Every business collects data. The difference between a business that reaps the benefits of their data and one that doesn’t is how they use their data and transform it to be analysed. Data aggregation tools expedite and empower the process of data analytics and business intelligence.
We’re going to share some of the data aggregation tools and data aggregation examples so you’ll be able to maximise the use of your own business’ data.
2. Why is Data Aggregation Important?
4. What is the Process of Data Aggregation?
5. What is Manual vs Automated Data Aggregation?
6. When to Use Data Aggregation?
7. What are Data Aggregation Examples?
8. What are the Best Data Aggregation Tools?
9. How to Choose Data Aggregation Tools?
Data aggregation is the process of gathering raw data and preparing it to be summarised using statistical analysis. The data could come from a single source, but more often than not, data is pulled from various sources and aggregated to create a cohesive picture and summary.
Data aggregation is a critical function for data analysis and business intelligence (BI). Without data aggregation, you wouldn’t be able to have analysis or BI.
Every business has much to gain from performing data aggregation. Data aggregation tools and solutions make it possible to:
Additionally, when working across multiple systems or running multiple campaigns, you’ll want to be able to gauge information holistically. Data aggregation makes this possible.
Anyone with a business goal should and can use data aggregation. Data aggregation is the key process for data analytics.
With data analytics, you can answer any type of business question. For example, marketing departments would want to know how much time visitors are spending on the company’s website and what pages/products are of most interest.
Financial and investment companies use data aggregation tools to understand how the market and politics may impact financial decisions. They’ll also use data aggregation to prepare financial reports and statements.
Medical companies can use data aggregation to find the optimal treatment plan based on large datasets of similar cases.
As you can see from these abbreviated data aggregation examples, businesses across industries and of any size have an interest in data aggregation because it supports analysis.
Data aggregation consists of a three-step process:
Back in the past, data aggregation was stored in databases that were row-and-column relational databases. Nowadays, since data can come from so many sources, including social media, browsing history, Internet of Things (IoT) devices, and more, advents like noSQL databases make it possible to leverage data from more sources.
This brings us to our next point, which is how data aggregation tools can manage every aspect of data aggregation (and for some tools like SolveXia), there are even more capabilities included.
While some companies perform manual data aggregation in Excel, the entire process can be streamlined using automated data aggregation tools.
Manual data aggregation requires people to collect and aggregate data when they choose to. With data aggregation tools, this process can be scheduled and recurrent without the need for human intervention.
The main risk of manual data aggregation, besides the opportunity cost and time-consuming nature, is suffering from human error and manual mistakes. Naturally, data aggregation tools prevent such issues and make the entire process easier on the business.
Data aggregation is used for statistical analysis and to generate a summary/overview of data to be understood. Since data aggregation summarises information from various sources, it can be used to answer any business question, see how the company is performing, and drill deeply to provide specific information about customers, products, and campaigns.
Besides answering specific questions, data aggregation can also be used to provide data anonymisation. For example, if you wish to review overall average salaries by department or the average age of a client by product, data aggregation can make this feasible to do.
Before we get into the top data aggregation tools, let’s explore a few more data aggregation examples.
Companies can aggregate data to learn more about their customers to be able to optimise their marketing and sales campaigns. When companies pull data from different sources, they can create a single personal identification number (PIN) per customer to be able to market to them in a personalised manner.
To illustrate, a customer in a certain age group with a set list of preferences can receive a different digital experience than a fellow older customer with different preferences and purchasing history.
When it comes to financial teams, aggregate data helps to compare previous accounting periods with current periods and the future to know how the business is performing and to be able to forecast with accuracy.
With various data aggregation methods to choose from and data aggregation companies, it pays to know the best data aggregation tools available so you can choose the right one for your business’ needs.
Here’s a review of five of our favourite data aggregation tools to help you make your decision.
As a cloud-based platform that provides financial automation, SolveXia inherently serves as a data aggregation tool that saves your business time, prevents errors, and improves data accuracy.
The system’s easy-to-use web-based interface makes it possible to automate business processes and access data analytics. You can leverage SolveXia to automate regulatory reporting, account reconciliation, expense and rebate management, analytics, and more.
Rather than having to deal with various spreadsheets and key person dependencies, SolveXia increases internal control, transparency, and collaboration within any business while also providing efficiency and maximum productivity.
A Windows application solution, Alteryx automates natural language processing (NLP) for text mining and analytics. The solution aims to accelerate digital transformation by providing end-to-end automation for machine learning and data science processes.
IBM Cloud Pak for Data is a visualisation tool that provides data integration capabilities. It’s typically used by large enterprises as an AI platform to help businesses collect, organise, and analyse data.
Stata is statistical software that executes data science for your business. It includes automated reporting, a suite of statistical tools, and advanced programming with automation abilities. Its powerful suite can handle advanced statistical methods; however, it requires the user to input the right prompt in the codes to receive accurate results.
Zoho Analytics is a CRM analytics software that offers customisable dashboards and reports for data-driven decision-making. It is heavily geared towards sales and marketing teams to make use of sales data and track KPIs.
Beyond exploring the capabilities of data aggregation tools, there is a list of criteria that’s worthwhile to review before making your final decision. Some of the considerations to keep in mind include:
Data aggregation companies and data aggregation tools exist to empower your business to do more with its data. With access to a data aggregation tool, you’ll be able to leverage data from disparate sources in a centralised solution, ensure it is clean and ready for use, and transform it into actionable insights for optimal business outcomes.
To learn more about how a tool like SolveXia can automate your business processes and gain more from your data analytics, feel free to request a demo.
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