Tips + Tricks

The Biggest Myths of Drag and Drop Business Intelligence Tools

By Logi Analytics
Share on LinkedIn Tweet about this on Twitter Share on Facebook

The claim to fame for most drag and drop business intelligence (BI) tools has been providing business users with the ability to create dashboards and reports without any help from IT. But in practice, can they really deliver this without significant IT cycles? Does this approach really provide a single version of the truth to everyone within an enterprise (beyond just analysts and power users)? What about entities outside the bounds of the VPN?

Myth 1: No IT involvement at all 

Some discovery and dashboard tools provide insights at the individual or desktop level without any IT involvement. However by excluding IT, these tools don’t necessarily scale well or integrate easily with multiple security frameworks across an extended enterprise made of customers, vendors, consultants and internal departments.

Dashboards with identical Key Performance Indicators (KPIs) created by two different departments can have very different results depending on how the original data was sliced and diced. For instance, if you’re working with a dataset in flat files, such as Excel, that are local to your department, you will generate dashboards that may not provide a complete picture or contribute to the company’s evolving understanding. This is because you’re not working from a centralized data store, and there is no governance over business calculations. As a result, the integrity of your dashboards is at risk and of less value to the broader company. Moreover, your dashboard may be taken at face value, distorting the analysis done by other departments or misinterpreted by executives.

Therefore, your company will need to create a centralized data source within these tools down the road and your IT department either has to rush to learn the backend architecture of the tool and prepare data for use, or hire a channel partner to get the data structured and usable within these tools. In essence, your company is replicating the data tier that it has already spent a lot of resources building.  Not only is IT forced to re-create business calculations within these tools, they introduce the risk of losing the original business calculations.

These tools may claim that their products are so simple that you won’t need IT, but as soon as your needs grow just a little bit more sophisticated, you’re involving IT expertise one way or another.

Myth 2: Scalability of the architecture 

Many drag and drop embedded BI tools claim to provide end users with complete flexibility to carry out their analysis. However, these tools come with a fixed architecture (e.g. ETL, backend repository, design interface, web technologies, etc.), which doesn’t allow them to scale as business needs change or increase over time.

A common use case is the addition of new data and data sources. As your enterprise looks to scale its operations and the data size changes from gigabytes to terabytes or more, these tools may suffer in performance because their back-end repositories may not be able to handle big data sets. What if an enterprise wants to use a more current visualization library, such as D3 instead of HTML5? Due to the pre-configured architecture of these tools, your enterprise is either stuck with what it has or has to invest in new BI tool that can not only satisfy its current needs, but can also scale as the business needs evolve.

The Logi approach 

Logi Analytics provides a more agile architecture where enterprises can bring in the data and technologies of their choosing and replace them as business needs evolve. An example of this agile architecture would be an enterprise that leverages Microsoft SQL Server as its data tier (data warehouse, data marts, etc), Clover for ETL and Logi Analytics for visualizing the data. Using this agile architecture, Logi provides the means to carry out self-service analysis without replicating the data tier within the tool.

The agile architecture not only allows the enterprise to continue using the tools of its choice, but also allows the enterprise to scale or replace one of these tools without replacing the entire BI stack. For instance, an enterprise that has acquired another business unit may need to scale the performance of its data tier. With Logi, it’s possible for the enterprise to move from SQL Server to a columnar data tier such as Vertica without issue.

While data preparation is still required prior to self-service analysis, Logi’s agile architecture saves enterprise IT significant time and resources by not having to replicate the data tier within a BI tool and scale its operations. And our products’ intuitive design makes it easier for end users of all stripes to build and interpret reports, without sacrificing IT’s expertise.

Want to experience Logi Analytics for yourself? Download a free trial here.


Originally published October 29, 2014; updated on August 10th, 2017

About the Author

Logi Analytics is the leader in embedded analytics. We help team put business intelligence at the core of their organizations and products.