Data analytics is the process that business analysts use to inspect data and draw insight from it. It's a long, multi-stage process, starting when the data is collected. It doesn't really end; it's an iterative process where there's always new insight to be gained.
Businesses can use data analytics for all sorts of different things. Every business operation can benefit from improved data analytics, from sales, to marketing, to hiring, to supply chain. Anywhere a business collects data, they can use data analytics to streamline processes and drive insight.
Data analysis is made up of many smaller steps, each of which has its own purpose. These steps include things like data transformation, which helps get data into a format where it can be used, and data visualization, where data is turned into graphs and charts that can easily be understood.
In the past, businesses had to employ teams of data experts and use complex, frustrating business software to generate their data analysis. However, with the recent shift towards cloud-based, self-service BI, businesses don't need to do all that.
With self-service BI, businesses can do their own analytics, with employees that might not have much data experience. These tools are quick and intuitive, and are very simple for the average person to use. With modern tools, any business can access the power of advanced analytics, even without any data analysts on payroll.
What does data analysis look like?
Data analytics is a long, multi-stage process. It begins when a business initially collects their data, and it doesn't end until the data has become outdated, stale, or irrelevant. There are many individual steps to data analytics. Data may go through all of the steps of data analytics, or it might just go through a few.
Each of the steps of data analytics are designed to make data more relevant and actionable. Data as it's collected can't easily be used for insight. The data analytics process turns data from raw unconnected information into something that shows clear trends and relationships.
The first step of data analytics is data collection. How data is collected will differ from tool to tool. Some data collection methods are passive, meaning that they collect data in the background while users do other things. For example, as a salesperson moves through the different stages of a sale, various data points about each stage will get logged in their sales tracking tool.
Other data collection methods are more active, and allow for clear user input. In these cases, users can often enter data directly into a piece of software or spreadsheet. Many businesses start out actively collecting data, then shifting to more passive data as they understand their business software better.
Regardless of how data is collected, it needs to be transformed in some way to make it easier for a business intelligence tool to analyze it. Business systems store their data in a wide range of different styles and formats. During the data transformation stage, data from different sources is changed in a way that doesn't change its content, but does change how it's represented.
For instance, one business tool might store state names as abbreviations, while another might write the full state name out. To combine data from these data sources, the data from one tool will need to be changed to the data format of the other data. Data like state names need to be expressed in a consistent way to really unlock the power of data analysis.
There are all sorts of other transformations that might need to be applied on a data set. One major strength of a BI tool is that they can transform data sets from multiple sources together, at once. If a business wants to join their Shopify data to their Salesforce data, they can do so with a BI tool.
During the transformation process, there needs to be some process that removes inaccurate, irrelevant, or duplicate data. Data analysts need to get rid of this useless data, so that it doesn't throw off the results of their later analysis. This process is called data cleansing. Many BI tools have features that automate or semi-automate this process.
Sometimes, data is cleaned before the transformation process. Other times, it's cleaned during or after it's been transformed. Regardless of when the data is cleaned, a business needs its data to be valid and accurate before they analyze it.
In a BI tool, all these steps are usually handled by the tool's ETL feature. ETL stands for 'extract, transform, load'. During the ETL process, data is extracted out of the software it was collected in, transformed into the most useful format for that data, and loaded into the BI tool's storage.
After data has been collected, transformed, and cleaned, it's ready for data analysis. In data analysis, users apply all sorts of statistical models to the data in an attempt to find trends and relationships. Some of these models are fairly simple, like finding averages or changes, while others are more complex.
Most tools have self-service analytics, meaning that it's up to the user to apply different statistical models. This allows for more customizability and flexibility, but the user has to know a bit about what they're doing and why. Other tools automate this process. This automation is often paired with techniques like machine learning or neural network computation.
Some tools have predictive features that allow users to forecast what their data will look like in the future. Most tools have fairly simple predictive capabilities like best-fit and regression lines, while others use machine learning to calculate more accurate forecasts.
After users have decided on the analysis that they want to perform, it's time to visualize the data. It's important to visualize the data effectively, so that viewers can easily understand what the implications of the analysis are.
For example, if a user wants to show how a metric has changed over time, they might graph that metric as a line chart. A line chart is a simple, intuitive way to show someone how a metric is trending. If a user wanted to compare revenue across different regions, a bar chart would be a good choice, since they excel at showing how different cohorts compare to each other.
Most BI tools have dozens of different ways for a user to visualize their data. Much of the challenge of building visualizations isn't actually building the visualization, but in knowing which visualization to use and why.
Some tools offer their users a wide range of different ways to customize their visualizations. Users can change many of the visualization features, like its size, color, or font style. Other tools are more limited in how users can change their visualizations.
After the visualization stage, the data set is ready to provide insight. The data analytics process helps to ensure that the data set is as accurate as possible and that it's presenting its implications in an easy-to-understand way. Now, the data set can be used as a standalone visualization or as part of a dashboard.
Conclusion
Data analytics is the reason that many businesses choose to invest in a business intelligence tool. It's a powerful tool that helps businesses to unlock the value of their data.
For more information on how data analytics can help your business, contact us today. Our team of experts can help you find the BI tool that's best for you.
