Published on

November 24, 2023

Business Intelligence

Search Driven Analytics: Actionable Insights Faster in Plain English

Search Driven Analytics can transform businesses by making data analysis more accessible. Read our step-by-step guide to take advantage of it today.
Giorgio Barilla
Digital Marketing Specialist
Business Intelligence

Analytics empower informed decisions, but making sense of endless data can be daunting, especially for non-technical users. Reliance on technical teams or canned reports often hampers agility.

Unlocking insights on your own is possible with Search Driven Analytics. This emerging approach puts intuitive, self-service analytics directly into business users' hands.

In this article, we'll see how plain English questions instantly produce visualized answers, no coding needed, thanks to large language models. AI and machine learning enable leading platforms to make data exploration easy for anyone.

Key Takeaways

  • Search Driven Analytics empowers users to explore data through conversational, natural language questions. AI and ML rapidly generate visualizations and insights.
  • It eliminates reliance on technical teams for analysis. Business users can independently access timely, tailored reports to drive faster, data-informed decisions.
  • Platforms like Akkio allow self-service data exploration via features like Chat Explore. Teams can now quickly uncover trends and patterns that impact strategic business choices.

Understanding Search Driven Analytics

Users can simplify data exploration with Search Driven Analytics by posing questions in natural language and receiving visualizations as answers.

Imagine being able to type a query like “what was our net revenue last quarter?” into a search bar and receiving an insightful visualization in response. This powerful approach democratizes data analysis, making it accessible to a broad range of business users across various fields like sales, marketing, finance, and healthcare.

Our customers are using Chat Explore, a leading GPT-4 powered search-driven analytic tool, to generate reports on sales outcomes, marketing insights, and full-blown dashboards from simple requests.

The distinction between Search Driven Analytics and traditional analytics tools lies in how search-based analytics work. Unlike conventional tools that need structured input or manual data manipulation, Search Driven Analytics uses natural language processing (NLP) to understand user queries and pull relevant data from databases.

How Search Driven Analytics Works

NLP is a technology that enables computers to comprehend human language, which is essential in search-driven analytics to uncover hidden insights from unstructured data. NLP translates human language questions into queries that the analytics engine can interpret, enabling natural language search capabilities.

Implementing search-based analytics can be tricky due to differences in data, metrics, KPIs, terminology, and industry knowledge across companies. However, leading search analytics platforms are designed for ease of use by business users of all technical skill levels.

Platforms like Akkio stores all kinds of tabular data and integrate with your existing tech stack. Once the data is in the system, we help you clean it, visualize it, and even create machine learning models with it. Everything happens without coding.

Key Components of a Search Based Analytics Platform

Beyond NLP and machine learning, search-driven analytics platforms utilize AI to:

  • Automate manual work - Handle time-consuming data prep, reporting, and monitoring tasks to increase productivity.
  • Enable semantic search - Analyze context and user intent behind queries to deliver the most relevant insights from massive datasets.
  • Index data - Efficiently crawl multiple siloed sources, cataloging and organizing information for rapid access across the organization.

Additional AI capabilities create further value:

  • Augmented analytics - AI suggestions enhance human analysis with smart data prep, model building, and metric recommendations.
  • Continuous learning - Platforms learn from user behavior to surface personalized, relevant insights over time.
  • Predictive analytics - Forecast future outcomes and prescribe actions based on detected trends and patterns.

The Benefits of Search Driven Analytics for Businesses

With intuitive access to insights, Search Driven Analytics accelerates data-driven decision-making across the business.

It empowers cross-functional teams to independently analyze unstructured data, make timely calls, and capitalize on fleeting opportunities. Organizations become more responsive and agile.

Empowering Business Users

Search Driven Analytics democratizes data analysis by:

  • Reducing reliance on technical specialists - Business teams like marketing, sales, and finance can directly access insights as needed rather than wait for reports.
  • Accelerating time-to-insight - Users get answers in real-time instead of days or weeks. Rapid exploration leads to quicker decisions.
  • Driving adoption across organizations - Simple search encourages usage across departments, increasing analytics ROI.

Accelerating Data-Driven Decisions

Search Driven Analytics provides businesses with immediate access to insights, allowing them to swiftly respond to market changes and find new opportunities. This accelerated decision-making process offers several benefits, including:

  • Time savings: an obvious one, but important to highlight. Service providers, in particular, can benefit from massive cost savings, enabling them to onboard more clients and provide a better service in less time;
  • Increased trustworthiness in decision-making: AI platforms like ours typically explain the "logic" behind every graph and response, reducing inaccuracies. You won't need a data scientist to learn what's behind a data point anymore;
  • Proactive decision-making: data insights are not just good to look at - you need to actually take action on them. This is where an end-to-end machine learning platforms really shine. With specialized solution such as Akkio, you get to use the data to generate machine learning models, ship models in data warehouses, and outperform your competitors in lead scoring, ROI forecasting, and so many more use cases;
  • Cost savings: Akkio is fairly priced and starts at $49/m after a 2 weeks, no commitment, free trial. No credit card required;
  • Improved customer experience: say goodbye to sleepless nights because the client wants to know what's the ROAS of all campaigns by yesterday. Simply provide a public link to the search based analytics platforms, and they can generate the insights on their own - 24/7. And yes, you can white-label the platform.

Chat Explore: Revolutionizing Data Exploration

Chat Exploreallows collaborative, conversational data analysis between teams thanks to powerful large language models (LLM) plus GPT-4, the industry-leading API from OpenAI.

Let's dive in and see how Chat Explore works in practice, starting from a dataset on 30,000 Spotify songs (by Kaggle, download it here).

