Have a Business Query? Get Answers in Seconds with Amazon QuickSight Q

Implementing machine-learning to deliver business intelligence and insights, improve efficiencies, and empower data-driven decisions, with Amazon QuickSight

Any enterprise serious about streamlining business workflows and improving efficiencies, would have made the move away from manual spreadsheets towards dynamic workspace solutions long ago. Even though basic spreadsheets still have relevance for certain tasks, there are associated risks, for example, calculation errors or version control, with  several examples hitting the headlines. Using data management solutions, for example, Smartsheet, enables an organization’s data to be managed and accessed in a single online environment. However, the key to gaining competitive advantage is the ability to leverage automation to unlock intelligent and actionable insights from the data. Analysing data and reporting results, whether presenting internally or to clients, needs to be quick and intuitive, and the fewer manual processes involved the better. And yet, even with a dynamic workspace solution it can prove a challenge.

The more comprehensive a data management tool, the more complicated and time-consuming it can be to identify the information, stored in multiple worksheets, that comprehensively answers a business query. Collating data into a visually impactful dashboard is great for reviewing everyday business queries, but even creating dashboards requires a certain level of aptitude. When it comes to ad hoc business queries, many hours may be spent reaching a conclusive answer.

And this was exactly what we were finding, here at SourceFuse. The company had been using Smartsheet for several years, but experienced several challenges when responding to ad hoc queries, including:

• Manual efforts: with no intuitive way to work on multiple sheets, end-users had to manually search for the information by creating a calculated column. Unfortunately, not all end-users possessed the skills required to write these calculations, and in this case, they would have to tediously scroll through the sheet.
• Skill dependency: in order to use Smartsheet as effectively as possible, end-users would need to rely on an expert with the right technical knowledge, in order to generate insights with complex formulas. 
• Performance: the need for creating a lot of calculations was slowing down the time for retrieving a result. In addition, time-to-result was impacted when multiple users were trying to retrieve some information at same time.
• User interface: if multiple users logged in simultaneously, it would slow down the processing of information, and cause further delays when responding to clients. The UI included filters, which helped narrow down the search, but users would still need to know in which files to locate the relevant information.