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DataHero – Visualization for the Non-technical

I am consistently impressed by the number and quality of new data tools I’m seeing online. In the past, if you did not have access to an enterprise-level tool, your charting and analysis was done in Excel (and even if you did have an enterprise-level tool, your charting was probably still done in Excel).

 

The latest cool tool to come across my screen is DataHero. With the ability to connect to popular cloud-based services, accept CSV and Excel file uploads, and combine all of them together (when sensible), DataHero makes charting and analysis pretty darn easy.

 

Is this a tool for everyone in the enterprise? No, but I don’t think that’s what it is trying to be. It seems that the better fit would be a smaller department, say a marketing or sales department in a midsize business. Often, these users are relying on SaaS applications to do their day-to-day work, applications like HubSpot and MailChimp. And my experience has shown that these users tend to be on something of an island when it comes to enterprise data, since much of their activity involves working with data before it ever hits the company’s operational applications. As a result, these generally non-technical users are left to either figure out charting in Excel, or to buy cups of coffee for their more technical co-workers in exchange for a spreadsheet or two.

 

A Quick Chart

 

How easy is it to use? Given my background with data and reporting tools, I may not have the same perspective as a data novice, but it seems pretty straightforward to me. Select your SaaS data source (from among 23 choices), or upload an Excel or .csv file from your computer. Once imported, DataHero asks you to confirm the data types in an easy-to-use format (in this case I’m using one of the available sample data sets provided by data provider Qandl):

 

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When everything looks good, a single click on the ‘IT LOOKS GOOD!’ button and DataHero brings up a number of suggested charts against your data (I think this is really cool):

 

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One option is to select one of the suggested charts and make modifications to it, if needed. If you don’t like the suggested charts, or have a specific chart in mind, clicking on the ‘CREATE NEW CHART’ icon brings you to a blank screen with your data fields along the left side, ready to drag and drop onto the chart area:

 

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As you drag and drop the various fields onto the chart area, DataHero will figure out the best (or at least a good) way to display it. From the example above, I first dragged ‘Exports (metric tons)’ onto the chart area, followed by ‘Fuel Type’. The resulting chart looks clean and usable:

 

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But maybe the chart doesn’t have enough context yet. So, let’s add in the ‘Date’ (which in the source data is only the year) to make it more meaningful:

 

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Getting there! Note that the Fuel Types in the legend are sorted alphabetically (as directed by the ‘Order: A-Z’ in the ‘Colored By’ dialog box on the left side). Instead, let’s sort them by the largest values. Simply drop down the Order dialog and select ‘Order: Top by Value’ (I also added a title to the chart while I was at it):

 

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There! First chart done. Of course I could continue to tweak it by adding labels, etc., but you get the idea.

 

Other Features

 

Filtering – I didn’t show it in my demo above, but charts can be filtered by a data item whether it shows up on the chart or not.

 

Combining Datasets – A great feature of DataHero is the ability to tie two (or more) datasets together. Once your first dataset is selected, you tell DataHero to ‘Combine Your Data’. It asks which field in the dataset should be used as the link, then prompts for your second dataset. Note that your second dataset already needs to be in DataHero before you start this operation, I didn’t see any way to add it in on the fly. Once the second dataset has loaded, you tell DataHero which field to link on (and, yes, they do need to match!).

 

Dashboards– In a manner similar to Tableau, charts can be grouped into dashboards. Note that, unlike combining datasets, which is available on a limited basis with the free account, you must have a paid account in order to create dashboards.

 

Data Update – Because your underlying data changes, periodic updating is a necessity. These updates can be kicked off manually, or can be scheduled through DataHero. Note that files uploaded from your computer must be updated manually.

 

Export – Interestingly, exports of charts are available only as .png files, while dashboards can be exported as a PDF packet or a zip file of images. While this would be acceptable in many cases, it also has its limitations.

 

Free Account Limitations

 

Though a free account can provide value right out of the box, it does have limitations:

 

  • Uploaded files can only be 2MB, while a paid account allows for 10MB uploads
  • Only one dataset combination is allowed with free accounts, while there is no limit with paid accounts
  • As mentioned above, dashboards are only available with a paid account
  • Data updates are available only with paid accounts (though you can try one update with the free account)

 

Paid Plans

 

DataHero keeps it pretty simple with their plans. In addition to the free plan discussed throughout, there are two paid plans: Premium ($49/month), which gives access to one user, and Team ($69/month/user), which allows multiple users to work on the charts and dashboards, and has access to priority phone and email support.

 

Caveats

 

Now before you go out and switch all of your department’s reporting over to DataHero, remember that with any SaaS there are possible issues:

 

  • Longevity – I don’t have any clue about the company’s financial health, but there have certainly been cases over the years of apps disappearing quickly, and with little or no warning.
  • Lack of Calculations – I didn’t see any way to create calculations within the charts, so either I missed it or you need to make sure that everything is available in your source data before you start.
  • Volume – I didn’t attempt any large-volume tests with the tool so I can’t vouch for its performance when data sets get large. At the same time, DataHero is not claiming to be the right tool for large-volume environments.
  • Database Connectivity – As of right now, there is no way to hook directly to databases. Of course, adding that capability would pit DataHero against enterprise-level tools, and that doesn’t appear to be their target market.

 

As with every other piece of software ever written, DataHero is not perfect. But for what it is trying to do, it sure seems like a good candidate for many of the data-poor teams out there. Give it a try and let me know what you think!

 

My Recommendation

 

I am really impressed with DataHero as a low-impact data visualization tool.  If you’ve got needs that just don’t warrant the weight of a MicroStrategy, a Business Objects, or even a Tableau or Qlik, give it a try.  I think you’ll be pretty impressed.

 

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