4 Actionable Tips to Get More from Your Big Data

An actionable decision is something that works as a catalyst for driving sales. The goal of any business is to stay out of the red by having a continual turn-over of new sales, which is something that can only be done if every link in the business chain is functioning smoothly. Analyzing the big data of your business is the most precise way to make these key decisions. 

1. Use Six Sigma

Six Sigma is a series of management techniques that can improve your business and reduce the possibility of making significant errors. This old-school process has found a home in modern data analytics because it works to streamline the process of the entire business. Here are the main steps of Six Sigma:

Define the problem

Defining the problem means two things; first, that you recognize you have a problem somewhere in the business and second, that you can pin-point what that problem is. 

Measure the right data

Any time you are dealing with analytics, it's important to make sure you're measuring the right data. Some data is simply irrelevant. While it might seem useful to intake all the data even remotely associated with your business, the data you want to pay the most attention to are often the anomalies.

Find correlations and patterns

When you have the right data, it's time to look for any correlations and patterns. This can mean searching for positive and negative patterns. Don't overlook positive patterns, as that data shows you are doing something right.

Improve weak areas

Improving the weak areas of your business means taking a good assessment of your negative correlations and trying to figure out how to improve them.

Conduct A/B tests

When you make improvements, you'll want to conduct A/B tests. An A/B test is a simple experiment where you have a control (A) that you are testing against an alternate or an improvement (B). If the improvement tests better than the control, then it can be adopted into your business model.

2. Illustrate your analysis

When you generate a data analysis, it's important that you use effective visualizations to process that information. It's much easier to understand statistics and percentages when they're presented as a graph.

You can also illustrate your analysis by segmenting your audience, the channels that are working, and the weak areas of the business. The right illustration of your data can clarify statistics and make it easier to compare improvements.

3. Try integrating data sources

Comparing data might be one of the most useful things you can do to understand the context of what you've collected, so you should consider integrating other data sources into your analysis. Using multiple sources to gather data means you are getting a comprehensive understanding of your big data. 

To do this, try integrating your internal data with larger databases. Larger databases will provide you with a comparison that can help you make decisions on how you can optimize data sources and analytics. By understanding what the larger databases are observing about similar data sets, you can figure out how to improve.

4. Use real-time data analytics

One of the tough things about data analytics is that any data you're currently analyzing is already out of date, because it takes time to collate and understand its context. After all, generating reports isn't something that happens by magic. It takes time. If you're still running data analytics piece by piece, then you're probably losing valuable time for decision making.

This is where automating your analytic processes comes in. APA allows you to easily share data and automate tedious and complex processes to turn data into results. This type of platform is invaluable when it comes to delving into the data to access realtime insights and improve ROI. 

Analyzing your big data is a reliable way of staying on top of the key metrics for your business. Data analytics can inform many of your decisions, from profiling your target audience, using Six Sigma to reduce the possibility of making analytic errors, and integrating data sources with real-time data analytics.