Collecting data quickly and efficiently is a focus for businesses regardless of their size. For small and mid-sized businesses, successfully managing data without being a burden on an understaffed and overworked IT department can be an added challenge. Plus, there is the added responsibility of making sure ACCURATE data is being analyzed to build reports that will ultimately lead to informed business decisions. Simply eyeballing the data for mistakes is difficult to do and opens the door to errors.
Clearly, the size of your business does not necessarily relate to the size of your data. Even small hospitals, like Crouse Hospital in Syracuse, NY, have massive amounts and varieties of data to compile from multiple systems and departments.
I’ve found that SMBs have an average of 7 to 10 disparate sources of data and IT groups and business users at these organizations spend a tremendous amount of time trying to manage it all.
This much data potentially holds a lot of valuable knowledge, but ascertaining value from the data is not always easy. To optimize what your data can do for you, SMBs need to address three fundamental areas: data management, business analytics, and reporting.
Most people, when they think of data quality, think of a big, complex solution that will take months and months to get up and running, cost hundreds of thousands of dollars and require a team of highly skilled programmers. This doesn’t have to be the case.
To make sure you’re truly optimizing your data, there is an end-to-end process you should go through but you can start anywhere in that process depending on your needs. At SAS, we’ve built solutions for SMBs specifically that integrate, manage, cleanse, forecast, analyze and report on data -- encompassing the whole process.
There’s no point in having software that does amazing things if you can’t use it. SMBs need more out of their data. Visualization tools, prebuilt models, and interactive, point-and-click interfaces, without the nuisance of programming, can all lead an SMB to getting the most out of their data without overloading them with processes.