Going Further, Together with AI-driven Sentiment Analysis (Infographics)

Enterprise AI adoption is accelerating, but governance maturity is not keeping pace. According to a 2025 research report by Infosys on Responsible Enterprise AI in the Agentic Era, 95% of enterprises reported AI-related incidents in the last two years, while only 2% met the " responsible AI “gold standard readiness levels. Another 2025 study on the state of AI security found that 70% of organizations still lack optimized AI governance frameworks.

This gap explains why enterprises can no longer treat AI governance as a compliance checkbox. Beyond avoiding regulatory penalties, governance today is about ensuring reliability, accountability, security, transparency, and business resilience as AI becomes embedded into core operations.

For instance, the dashboard given below mirrors a project undertaken for a client seeking insights into the pandemic's impact on their business across specific areas. They wanted to determine the number of stores stocking their product within a defined radius, highlighting the local business impact amid the pandemic.
To craft the map showcased in this dashboard, we leverage Tableau's map layers feature introduced in version 2020.4. For further insights into this functionality, additional details can be found here.
Prior to initiating the map creation process, frequently refer to the Profit Margin field. Here's the calculation for this field: it computes the percentage of Sales that translates into Profit. This calculation enables us to gauge the profitability derived from our sales figures.
For the States map layer, the State field is utilized and placed on the 'Detail' shelf. Each state is color-coded based on its Profit Margin.
Moving to the Cities layer, the City field is added onto the top left area labeled "Add a Marks Layer." To ensure the visibility of every city, the State level of detail is included as well. This accounts for cities existing in multiple states, displaying every city/state combination. Cities are color-coded using the Profit Margin field, with additional color based on the absolute value of the Profit Margin. This helps visualize the range and direction of profitability for each city.

Buffer Calculation

The Buffer calculation generates a radius, known as a "buffer," around a specific map point, defined within the syntax parameters. Here's the syntax breakdown for the Buffer: The initial part determines the center location, followed by the distance around the point, and finally, the chosen unit of measurement.
To establish the desired centroid point, we employ the Makepoint function. This function simply utilizes latitude and longitude coordinates to generate a point on the map. Below is the calculation illustrating its usage.

To achieve the interactivity you desire, you'll begin by creating three parameters: [Location Lat], [Location Long], and [Radius]. These parameters offer flexibility, allowing you to adjust them within the dashboard interface.
As you click on different cities, the [Location Lat] and [Location Long] fields dynamically change, altering the central point. Meanwhile, the [Radius] field, functioning as an input parameter, enables you to modify the radius distance according to your preferences. This setup grants you personalized control over these parameters directly within the dashboard.

With the creation of the final map layer field, you can now drag this field to the top left of the map and add it to the existing layers. Once done, you'll have all the map layers integrated into the map, allowing you to recreate the dashboard as depicted below. This comprehensive setup will mirror the dashboard layout and functionality.

Parameter Actions

Parameter Actions are essential at this stage to ensure dynamic interaction within the map layers. By implementing parameter actions, we enable the Location Lat and Location Long fields to adjust dynamically when clicking on a city. This action directly affects the MAKEPOINT() field within the Buffer calculation, effectively altering the radius location. Below, you can observe the setup of the parameter action and how it facilitates this dynamic transformation.
Finally, we aim for these parameters to influence the available metrics showcased at the top of the dashboard. These metrics offer insights into the concentration of profit and profit margin within the selected radius. Below, you'll find the supporting calculations and the formulae for the metrics displayed on the dashboard. These metrics serve as indicators of profitability and profit margin concentration within the chosen radius.

Wrapping up, creating interactive data visualizations opens doors to explore and comprehend information, fostering informed decision-making and exploration of new analytical paths.

Semi-structured Formats with Snowflake Support

• • JSON
• • Avro
• • ORC
• • Parquet
• • XML

• • Variant
• • Flatten

Variant

A variant is a datatype which can hold semi-structured data in a single field. Snowflake stores semi-structured data in the column format when semi-structured data is loaded into a VARIANT column. A single row can contain other underlying data. Let’s demonstrate with the help of examples:

The stored data can be easily retrieved and structured from the above table by a simple query as below:
select
v:time::timestamp as observation_time,
v:city.id::int as city_id,
v:city.name::string as city_name,
v:city.country::string as country,
v:city.coord.lat::float as city_lat,
v:city.coord.lon::float as city_lon,
v:clouds.all::int as clouds,
(v:main.temp::float)-273.15 as temp_avg,
(v:main.temp_min::float)-273.15 as temp_min,
(v:main.temp_max::float)-273.15 as temp_max,
v:weather[0].main::string as weather,
v:weather[0].description::string as weather_desc,
v:weather[0].icon::string as weather_icon,
v:wind.deg::float as wind_dir,
v:wind.speed::float as wind_speed
from json_weather_data
Note: ‘v’ is the Field (Column) name, and the JSON tags are arranged alongside to retrieve datasets.

