learn: Offers tools for data preprocessing, such as handling missing values and feature scaling.
If you are a Snowflake customer once the model is built the deployment and scheduling is easy using Snowpark Python and ML features. You can store the model as a stored procedure and schedule it as a task within Snowflake and can trigger it after the data refreshes. I will write more about this in a later post.
Summary
In summary, as the next evolution for your data and BI team, harness the power of ML to provide your business users with actionable insights, aiding them in making data driven decisions. Moving into Machine Learning provides the opportunity for BI teams to build stronger relationships with your executives and stakeholders. Don’t wait for them to ask; take the first step, by proactively building models and showcasing your team’s capabilities to help drive your company’s business strategy.
