If data analytics were easy, everyone would do it, right? Well, what if were easy enough for anyone to do it? Can you image what sort insights you might glean from the vast pools of data your company collects about your manufacturing processes, sales or production outputs?
If all of your data stays hidden in the depths of some process control computer or in Excel spreadsheets on the manufacturing floor manager’s desk, are they doing anyone any good?
An easy-to-use and simple data analytics tool can help a floor manager or production supervisor gain valuable insights about the performance of a manufacturing process.
Tackling this issue – making data analytics easy – is not impossible. You just need a tool that helps you get the right information out of your data even if you are not a data scientist. What are the key elements of a data analytics tool meant for people who are not data analysts?
Here are 10 characteristics you should look for in an easy-to-use data analytics tool.1. Simple user interface. The tool should be set up to help you focus on solving a specific task. Whether you need to look for deviations from a normal process condition or determine if your last production batch is up to par, your tool should make it easy to focus on the task at hand with just a few simple clicks.
A simple user interface can help you focus on your goal.
2. Drag-and-drop functionality. The most obvious characteristic of an easy-to-use data analytics tool is that it should be easy to use. What exactly does that mean? Well to start, it shouldn’t require you do to any coding and might very well use drag-and-drop functionality for data access or import. It should keep alternative settings or confusing options to a minimum.
3. Guided workflow. A simple tool should provide some guidance for managing your data analysis. It may start with a few simple options up front based on what your goal is and guide you through the steps needed to get the result you want. For example, a guided workflow might take you step by step through the stages from Data Management to Data Analysis to Data Insights with a few simple clicks.
4. Essential charts. The best tools come pre-built with a standard set of basic statistical graphics (such as line, bar, scatter, histogram, bubble, boxplot), etc., but may go farther and offer complementary plots enabling more advanced aspects of data visualization.
Being able to quickly and easily visualize your data is essential.
5. Customization. The visuals should be easy to customize by changing labels, colors, and other elements. You should be able to move or resize your labels and adjust the color scheme based on categories or other values.
6. Visual pivoting. When you’re exploring data sets that contain many rows and variables, being able to visualize data in histograms or change easily between views can be important.
7. Filtering options. You should be able to filter the data you want to include easily in the analysis tool. For example, you may want to exclude all process data below a certain quality value or between certain dates, and see immediately how it affects your visualization.
8. Sharable reports. Quickly saving your views and creating reports that can be easily shared with colleagues or other team members is an important function. If you can save the reports in a number of different formats such as PDF, Word, Excel or PowerPoint with a single click and email it directly or regularly, even better.
9. Reliable Insights. Not only must the tool organize and visualize your data, it should help you conduct relevant analysis to glean important insights. That means being able to drill down into important points for more details, and having the key variables that affect a plot presented up front. And so much the better if the tool offers you valuable insights by laying out the analysis in the most logical formats.
The ability to toggle various plot properties can provide important insights.
10. Automation. Above all, you should be able to automate the process. Drag and drop functionality, scripting and simple setup for automatic repeating of functions and delivery of reports. If you have other analytics systems in place, it should be able to integrate with those.
An easy-to-use data analytics solution gives people who are not data analytics experts or data scientists, such as production managers or manufacturing process supervisors, the tools needed to evaluate and monitor process results, uncover deviations and better manage ongoing production processes.
What are your thoughts?
What do you think are the most important elements of an easy data analytics solution? Tell is in the comments below.
Watch a demo
Seeing the features described above in action may help you get a better understanding of their real significance. If you’d like to see a demonstration of the Umetrics Easy Analytics solution, watch this webinar.