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How to Use AI/ML with Python

Here’s the problem: Conventional dashboards aren’t making the most of the latest innovations in Artificial Intelligence (AI). Legacy Business Intelligence (BI) platforms report aggregated data, calculations like sums, counts, and averages.

These can provide valuable insights, especially for big-picture metrics like Key Performance Indicators (KPIs). However, they lack the kind of granular, in-depth insights of more advanced analytics. And the issue with the platforms that do offer the more advanced analysis is a lack of reporting capabilities and accessibility for business users.

Python and AI

Python offers a bridge between the sums and averages of traditional BI, and the more complex data science of AI and Machine Learning (ML). This means forecasting, recommendations, and more accessibility for business users. In other words: Better BI. Every business is different, requiring a bespoke approach to its BI analytics. That being said, here is a general roadmap for getting started with using python, AI, and ML for BI:

  1. What’s the goal?

    AI and ML are hot topics at the moment but you can’t just throw them at a problem and expect a solution (…yet). Defining the questions you want to answer is essential to employing AI correctly, as is writing the appropriate Python program.

  2. Get the right data:

    Garbage in, garbage out, as the old saying goes. The most advanced data science in the world cannot make up for an insufficient dataset. Again, it’s hard to give detailed advice given the unique demands of specific organizations, but ensuring proper collection, sampling, and cleaning methodology is essential. This article by AltexSoft goes into more detail on preparing your data for ML.

  3. AI methodology:

    This can be broadly categorized into predictive analytics and prescriptive analytics. Predictive analytics utilizes ML to forecast future outcomes; prescriptive analytics generates recommendations and actionable insights based on historical data. Python scripts can be used for both.

  4. Apply Machine Learning:

    If your BI task requires predictive or prescriptive analytics, consider implementing machine learning algorithms using Python libraries such as scikit-learn, TensorFlow, or PyTorch. Train and fine-tune models using your prepared data. Consider using a neural network and deep learning in cases where you’re looking to identify patterns and relationships in your data. 

  5. Visualize and interact:

    Use interactive dashboards and Data Apps to make the analysis more accessible to business users. Python libraries like Matplotlib, Seaborn, Plotly, or libraries for web frameworks like Flask or Django can be used to build visual interfaces. Check out some of the data apps built in Astrato, which combine the fields of data visualization and web application to produce powerful BI tools.

  6. Monitor, evaluate, and improve:

    Evaluate the accuracy and relevance of the predictions and recommendations of the AI and ML to ensure they align with your business objectives. Make improvements based on these evaluations, user feedback, and insights from new data.

Astrato: BI Dashboards with AI-Generated Advanced Analytics

Astrato combines conventional dashboard features with more granular analysis, delivered via Python and AI. The intuitive dashboard interface makes more advanced data investigation – features like forecasting, churn, and segmentation – accessible to business users. Additionally, Astrato is Cloud-native, meaning analyses and visualizations update as the data does, delivering the most accurate business insights possible.

Here’s Astrato’s AI Insights feature in action:

With Astrato, you don’t have to be a data scientist to get the benefits of Python machine learning and artificial intelligence. You can book a demo with Astrato to find out how it can improve your BI.