Technology tools for data analysis software#

These types of programs encompass everything from chart building tools and interactive visualizations to plug-and-play dashboards. They shine when you’re trying to tell a story with data. Visualization and dashboarding tools provide a graphical layer on top of your data.

For day-to-day work, getting to a decision is more important than flexing your analytical muscle. Otherwise, it’s all too easy to cherry pick results that support your preferred outcome.įor strategic decisions, be rigorous in your analytics. Even if you’re a statistical wizard, you still need to be careful to always create your hypothesis prior to conducting a test. Incorrectly run statistical tests will lead you astray, with potentially disastrous consequences for your business. Where it falls short: If you’re a novice, stick to something more familiar.

Technology tools for data analysis software#

Where statistical software shines: If you want to answer complex questions like “What characteristics predict purchases?” and “What behaviors predict churn?”, you need to use statistical software. A spreadsheet will tell you your sales for last quarter a statistical tool will help you determine the effects of a branding campaign on those sales. But if you need to do something more analytically rigorous you’ll need specialized software. Most business reports rely on sums, averages, and counts. As the complexity of a query increases, SQL becomes harder to write, debug, and maintain. Where it falls short: If you want to know how your ongoing sales are affected by your shipping prices, you’ll have to spend a lot of time writing a complex SQL query and then maintaining it as your data structure changes. For quick and dirty answers, and one-time analysis, there’s truly nothing better. Where SQL shines: Need to know how many orders you had yesterday? Write a quick SQL query and keep on moving.

SQL excels at one-time analysis, and its English-like syntax makes it more accessible to non-technical people. Structured Query Language (SQL) is the nearly-universal language used to interact with databases and query large data sets. Did you know it’s estimated that 88% of spreadsheets contain errors? Spreadsheets don’t automatically update, so looking at anything ongoing or complex is time-consuming and error-prone. Where they fall short: If your goal is to track ongoing sales data or marketing ROI, you’re in for some major headaches. Where spreadsheets shine: If you want to brainstorm, do some one-time analysis, or put together some quick financial models, spreadsheets give you the flexibility you need. The flip-side is that because of their popularity, they’re often misused for things they’re not good at. They’re a bit like the pocket knife of data analysis. They’re fast, easy, and amazing for certain tasks. Spreadsheets are hands-down the best-known data tool. In this post, we’ll lay out the different types of tools that you have at your disposal, and break down where they shine, and where they fall short. Choosing the wrong data analysis software ensures that your analytics will be arduous, time-consuming, and deliver sub-par results. You wouldn’t use a pocketknife to cut down a tree, or a sledgehammer to pound in a nail, yet many decision-makers regularly use the wrong tool to tackle their data jobs.

This is a guest contribution from Janessa Lantz, the Director of Content Marketing at RJMetrics, a cloud-based analytics platform.