Data Aggregation
Millions of rows are collected and stored in a database by a business every single day. Handling data has become a tough task. Data analysts have come up with many strategies for data management and optimize the process of analysis. Data aggregation is one such process. Let us learn more about how data aggregation helps people to understand the data better and easier.
What is Data Aggregation?
A database or data warehouse is a cluster of data with hidden useful information. Data Aggregation can transform that information as contextual insights. Data aggregation is nothing but summarizing or pivoting of data from a data warehouse to optimize the process of data analysis, and to gain relevant insights faster.
Why is Data Aggregation important?
The database or data warehouse as a whole might not be useful to gain insights quickly. Here, data aggregation becomes key. One row of crunched or aggregated data represents numerous rows of atomic data. So, data aggregation becomes important in the following cases.
To optimize data preparation:
Data cleansing and modeling can be done easier when we have aggregated data.
To perform arithmetic functions:
Using aggregate functions, we can perform complex arithmetic operations to get valuable insights.
To create pivot views:
Pivot views to access the relevant and most used data faster. It can be created with the help of data aggregator tools.
To automate the operations:
Data aggregation tools automate the heavy manual data analysis and save time and effort.
How does data aggregation work?
The entire pipeline of Data Aggregation can be classified into three buckets:
- Collection
- Processing
- Presentation
Collection
The collection plays a vital role in the entire process. A business could use multiple business apps, one or more databases, and also has lots of files and feeds. Using a data aggregator tool, data from multiple sources will be brought into a single workspace or a data warehouse.
Processing
A Data Aggregator helps to process the data to gain insights. Advanced aggregator tools can apply machine learning and artificial intelligence to make predictive analyses.
Presentation
Millions of atomic rows of data typically get summarized into a much smaller dataset. It makes the data more consumable and reduces the friction in data analysis. Aggregated data can be presented in the following ways:
- Presentation decks
- Charts
- Pivot tables
- Dashboards
The Process Of Data Aggregation
The data aggregation functions are some predominant ways to aggregate data and make it easier to access. You can create pivot tables and visualizations using a data aggregator tool.
Data aggregation primarily has 5 functions:
- Sum: Sum of selected data for aggregation (e.g., Total Revenue = Sum of Revenue column rows)
- Avg: Average of selected data for aggregation (e.g., Average Sales = Average of Sales column rows)
- Count: Total number of rows selected for aggregation (e.g., Total Customers = Distinct count of email addresses)
- Min: Minimum value in the selected data for aggregation (e.g., Lowest rating received = Minimum value in the Rating column)
- Max: Maximum value in the selected data for aggregation (e..g., Highest rating received = Maximum value in the Rating column)
Benefits of Data Aggregation
Easy Augmentation
Having a rich data set will be easier to apply machine learning and artificial intelligence models.
Quick access to data
Summarized pivot tables help to access the data quicker as it has only the data needed and is easy to consume. It helps the customer-facing teams to resolve the query faster.
Identifying trends and patterns
Aggregated data can be used to identify the trends and patterns that are hidden and time-consuming when it's done manually.
Contextual Insights
Teams across the company can gain contextual insights with data aggregation rather than consuming all the data which is mostly irrelevant to them and hard to understand.
Examples of Data Aggregation
Data aggregation is practiced across industries and departments. Though different industries have different use cases, data aggregation comes as a common solution to all problems. The following are the departments and industries where we can see heavy usage of data aggregation.
Sales
Sales teams need to have access to the data for almost every work they do.
- Convince their prospects with product performance data
- Measure their effort in converting a lead to a closed deal.
- Enhance their conversion by having sales pipeline data in hand.
Marketing
A campaign's success relies heavily on data. Marketing teams run multiple campaigns simultaneously. Data aggregation comes as a savior here to by helping marketing teams to
- Aggregate campaign-wise data into different tables.
- Compare which campaign is performing better and which is not.
- Track crucial KPI metrics such as CTR, CPC, CPA, etc.
- Find new market opportunities to generate leads.
Finance
The Finance industry includes the most predominant metrics for a business such as revenue, expenses, tax, etc. Aggregating data helps to manage finance in many ways like,
- Measuring revenue MoM & YoY to identify the pattern
- Identifying where they spend more and the equivalent ROI
- Maintain clear balance sheets and increase cash flow.
Retail
Operations of the retail industry and data go hand in hand. Stock management and inventory management benefited from data aggregation as follows,
- Understanding consumer demands by analyzing purchasing behavior
- Day-wise footfall trend in a retail brick-and-mortar shop
- Manage inventory stocks count to avoid hassle at last minute
Choosing the right aggregation tool
We have seen how data aggregation reduces the complexity of data analysis and enhances productivity. Manual aggregation makes no sense as it consumes a substantial amount of time. So, a data aggregation tool is the solution. While choosing a data aggregation tool, you should consider the following points,
Databases and Business apps integrations
You should check whether the data aggregator tool supports the popular databases or business apps that are widely used across industries.
Data Blending
This is an important feature that every aggregator tool should have. Aggregation is used the most when combining data from multiple tables/databases. You have to check if the tool supports look-up and data modeling.
Cloud Analytics
Most of our data is in the cloud nowadays and the internet is available almost everywhere. So you have to consider a cloud-based tool that can be accessed from anywhere you want.
Visual Analytics
Data becomes more meaningful and appealing when transformed into charts and reports. Having a data aggregator tool with a variety of data visualization options.
Augmented Analytics
Forecasting and predictive analytics powered by AI and machine learning will make you prepared for the future with advanced insights.
Zoho Analytics, a self-service BI platform, has been providing powerful data aggregation functions. Explore data aggregation free with Zoho Analytics
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