Embracing the Spreadsheet: Two Approaches to Self-Service Business Intelligence

Business users love Excel. Well, love is probably too strong a word. Let’s say business users are comfortable with Excel for performing daily reporting and analytic tasks. But the ubiquitous Microsoft spreadsheet application has some serious drawbacks.

Namely, business users often end up dragging data into isolated Excel spreadsheets for analysis, resulting in small, disjointed islands of data popping up on desktops across the enterprise. Excel also has data size restrictions, not a good attribute in the Era of Big Data.

The good news for the enterprise and end-users alike is that they now have a number of options to improve the ability to share and scale spreadsheet analysis, two of which I highlight below.

Microsoft SharePoint + PowerPivot

First there’s Microsoft itself. The company addresses Excel’s shortcomings with a combination of SharePoint and PowerPivot.

SharePoint is a well-known entity by now, so I won’t bother going into the details. Needless to say, it gives users the ability to share documents and content, including Excel spreadsheets and underlying data models. It also includes enterprise-level features such as role-based security, workflows, and versioning.

PowerPivot is an in-memory Excel add-on released in 2010 that significantly expands the amount of data that can be manipulated in the spreadsheet app. Users can easily integrate large data sets from multiple sources (including non-Microsoft databases and cloud-based data feeds) on their own, then slice-and-dice the data with the Excel functionality they are already familiar with including through the Office Fluent user interface.

PowerPivot users that want to take their analysis a step further can tap Data Analysis Expressions, or DAX, to actually build analytic applications. DAX leverages some Excel functionality, but does require users to understand somewhat more complex data modeling and analytic techniques.

1010data’s Trillion-Row Spreadsheet

Another option is 1010data. The private New York-based firm has developed what it calls the Trillion-Row Spreadsheet. It’s a cloud-based application that allows business users and analysts to manipulate data in the familiar spreadsheet format, but at Big Data scale. Users upload their data to 1010data’s cloud and can also add both open and proprietary third-party data feeds to enrich their own data.

And unlike a traditional spreadsheet, the Trillion-Row Spreadsheet includes database-like functionality so applications and transaction systems can access the resulting analysis, meaning critical insights aren’t left isolated on users’ desktops.

1010data’s cloud-based model also makes it easy for enterprises to share data and analysis with partners and customers. 1010data customer Dollar General, for example, recently built a portal to allow its suppliers, which include Coca-Cola, Purina and Nestle, to access the 1010data platform to perform their own analysis on PoS data.

Embrace the Spreadsheet?

Another important feature of both these approaches is that they include IT management and oversight capabilities that you don’t get with isolated spreadsheet use. They provide IT a view into who is accessing data and sharing analysis, and allow IT to provision hardware and resources appropriately.

I would qualify both approaches as self-service BI, a concept we’ve been hearing about from BI vendors for some time now. The idea behind self-service BI is to provide business analysts and regular end-user with easy-to-use, powerful-enough tools and applications to perform data analysis on their own without having to ask IT to spin-up local data marts, build new integration capabilities or – most importantly — train end-users.

Of course there are other approaches to delivering self-service capabilities, including the data visualization-focused approaches taken by Tableau Software and QlikTech, and the spreadsheet model doesn’t accommodate unstructured data analysis. But leveraging end-users’ fondness/reliance on spreadsheets is, I think, a smart play for enterprises with large volumes of relational data and large numbers of Excel users.

About Jeffrey Kelly

As Wikibon’s lead Big Data analyst, Jeff Kelly applies a critical eye to trends and developments in the Big Data and business analytics markets, with a strong focus on helping practitioners deliver business value. Jeff’s research includes market analysis, emerging technologies, enterprise Big Data case studies, and more. He also appears frequently on theCUBE to share his insights. Prior to joining Wikibon, Jeff spent seven years as a writer and editor at TechTarget, where covered a number of business and IT topics including IT services, mobile computing, data management and business intelligence. He holds a BA from Providence College and an MA from Northeastern University.