Upload the Dataset

Uploading a dataset into Akkio

Akkio supports CSV files and integrates with a plethora of platforms, including Google Analytics 4, Airtable, Redshift, BigQuery, and Snowflake. Explore all available integrations here or suggest a new one by contacting us inside the app.

Describe the Goal of Your Project

Generative Reports in Akkio

Let's try to find out if song popularity changes overtime, with the following prompt:

Use the album release date to see how song popularity changes over time. Is older music making a comeback, or does new music dominate?

That's all. Akkio will do the rest in the background using Generative Reports.

Navigate to Chat Explore

Navigating to "Chat Explore" in Akkio's dashboard

Once you're ready to explore your data further, navigate to "Explore" in the top-level menu, which will prompt you to the conversational analytics tools.

Search-driven Analytics in Akkio

Here, you can switch between GPT-3.5 (faster) and GPT-4 (more accurate) and get started. Akkio provides a preset of questions you might want to answer, but you can start from scratch by typing in the input field.

If other people need access to the data, and they're not part of your team, you can:

  1. Create a Slack Channel: all members will be able to chat with the dataset;
  2. Share a Public (or private) URL: we'll generate a link for quick access. You can white-label the tool if you're a service provider.

For example, let's try with a couple of questions:


Genre Dominance: Break down which genres and subgenres have the highest average song popularity. Is pop more popular than rock? Does indie have a stronghold?


Akkio creating a bar graph for genre dominance in a demo dataset of spotify songs


Top Tracks: List the top 10 or top 20 tracks by popularity, breaking them down by genre or artist as well.


Akkio creating a colored stacked bar graph for genre dominance in a demo dataset of spotify songs

Enhancing Search Driven Analytics with AI and Machine Learning

Augmented analytics is a next-generation approach that applies AI and machine learning to amplify human data analysis. It works by using technologies like NLP and machine learning to:

  • Automate manual data preparation tasks like cleaning and normalization.
  • Provide smart suggestions that enhance human insights - like relevant visualizations for query results.
  • Generate predictive models on its own based on detected patterns.
  • Summarize key information like trends, outliers, and correlations in data.

Data Preparation with AI

Switching to Chat Data Prep

Long are the days where all data preparation needed to happen in Excel. Nowadays, many extensions lets you bring GPT-superpowers in your data sheets. But, if you want to take this a step further, end-to-end machine learning platforms like Akkio can help.

With Chat Data Prep , our large language model help you clean your data from outliers, update date formats, remove or add columns, concatenate columns, and even translate rows. It's located one tab before "Chat Explore".

For example, let's ask the AI to create a custom metric to quantify a playlist's "effectiveness" in promoting tracks.


Create a custom metric that combines song popularity and the number of songs an artist has in a playlist to quantify a playlist's "effectiveness" in promoting tracks. Add the metric in a new column.


highlighting the column with the added metric in Akkio

If you're unsure about what happened, don't worry, Chat Data Prep offers an AI interpreation next to the prompt and doesn't apply any transformation until you click "Apply".

chat data prep explanation

Smart Suggestions that Enhance Human Insights

It's all fun, but what if you don't know what to look for? What if everything you're working on is alien data to you?

Well, that's why we specified a goal at the beginning. While we were preparing data, generating visualizations, and generate effectiveness metrics, Akkio built this generative dashboard for us:

Showing the reports screen in Akkio

This time, the AI prompted itself to generate multiple reports to match my query: Use the album release date to see how song popularity changes over time. Is older music making a comeback, or does new music dominate?

Do you want to take a peek? Here's a public URL to the dashboard. We added the other charts we generated in the article as well.

Generate Predictive Models

Predict and Forecast options in Akkio

Data insights can help, but actionable models can revolutionize how you serve your customers and/or operationalize your systems in a much more profound way.

Akkio lets you predict and forecast without coding knowledge. No need for data science degrees here.

Let's try it out by training the model to predict the "popularity";

Creating a model in Akkio
The insights report in Akkio

Our model is ready, and Akkio provides a full insights report to understand more about the performance, top fields, top factors, and segments. Feel free to take a look here.

Deploy Models Without Coding

Switching to the deployment tab in Akkio

You can deploy models into your HubSpot instance (perfect for lead scoring), Google Sheet, or big data warehouses like Snowflake and BigQuery. For a quick test, we deployed this one as a web app, which you can embed in any article or web page.

a model deployed as a web app

Here's the embed of the web app:

Have fun experimenting and we hope you can find the next hit song!

Other Applications of AI-Enhanced Search Driven Analytics

AI-enhanced Search Driven Analytics can be applied in various areas, such as:

  • Sales performance analysis: involves examining sales data to detect trends and patterns that can be leveraged to enhance sales performance.
  • Customer segmentation: the process of dividing customers into distinct groups based on common characteristics, including demographics, interests, and behaviors.
  • Financial forecasting: using AI algorithms to analyze financial data and predict future financial outcomes.


In conclusion, Search Driven Analytics has the potential to transform businesses by making data analysis more accessible and empowering non-technical users to make data-driven decisions quickly and confidently.

Platforms like Akkio, with its revolutionary Chat Explore feature, are leading the way in leveraging AI and machine learning to enhance Search Driven Analytics capabilities.

By implementing best practices and harnessing the power of AI-enhanced Search Driven Analytics, businesses can unlock deeper insights, make better decisions, and stay ahead of the curve in today’s competitive landscape.

Try Akkio for free today. You have 14 days to decide, no credit card required, and no commitment.

By clicking “Accept”, you agree to the storing of cookies on your device to enhance site navigation, analyze site usage, and assist in our marketing efforts. View our Privacy Policy for more information.