How QQube is keeping up with Power BI | CLEARIFY

QQube Keeps Pace with Power BI

Since Microsoft first released Power BI to the general Public in July of 2015, they have been on an incredible fast track to pursue domination in the visualization space, and continue to add literally dozens of new features with every monthly release.

They have taken Power Query, Power Pivot, Power View and welded them into one tool. With the July, 2020 release, they have even pulled over several dozen Excel functions.

Compared to other tools like Tableau, Power BI has elevated the skill of data modeling to another level, by giving you tools that normally required specific SQL and Data Warehousing Skills. 

To the average user, the end result is compelling, as is the fact that it comes with Microsoft Office, and at a low price point. But the reality is, that most of us are not data scientists, and fewer of us have the time to master the skills necessary to reverse engineer raw data, and craft data models that produce a variety of visualizations, tiles, and informative metrics.

Nevertheless, Power BI has grown to be almost 40% of the specific reasons that people download and implement QQube to view their QuickBooks data.

Where do QQube and Power BI Converge?

Our strategy has always been to make it easier to get information to run your business.  This means eliminating the need to spend days/weeks/months reverse engineering the QuickBooks raw data structures, and making available the end result in your tool of choice.

So we assimilated long standing data warehousing principles into our QQube product and eliminated about 80% of the time it takes to deliver an analytic from scratch.  Everything is automatically put together, ready for the end user to drag and drop in their tool of choice - yet still allowing for developers to get lost in the weeds, if they so desire.

Then along comes Power BI, with tools and features that combine a breadth of visualization tools like Tableau with SQL Server data modeling abilities.

Does this mean that Power BI data modeling can duplicate what QQube creates?  No, not in a million years.

First there is no direct access to the QuickBooks database, and you need some type of repository for transaction changes - and multiple QuickBooks files.  Second you would have to reverse engineer both QuickBooks data storage, and functionality - and then deal with the many holes that exist within the Intuit SDK.

The Custom Reporting Feature in QuickBooks Enterprise is the closest thing there is to having direct access to the database, but there are holes there as well.  And of course you must be logged into QuickBooks at the time you even use Power BI - which is not the case with QQube.

But starting from scratch with just Power BI vs Power BI and QQube (or even Power BI and a raw connector) isn't even a close contest.  QQube literally saves you dozens, if not hundreds of hours of time.  And that equates to big bucks saved for the customer.

And why we continue to tighten our integration with Power BI,

QQube Was The First

QQube was the first to deliver out of the box functionality that displayed QuickBooks desktop data in a variety of Power BI models. But our first iteration only created the necessary models, with no actual metric or report examples.

Beginning with Version 6.1 in November, 2017 we took the user experience multiple steps forward, by providing examples that would not only display the strength of our offering, but produce out-of-the box information that could be immediately consumed by anybody in the organization.

Not only that, we provided several hundred DAX formulas, so that customer not familiar with the Power BI language and syntax, could get started with a minimal learning curve.

In that first integration, we provided the following ease of use features:

• Data models for each analytic, including necessary connection, tables and relationships.
• Common sense field names. (The underlying field names can be unrecognizable)
• Remove (hide) fields not normally used in visualization such as street address (zip code, city, state, country normally would be).
• Create useful hierarchies e.g. account, class, customer where applicable within an analytic.
• DAX Formulas.

QQube 7.7 Tightens the Integration

We added the following features as a result of improvements to Power BI:

• Cleaned up the data model visualizations.
• Defaulted all dates to one specific format.
• Set date dimension numerical fields to not auto summary.
• Categorize the proper fields for GIS purposes e.g. city, state/province, postal code, country.
• Set default numerical formats and math operations (e.g. sum/avg) for all analytics:
  • Quantity
  • Hours
  • Line Rate
  • Line Exchange Rate
  • Amount
  • Percent
  • Ratios
• Synonyms for natural language queries
• Adjustment of hierarchies to accommodate new Power BI Functionality
• Bubble text descriptions (Microsoft Power BI has a bug which won't recognize line feeds, so currently we did this for just the folder names)
• Reformatted all DAX formulas for easier readability.
• Added additional help resources

What's Next?

We will focus on three areas:

1. Once Microsoft fixes their bug with bubble text for field information, we will add all the descriptions.
2. Continue to add to the library - including contributions from our own QQube Certified Solution Provider community.
3. Add models with multiple analytics

Stay tuned.