Ada from BoostKPI is a privacy-first, comprehensive AI data analyst that excels at reporting, generating insights, and finding root causes of KPI anomalies in high cardinality, time series datasets.
Today marks a huge leap forward in data analytics, as we officially unveil Ada (for Ai Data Analyst) - the first privacy-first AI Data Analyst, revolutionizing how businesses interact with their data.
🔍 Why Ada?
In an era where data privacy is paramount, Ada stands out by offering comprehensive insights without compromising your data's confidentiality. While existing tools like analytics dashboards highlight the “known knowns” and make it possible to check the “known unknowns”, Ada uncovers the important “unknown unknowns” that are often overlooked. With the capability to sift through billions of data segments, Ada identifies the root causes of your business challenges, providing actionable insights like never before.
💡 Features that Set Ada Apart:
- Unparalleled Analysis: Delve into billions of data segments to uncover the root causes impacting your business.
- Cost-Effective Solution: At 10x cheaper than hiring a data analyst, Ada is not just advanced but also economically advantageous.
- Granular Alerting: Stay ahead of the curve with Ada's precise alerting, ensuring you're always informed about the critical metrics that matter most.
- Proven Track Record: Trusted by public companies like Wish.com and unicorns like Smugmug.com, Moloco.com, and InMobi.com, Ada is not just a tool but a transformational force in data analysis.
🎉 Try Ada Today
With GPT integration, you can try out a limited version of Ada at https://chat.openai.com/g/g-sKhL... . Just upload a csv file at the GPT store or use one of the demo datasets to get going. Learn more at https://docs.boostkpi.com/docs/d... For a free POC, go to https://boostkpi.com Ada integrates with multiple data warehouses like Amazon RedShift, Google BigQuery, Databricks, and Snowflake.
Join us on this exciting journey and see why Ada is the game-changer your business has been waiting for.
@debabrata_dash Thank you for the support.
The inspiration for applying GPT to structured data stemmed from a desire to close the gap between the complexities of data analysis and its accessibility. By leveraging GPTs, we can offer a user-friendly interface that simplifies the process of querying, analyzing, and understanding structured data. This innovation enables businesses to harness their data more effectively, eliminating the need for deep data science knowledge.
To maintain reliability and prevent inaccuracies, we've implemented measures to avoid hallucinations and ensure accurate outcomes for our users. In advanced analyses, such as our drilldown feature, which identifies key segments contributing to most changes from potentially millions or billions of possibilities, we adopted an orchestration strategy. Here, GPT serves as the user-facing component, supported by a specialized ML algorithm designed to handle billions of segments swiftly. This approach, combining multiple ML algorithms with GPT, guarantees an exceptional user experience.
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Super interesting @amitmanjhi ! Congrats on the launch. Curious how accurate is Ada over say running specific queries? Deep insights on data would in most cases drive business decisions, hence getting the accuracy right would be God-send for me.
@animeshs Thanks for the support.
Behind the scenes, Ada is indeed running a combination of code and (SQL) queries. As you rightly pointed out, accuracy is is crucial, especially because the insights provided guide business decisions.
Remember you can ask Ada a bunch of questions. You can ask a variety of questions related to analyzing datasets, especially focusing on changes in key performance indicators (KPIs) over time. Here are some examples of the types of questions you might consider:
1. **General Analysis Questions:**
- Can you show me how revenue has changed over the last quarter?
- What are the top-performing brands in terms of orders in the past month?
- How has the average session length varied by device type over a specific period?
2. **Comparative Analysis Questions:**
- Can we compare the performance of different marketing channels on order volume in two distinct periods?
- What is the impact of different ad sets on Facebook marketing campaign performance month-over-month?
- How do different countries contribute to ecommerce revenue changes year-over-year?
3. **Drill-Down Questions:**
- Can you drill down into the data to identify what led to a significant drop in impressions in our ad tech data?
- I noticed an increase in product views; can we drill down to see which channels are driving this?
4. **Trend Analysis Questions:**
- What are the emerging trends in device types used for our service, based on session data?
- Are there any notable trends in ecommerce orders by country and channel?
5. **Predictive Analysis Questions:** (Note: While predictive analysis might be more complex, you can still ask for insights or patterns that might hint at future trends.)
- Based on past performance, which products might see an increase in orders next quarter?
- Are there any patterns in ad click-through rates that could inform our next campaign's targeting strategy?
Remember, you can also choose a dataset from the provided demo datasets for analysis if you don't have your own data ready. These include datasets on ecommerce, subscription services, Facebook marketing, ad tech, and data quality, each with its own set of dimensions and KPIs for detailed analysis.
(This is an actual answer from Ada in response to "what questions can I ask?")
Ada by BoostKPI
Ada by BoostKPI
Ada by BoostKPI
Ada by BoostKPI
Ada by BoostKPI
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Ada by BoostKPI
Ada by BoostKPI
Ada by BoostKPI