How to Produce Bigger and Better Results With Small Data Tools
The evolution of data mining, analysis, and management has been nothing short of amazing. Once upon a time, companies of all sizes relied heavily on Microsoft Excel to store, sort, and handle information on a day to day basis. While platforms like Hadoop are lightyears more advanced in the way of technical specifications, small data tools can still be highly effective when adopting a big data mentality.
Approach Old School Apps With New School Smarts
Today’s big data applications are impressive to say the least. They store and process near infinites amounts of data. They merge diverse data sources happily together under a single roof. They turn raw mumbo jumbo into meaningful intelligence you can actually comprehend through visualization. That’s all great, but it doesn’t necessarily mean you have to kick your once reliable data management tools to the curb, especially when they’re utilized to their full potential. Here are some examples:
Look for Big Data features
Software vendors are steadily giving their traditional applications big data features. For example, new functions like Power Map, Power Pivot, and Power Query make it possible to use data modeling, advanced analytics, and visualization in Excel spreadsheets. These features and others enable small data applications to become huge additions to your big data toolkit.
Use official help sources
Even the simplest of data management programs are backed by some pretty solid help resources. Microsoft, for instance, offers vast online documentation that helps users with everything from getting started with Excel to mastering advanced tricks. Some of the best resources allow you to get hands on with sample spreadsheets and put live data sets into play.
Ask for community help.
When you can’t find the answers you need from official help sources, the community is your next best option. No matter what program you’re using, it’s bound to have an active community bustling with users who’ve experienced the same issues you’re facing. The search engine is your friend when it comes to finding blogs, forums, social networks, and other helpful community resources.
Keep It Simple and Essential
You know how they say not all money is good money? Well in the digital universe, it’s more like “not all data is good data”. Rather than giving every single report and every bit of data the same high-level priority, identify what truly matters and set your focus on that. By taking this targeted approach, you’ll not only improve the quality of your big data project, you’ll reduce the workload your analytics and IT teams have to burden themselves with.
Feed Your Team Better Data
Few things are more frustrating than cleaning up errors and omissions in spreadsheets. Companies can help their analysts minimize stress and agony by feeding them data that is in the best possible shape. From proper file labeling to accuracy assurance, extensive quality checks must be performed before deploying your data in any environment, but especially one with limited analytical capabilities. Adopting a new level of attentiveness won’t necessarily eliminate errors, but it can help greatly reduce them while empowering your team to work more efficiently.
Know Your Limitations
Ultimately, the key to getting the most from your traditional data management applications is accepting what they can, and can not do. Strides have been made, but even with all its improvements, a well rounded application like Excel will only do so much for your big data initiatives. The sooner you realize you’ve hit your ceiling, the sooner you can work on upgrading to a solution that offers the enhanced functionality you require. This is when you want to look for big data tools that allow you to easily import spreadsheets, databases, and other existing data sources for a seamless transition.
by Big Data Companies