Most recognition programmes in large enterprises operate as standalone initiatives without integration with payroll and performance data. Because of this separation, recognition has no structural link to compensation, promotion, or appraisal. for hr software for enterprise, check empcloud.com brings recognition into the same platform framework as performance and payroll, allowing HR teams to administer structured recognition programmes that connect to the data these other modules already hold, rather than running in isolation from them.

Recognition data linkage

Recognition that operates outside performance frameworks produces activity without strategic weight. Badges awarded through a separate tool carry no connection to appraisal ratings, competency assessments, or KRA achievement data that the performance module holds against each employee. When recognition sits within the same platform, nomination triggers can draw from performance data directly. Nominated employees are presented with a profile that includes their current KRA scores, recent appraisal results, and competency ratings, ensuring that recognition is based on documented performance evidence rather than simply recollections. Peer recognition activity is logged against the employee record and incorporated into the performance module, giving appraisers a broader view of the employee than direct manager observations can provide.

Are monetary awards payroll integrated?

Spot awards, performance bonuses, or long-service payments that include monetary components create payroll processing requirements that disconnected recognition tools cannot meet without manual data transfer.

• Award payroll feeding – Approved monetary recognition awards pass directly into the payroll module for processing in the relevant pay cycle, removing the manual step of transferring award amounts from a separate recognition system into payroll each period.
• Tax treatment application – Monetary awards attract TDS implications that the payroll module applies automatically based on the award amount and the employee’s existing tax computation for the year, keeping statutory treatment correct without separate manual calculation.
• Long-service award processing – Tenure-based awards trigger from the HR module when employees reach defined service milestones, with payment amounts calculated per grade-specific policy and processed through payroll without HR teams tracking eligibility manually.
• Audit trail maintenance – Every monetary award record is maintained with the approval chain, nomination basis, and payroll period in which it was processed, keeping recognition expenditure documented for finance and audit purposes without separate record keeping outside the platform.

Non-monetary recognition structures

Not all recognition carries a monetary component, and non-monetary programmes require their own administrative structure within the HR platform to hold value beyond informal acknowledgement. Certificate generation, digital badge assignment, and public recognition posts within the employee portal each attach to the employee record when issued, building a recognition history that spans the employee’s tenure rather than existing only in a separate communication tool. Recognition leaderboards pull from verified award data within the platform, showing active programme participation across departments without HR teams manually compiling recognition activity from email announcements or external tools. Within HR analytics, manager recognition frequency data provides HR leadership with visibility into which departments run active recognition cultures and where engagement levels are low enough to warrant intervention.

Recognising programs that are managed within a core HR platform have a greater operational and strategic impact than those that are managed within standalone tools. Providing enterprise HR teams with a programme structure that contributes to retention and engagement outcomes is more effective than running as an administrative exercise separate from the data that drives workforce decision-making, as it connects recognition to performance evidence, processes monetary awards through payroll, and maintains a documented recognition history within employee records.

A Stock Screener is a powerful tool that helps investors filter stocks based on specific criteria like market cap, price-to-earnings ratio, dividend yield, and more. Whether you’re monitoring blue-chip stocks or tracking the IRFC share price, having a customizable screener in Google Sheets can give you a personalized and flexible way to manage your watchlist.

In this blog, we’ll show you how to build your own stock screener in Google Sheets, step-by-step—without needing advanced coding skills.

Step 1: Open Google Sheets

Start by creating a new spreadsheet in Google Sheets. This will be the base of your screener.

Label your columns as follows:

| Ticker | Company Name | Current Price | Market Cap | PE Ratio | 52 Week High | 52 Week Low |

Step 2: Enter Stock Tickers

List down the stock symbols (tickers) of companies you want to track. For example:

• NS (for IRFC on NSE)
• NS
• NS
• NS

Make sure to use the correct ticker format, especially for Indian stocks listed on NSE or BSE.

Step 3: Use GOOGLEFINANCE Functions

Now comes the magic—using the GOOGLEFINANCE() function.

Example formulas:

• Current Price:
  =GOOGLEFINANCE(A2, “price”)
• Market Cap:
  =GOOGLEFINANCE(A2, “marketcap”)
• PE Ratio:
  =GOOGLEFINANCE(A2, “pe”)
• 52 Week High:
  =GOOGLEFINANCE(A2, “high52”)
• 52 Week Low:
  =GOOGLEFINANCE(A2, “low52”)

Drag these formulas down to apply them for all rows.

Note: The GOOGLEFINANCE function works best for stocks listed on US exchanges, but for Indian stocks like IRFC, some data points like PE or Market Cap may not populate. You may need to manually input or use third-party APIs or data sources for comprehensive data.

Step 4: Add Filters for Screening

To make your sheet act like a screener:

• Click on the header row.
• Go to Data > Create a filter.

Now you can filter based on criteria like PE ratio < 20, Market Cap > 10,000 Cr, etc.

Step 5: Conditional Formatting (Optional)

Use conditional formatting to highlight undervalued stocks or those nearing their 52-week lows.

For example, highlight PE ratios below 15 in green, or flag stocks trading within 5% of their 52-week low.

Why Use Google Sheets for Screening?

• It’s free and customizable.
• You can access it from anywhere.
• It lets you integrate data from multiple sources.
• You control the logic and formulas—tailored to your investing style.

Final Thoughts

While tools like screeners on broker platforms or financial websites are great, building your own Stock Screener in Google Sheets puts you in the driver’s seat. Whether you want to track tech stocks or monitor the IRFC share price, this DIY approach gives you flexibility, control, and insights tailored to your investment goals.