Flatten

Compound values are flattened into multiple rows with the use of the Snowflake Flatten Command. A tool called Snowflake FLATTEN is used to transform semi-structured data into a relational structure. Relatively complex JSON structures such as nested JSON structures can be structured with FLATTEN function in Snowflake. Here is an example of the same: The above JSON file stores a single set of conversations between two texters in a single row. If the requirement is to get every individual message as a separate row a FLATTEN function can be used to produce the following result:

Here is the query to generate the above table from semi-structured data:

Summing Up

The ability of Snowflake’s assistance is undeniable. It aids you to make better decisions and offers a better overall experience when attempting to get the most out of your data, through features like Flatten and Variant. If you want to be a part of Snowflake’s incredible platform, let us help you. Beinex’s partnership with Snowflake enables us to offer you advanced features like automated tuning and elastic computing, along with analytics modernization services, to help your organisation realise exponential Return on Investment.

Top Differences between Dynamic Set and Fixed Set

1. Set members change when the underlying data changes.
2. It has a single dimension.

1. Set members do not change.
2. It can be single-dimensional or multidimensional.

Steps to Create a Dynamic Set

The process to create a dynamic set is as follows: In the Data pane, right-click on the sub-category dimension and choose Create > Set. (Figure 1)

Figure 1: Creating a Set

• In the Create Set dialog box, set up your set. You can configure it using the following tabs:
1. General: Use the General tab to choose one or multiple values to be considered when computing the set. Alternatively, you can choose the Use All option to consistently consider all members, even when new members are added or removed.

If you know the top-selling products beforehand, you can manually select the products as shown in Figure 2 below.

Figure 2: Creating a Set using General Tab

2. Condition: Utilize the Condition tab to establish criteria that decide which members should be incorporated into the set.

You can specify this condition and create the set if you need products with sales greater than $50,000. (Figure 3)

Figure 3: Creating a Set using Condition Tab

3. Top: Employ the Top tab to set restrictions on which members should be included in the set.

For instance, you can establish a limit based on total sales, where only the top 5 products with the highest sales are included. (Figure 4)

Figure 4: Creating a Set using Top Tab

• Once you have completed the configuration, click the "OK" button.
• The newly created set will appear at the bottom of the Data pane within the Sets section. You can identify it by the set icon, which denotes a set field.

Steps to Create a Fixed Set

The process to create a fixed set is as follows:

• In the developed visualisation, select one or more marks from the view (Figure 5).

Figure 5: Selecting the marks.

• Right-click on the selected mark and select “Create Set” (Figure 6).

Figure 6: Creating the set.

• Type a name for the developed set (Figure 7).

Figure 7: Typing the Set Name.

• When finished, select “OK”. This newly created set can be accessed from the data pane. When this set is placed in the filter, the view will be filtered to show only the relevant set values.

Top Benefits of Sets in Tableau

• Top N or Bottom N Analysis: Sets can filter the data to display only the top or bottom N values based on a specific condition. For example, you could create a set to show the top 10 products by profit or even combine sets and display the top N and bottom N products by profit in a single chart. (Figure 8)

Figure 8: Top 3 and Bottom 3 Products by Profit

• Segmentation Analysis: Sets can also segment data into groups based on a specific condition. This can be useful for analysing performance differences between different groups. For example, you could create a set to segment customers based on their geographic location.

• Excluding Data: Sets can be used to exclude specific data points from a visualisation. For example, you could create a set to exclude customers who have not purchased in the last six months.

What is Set Actions in Tableau

Set actions allow users to modify the values within a set, on selection of marks within a view. This enables your audience to engage directly with a visualisation or dashboard and control various aspects of their analysis.

To utilise set actions:

1. Create sets associated with your data source.
2. Build set actions using the created sets.
3. Optionally, create calculated fields that incorporate the sets.
4. Construct visualisations referencing the sets.
5. Test and adjust the set actions for desired behaviour.

To create a set action that helps in drilling down category:

1. Create a set that selects a particular category (Figure 9 shows creating a set using the general tab selecting only furniture)

Figure 9: Creating Category Set using General Tab

2. If you are in a worksheet, go to Worksheet > Actions.
If you are in a dashboard, go to Dashboard > Actions.

3. In the Actions dialog box, click “Add Action” and choose "Change Set Values."

4. In the Add/Edit Set Action dialog box:

• Provide a descriptive name for the action.
• Choose a source sheet or data source. By default, the current sheet is selected. If you opt for a data source or dashboard, you can select specific sheets within it.
• Choose the desired method for users to execute the action:

1. Hover: The action will trigger when a user hovers the mouse cursor over a mark in the view.
2. Select: The action will activate when a user clicks a mark in the view.
3. Menu: The action will initiate when a user right-clicks (or control-click on Mac) a selected mark in the view and then selects an option from the context menu.

