You can get nowhere without data, but raw data will not get you far in the modern world. You see, data is a raw material that needs to be processed. It needs to be analyzed so that its results can become actionable information. This is a skill that every business owner and executive must acquire. So, to help you, here are the top six tips for more efficient business data analysis.

1. Define your objectives in advance

Before you analyze anything, you need to determine what you’re looking for. We’re not saying that you need to know what the outcome will be, only what you’re looking for.

At the same time, the statistics may sometimes reveal something you had no idea would happen. This can help you improve your business and give you a significant competitive edge.

Finally, you must keep in mind that you can also approach this matter from a business standpoint. This means visualizing the outcome before anything else. What do you want to achieve with this data? Increase site visits? Recover abandoned carts?

2. Pick the right data tools

You’re not going to analyze this data manually, which is why your next objective needs to be finding the right tools for the job. 

First, you need the right tools for the job. Ideally, you would start with document management software and then pick something potent from a wide range of powerful AI tools. 

Document comparison platforms are useful in more than just analysis. They can also be used in version control (in a scenario where more than one party works on this data simultaneously). In the legal industry, having the best attorney document comparison can save you from trouble.

3. Collect the data

There are several ways to collect data: by asking your customers directly through surveys, polls, and reviews or by collecting it passively through your site (cookies).

Not all data is useful, so you need to make sure that you set meaningful metrics (observe things that are relevant to your brand). To do this, you need to get familiar with vanity metrics that sound important but don’t contribute much to your brand.

Also, remember that you obtain the majority of data straight from your site and your user’s interactions with it, meaning your site needs to be up to date. It’s even more important that you remain compliant with any valid data privacy laws in your region.

4. Engage in data preprocessing

This data needs to be prepared for analysis, but this is not always an easy thing to do. The thing is that nowadays, this is often done through an AI tool, but just because it’s handled via a tool and not manually, this doesn’t mean that the process is any different.

During the data preprocessing stage, the party preparing the data removes outliers, handles missing values, and, if necessary, transforms data from unstructured to structured. NLP (natural language processing) can provide incredible help with the last one, which has sped up the entire process and increased accuracy. 

5. Be careful about how you use them

Sometimes, you can have a purpose for this data in advance. We’re primarily talking about the concept of hypothesis testing. This is a scenario where you already have a potential result in mind, and you’re just trying to get the data to validate or disprove your assumptions.

Then, you may also be engaging in predictive modeling. In this scenario, you’re doing your best to forecast future outcomes by analyzing past data. The longer in the past, you can go (and the larger your data sample), the more accurate you’ll get. Not to mention that AI predictive tools are also quite helpful, and without their processing power, you wouldn’t get nearly as accurate results.

This is the key step since, on its own, data doesn’t mean much.

6. Descriptive statistics and data visualization of results

What do you do with the results? Remember, you’re not just trying to get the statistics but to obtain them as a resource that you can use to improve your work. This means that the results need to be available to the entirety of your team and in a form/format they can easily understand.

The first way to get there is to start using descriptive statistics. For instance, instead of saying 15% of the customers do X, you can say one in six customers. Sure, it’s not exactly 15%, but it’s close enough, and rounding up numbers is completely acceptable when descriptive statistics are in question.

Another, even better, solution is to use visualization of results. A graph or a chart is easier to understand even for someone who’s not that deeply familiar with the topic.

Wrap up

The reason why you need to learn this is because it’s a skill/task that you’ll need in continuity. It’s not just one a one-and-done process but a recurring one that you’ll practice for as long as you’re in the business. With better tools and practices, the results will be more impressive.