One of the common challenges data analysts face when working with large datasets in SQL is identifying and managing outliers. Outliers are data points that deviate significantly from other observations, and they can distort statistical analyses, leading to misleading conclusions. Effectively handling outliers is critical for ensuring the accuracy and reliability of data-driven insights. This article will explore statistical techniques for identifying and managing outliers in SQL while incorporating key concepts often covered in a data analyst course in Pune.

What Are Outliers and Why Are They Important?

Outliers are data points that lie significantly outside the range of the rest of the data. These points can arise due to errors in data entry or measurement variations or represent rare occurrences that could be interesting for specific analysis. In either case, their presence can impact the integrity of your analysis. For instance, an outlier can skew the mean, inflate variance, and lead to incorrect inferences. Data analysts who are well-versed in a data analyst course understand that outliers must be managed appropriately to ensure the robustness of statistical conclusions.

Identifying Outliers Using SQL

Before managing outliers, they first need to be identified. SQL provides a variety of methods for spotting anomalies in datasets. One common approach uses statistical techniques such as the interquartile range (IQR) or standard deviation. To identify outliers, SQL queries can be written to calculate these statistics and filter out the values that fall beyond acceptable thresholds.

1. Interquartile Range (IQR) Method

The IQR is a measure of statistical dispersion calculated by subtracting the 25th percentile (Q1) from the 75th percentile (Q3) of a dataset. Data points that fall below Q1 – 1.5 * IQR or above Q3 + 1.5 * IQR are often considered outliers. SQL can be used to calculate these quartiles and identify potential outliers using the following query structure:

WITH percentiles AS (

SELECT

PERCENTILE_CONT(0.25) WITHIN GROUP (ORDER BY value) AS q1,

PERCENTILE_CONT(0.75) WITHIN GROUP (ORDER BY value) AS q3

FROM your_table

)

SELECT *

FROM your_table, percentiles

WHERE value < (q1 – 1.5 * (q3 – q1)) OR value > (q3 + 1.5 * (q3 – q1));

By learning such methods in a data analyst course, analysts effectively gain hands-on skills in filtering outliers.

1. Standard Deviation Method

Another common technique to detect outliers is the standard deviation method. Data points with more than 2 or 3 standard deviations from the mean are typically considered outliers. This method assumes that the data follows a normal distribution, and large deviations indicate that a data point is likely an anomaly. In SQL, the standard deviation can be calculated and used to filter outliers with the following query:

WITH stats AS (

SELECT

AVG(value) AS mean,

STDDEV(value) AS stddev

FROM your_table

)

SELECT *

FROM your_table, stats

WHERE value > (mean + 3 * stddev) OR value < (mean – 3 * stddev);

A solid foundation in a data analytics course would help a professional master such techniques, as understanding statistical methods is crucial for accurate data analysis.

Managing Outliers: Handling Techniques

Once outliers are identified, the next step is determining how to manage them. Depending on the context and the nature of the data, there are several ways to handle outliers in SQL.

1. Removing Outliers

One straightforward approach is to remove outliers entirely from the dataset. While this method effectively ensures that statistical analyses are not skewed, it may not always be appropriate if the outliers contain valuable information. Data analysts learn in a data analyst course in Pune to weigh the pros and cons of removing outliers in specific scenarios. The SQL code for removing outliers using the IQR method might look like this:

WITH percentiles AS (

SELECT

PERCENTILE_CONT(0.25) WITHIN GROUP (ORDER BY value) AS q1,

PERCENTILE_CONT(0.75) WITHIN GROUP (ORDER BY value) AS q3

FROM your_table

)

DELETE FROM your_table

WHERE value < (SELECT q1 – 1.5 * (q3 – q1) FROM percentiles)

OR value > (SELECT q3 + 1.5 * (q3 – q1) FROM percentiles);

Analysts can ensure that extreme values do not influence their models and analyses by removing outliers. However, this method requires a deep understanding of the data, which is emphasised in a data analyst course in Pune.

1. Capping Outliers (Winsorizing)

In cases where retaining the outliers is essential for some analytical purpose, capping the extreme values can be an effective strategy. Capping involves replacing extreme outliers with a predefined threshold value, such as the dataset’s maximum or minimum non-outlier value. This approach prevents outliers from distorting statistical measures while preserving their presence.

WITH stats AS (

SELECT

MIN(value) AS min_val,

MAX(value) AS max_val

FROM your_table

)

UPDATE your_table

SET value = (SELECT min_val FROM stats)

WHERE value < (SELECT min_val FROM stats);

Transformation Techniques: Log or Square Root Transformation

Another approach for handling outliers is through data transformation. If outliers are due to skewed data, applying mathematical transformations like a log or square root transformation can help bring the data back into a normal distribution. SQL can be used to perform these transformations:

UPDATE your_table

SET value = LOG(value + 1);

Such transformations are part of any data analyst’s toolkit and are often covered comprehensively in a data analyst course in Pune.

The Role of Data Visualisation

Data visualisation is an essential tool for detecting outliers. While SQL queries can identify outliers programmatically, charts like boxplots, histograms, or scatter plots can visually represent anomalies. Many SQL databases can integrate with data visualisation tools such as Tableau or Power BI, which makes it easier for analysts to spot trends and outliers at a glance.

Conclusion

Effectively handling outliers is crucial for maintaining the integrity of any data analysis. Using SQL for statistical techniques like the IQR method or standard deviation helps analysts identify anomalies efficiently. Additionally, managing outliers through removal, capping, or transformation ensures that extreme values do not distort analyses and models. Data analysts who complete a data analysis course in Pune are equipped with the necessary skills to deal with outliers, ensuring that their data analyses are both accurate and insightful. As the demand for skilled analysts grows, mastering the art of handling outliers will remain vital to any data analyst’s skill set.

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