• To specify the target set:

1. First, choose the data source from the available options.
2. Then, select the desired set from the Target Setlist.

Figure 10: Setting up set action

• Specify what happens when the action is run in the view:

1. Assign values to set - Replaces all values in the set with selected values.
2. Add values to set - Adds individually selected values to the set.
3. Remove values from the set - Removes individually selected values from the set

• When the selection is cleared in the view:

1. "Keep set values" will retain the current values in the set without any changes.
2. "Add all values to set" will include all possible values in the set.
3. "Remove all values from set" will remove all previously selected values from the set.

5. After configuring the desired behaviour, click "OK" to save the changes and return to the view.
6. To ensure the set action functions as intended, interact with the visualisation, and test its behaviour.

Benefits of Set Actions

1. Filtering: Set actions can filter data based on user selections. For example, you could create a set step that filters the data to show only the top 10 customers in a particular region.
2. Highlighting: Set actions can also highlight data based on user selections. For example, you could create a set action highlighting all the customers who have purchased in a particular month.
3. Drill-downs: Set actions can create drill-downs that allow users to explore the data in greater detail. For example, you could create a set action enabling users to drill down from a high-level view of category by sales to a more detailed view of sales by sub-category. (Figure 11)

Many businesses make a concerted effort to create work cultures that increase job happiness, encourage people to find meaning and satisfaction in their work, reward and recognize employees for their actions, and foster both personal and professional growth.

While many tactics are adopted for the company’s benefit and to considerably increase retention, leaders cannot rely only on them. They must face the uncomfortable reality that they will eventually lose significant talent if they do not keep an open mind and a realistic outlook on the future. Influential leaders act daily to safeguard themselves, their teams, and their companies from the risk of attrition because wishful retention thinking is not a viable business strategy.

Is it possible to foresee attrition so that it only impacts the business a little less, given that it is impossible to stop employees from leaving? Well, with the help of technology, it is possible. The churn model, among others, can help in this situation. Are you wondering how? Through an employee churn prediction model, we can make it happen. After understanding which employees are on the verge of leaving using the churn model, it is possible to reach out to them and understand their grievances.

Employee Churn Prediction Model

It's a predictive model that calculates the likelihood (or vulnerability) of each employee leaving. It tells us how likely we will lose employees or a specific employee in the future at any given time. It classifies employees into two groups (classes): those who quit and those who don't. It will typically tell us the probability of the employee belonging to which of the groups in addition to placing them in one of the two groups. Thus, a churn model can be used to estimate the chances of resignation.

Explaining the Model

Modern churn models frequently draw their foundation from machine learning, more specifically from binary classification methods. There are several of these algorithms; therefore, it's important to test which one works best in each circumstance. Here we have made use of four machine learning models:

Random Forest

Supervised machine learning algorithms like random forest are frequently employed in classification and regression issues. On various samples, it constructs decision trees and uses their average for classification and majority vote for regression.

The Random Forest Algorithm's ability to handle data sets with both continuous variables, as in regression, and categorical variables, as in classification, is one of its most crucial qualities. In terms of classification issues, it delivers superior outcomes.

KNN

One of the simplest machine learning algorithms, based on the supervised learning method, is K-Nearest Neighbour. The K-NN algorithm assumes that the new case and the existing cases are comparable, and it places the latest instance in the category that is most like the existing categories.

A new data point is classified using the K-NN algorithm based on the similarities after storing all the existing data. This means new data can be quickly and accurately sorted into a suitable category using the K-NN method. Although the K-NN approach is most frequently employed for classification problems, it can also be used for regression.

Decision Tree

The supervised learning algorithms family includes the decision tree algorithm. The decision tree technique, in contrast to other supervised learning methods, can handle classification and regression issues.

By learning straightforward decision rules derived from previous data, a Decision Tree is used to build a training model that may be used to predict the class or value of the target variable (training data).

Support Vector Machine

One of the most well-liked supervised learning algorithms, Support Vector Machine, or SVM, is used to solve Classification and Regression problems. However, it is employed mainly in Machine Learning Classification issues.

The SVM algorithm's objective is to establish the best line or decision boundary that can divide n-dimensional space into classes, allowing us to quickly classify new data points in the future. A hyperplane is a name given to this optimal decision boundary.

SVM selects the extreme vectors and points that aid in creating the hyperplane. Support vectors representing these extreme instances form the basis for the SVM method.

A Step-By-Step View of the Process

Step 1: Loading data to databricks

In the initial stage, CSV data collected are loaded to the churn model. Any type of data sets can be employed here depending on the situation.

Step 2: Transformation: converting to the requisite format

Step 3: Feature selection

Step 4: Splitting the data

Step 5: Standardisation

Step 6: Model selection

During model selection, datasets are provided to the machine algorithms like Random Forest, KNN, Decision tree and Support vector machine. Each algorithm produces its own sets of accuracy values; from that, the most accurate predictions are selected. Using the same procedure, we can categorize the employees into groups, for example, those who are planning to resign and those who are not.

Step 7: Result generation