5 Simple Steps To Make Data For Analytics Improve

Introduction

It’s not uncommon for organizations to make analytics data worse. Whether it’s a result of poor record-keeping, an overabundance of data or bad processes, it happens all too often: companies gather information, then lose it. In this post, we’ll outline five steps you can take to make sure your data is better when it’s time to analyze it.

1 – Clean and Organize Your Data

Data is the foundation of analytics. It’s what you use to make decisions and take actions, so it’s important that your data is clean and organized.

Cleaning your data means removing any inconsistencies or errors that could cause problems later on down the line. For example, if someone is listed as “Jane Doe” but their first name actually goes by “Jenny,” then this would be considered an error in your dataset because they aren’t matching up with their legal name–you’ll need to correct this before using any other information about them in your analysis projects.

Organizing also helps ensure accuracy in analysis results: if all of your personal information is stored in separate spreadsheets or files (or worse yet, handwritten notes!), then it’ll be hard for anyone else who doesn’t know where everything goes when working on an analysis project with someone else!

2 – Get Rid of Duplicate Records

Next, you want to remove duplicate records. This is important because it will make your data cleaner and more accurate. There are a few ways you can identify duplicate records in Excel, but the easiest method is by using the VLOOKUP function.

To do this:

• Open up your spreadsheet with all of your data in it (the one from Step #1)
• Select cell A1 on the first worksheet (it will be blank). This will be where we place our formula so that we can find duplicates across all of our worksheets at once.

3 – Identify Which Variables to Analyze

• Identify which variables to analyze.
• Use the right tools for the job.
• Know what you are trying to achieve and use the right data to achieve your goals

4 – Pick the Right Type of Analysis for your Data

The first step to better data analytics is picking the right type of analysis for your data. There are many different types of analytics, each with their own strengths and weaknesses. Before you start analyzing anything, you need to know what kind of information can be gleaned from it.

Let’s go over some common types:

• Descriptive Analytics – These are used to describe past events or trends in your business (e.g., sales figures). They don’t predict future outcomes but can help identify patterns that may lead to better predictions later on down the road by providing more context around what happened during given time periods when something occurred (or didn’t). An example would be looking at sales figures per product category over time so that when one category drops off significantly compared with others due perhaps only partially due its popularity waning but also because there were fewer marketing efforts made towards promoting those products overall during that period where they might otherwise have performed better if given more attention up front during production planning stages instead than letting them languish as lost opportunities while other higher-margin lines continue selling well regardless because they’re easier sells since most people already know about them–and thus no longer need any convincing before buying them!

5 – Visualize to Make Sense of it All

Visualization is a method of presenting data in ways that make it easier to understand and interpret. It involves presenting the data in visual form, such as graphs, charts, diagrams and maps. Visualization helps you quickly spot patterns or trends in the data. This can help you make sense out of the information at hand so that you can spot errors or outliers that might have slipped through during collection.

Visualization can also assist with identifying relationships between variables within your dataset–something which would have been difficult or impossible without visual representations of this relationship (for example: if there was no way for us humans to see how much time each person spent online each day).

If you follow these steps, your data will be more useful for analysis.

• Data is the foundation for analytics.
• Clean and organize your data so it’s accurate, relevant, complete and timely.
• Make sure you have the right format for your data so that it can be easily accessed by users who don’t know how to code (like non-technical analysts).

Conclusion

Now that you have your data, it’s time to start analyzing it! We hope these tips have been helpful in getting started with your own analysis projects. Remember that it is important to use the right type of analysis for each situation and always remember that data visualization can help make sense out of numbers.