While some third-party solutions for Clio offer additional reports, they don’t let you customize the visualizations to your firm’s specific needs. Instead, we have found that firms employ various workarounds to create custom visualizations for Clio.

Starting today, our Clio and Power BI integration will help firms of all sizes get a better, customized view into their firm’s key metrics.

The Clio and Power BI Challenge

Many firms who use create custom visualizations in Clio export the data as CSVs and manipulate it in Excel or upload it to Power BI. This forces firms to rerun exports and refreshes constantly. The task is tedious, whereas Power BI is a seamless solution for developing interactive and impactful dashboards.

Bridge the Gap Between Clio and Microsoft Power BI 

To address the Power BI and Clio gap, Virtuas developed the Power BI connector for Clio. Our connector allows Power BI and Clio to work together seamlessly.

Data Refresh Automation

The main benefit of the connector is that it connects directly to Clio APIs, cutting down on the inefficiency of CSV exports. You can schedule the data refresh to suit your firm’s needs, whether it’s daily, weekly, or monthly. Once you set the connector to refresh automatically, your data will stay up-to-date, without manual intervention.

Accessible Everywhere

You can access Power BI on many platforms. Whether you’re mobile or in front of your computer, you will have access to all of your dashboards and reports. You can also customize and share your visualizations with clients and colleagues.

Improve Your Workflow

Say goodbye to CSV exports! You can store your data securely in Clio and then easily bring your data into Power BI. There is no need for third party apps, and with the data refresh automation, you don’t have to spend time downloading and uploading files anymore. Reach out to Virtuas to carry out the Clio and Power BI integration so you can focus on your core services.

Looking for More?

We want to make the Power BI connector for Clio work for your needs. If you need help getting started or assistance with building your visualizations, contact us. Likewise, we would love to hear your feedback so we can continue to make improvements to the integration.

In my previous article I discussed some of the drawbacks of Microsoft’s RPA solution. The biggest one being the inability to interact with Windows file system. Microsoft just took a big stride in remedying this with the acquisition of Softomotive, makers of WinAutomation. WinAutomation is a leading desktop RPA software and has been around for 15 years. It is now fully capable of interacting with Power Automate.

Not only does this bring more functionality to Microsoft RPA offerings, but it still sticks to their low-code approach. This means that people who are well versed in coding, and people with no coding skills can work on automating their own processes. It also shows how Microsoft is moving ahead and expanding their RPA toolset, so other drawbacks like Web UI Flow’s lack of right clicking could be solved soon. This acquisition also helped build up their connector library that was already at 315 connectors, and now has added SAP, legacy terminal screens, Java, and Citrix.

How does this affect me?

These updates also led me to a different conclusion than my previously posted article. With this new desktop interacting ability, Microsoft closed the gap with UiPath: if you’re looking at starting a new automation project, Microsoft is your best bet. It provides the speed of low code and what it lacks in custom coding it makes up for with its integration with Power Automate Flow. This means utilizing the vast library of connectors Microsoft offers can help speed up the time it takes to fully automate a process.

Update: Microsoft recently acquired Softomotive so this article is no longer accurate. Learn more here.

Robotic process automation is a hot topic today in the IT world. Two of the biggest players currently are UiPath and Microsoft. Both of these RPA providers have reached a point where repetitive tasks in an organization can be completed without any human interaction. This means setting up and maintaining RPA robots is knowledge IT professionals need to understand.  

UiPath

Both RPA processes are advancing but each has different strengths. UiPath provides a more complex system for coding and has more actions to choose from. This can be overwhelming and difficult for new users to adapt to. The navigation also proves difficult at times when you have a larger project. At the top level is one big container for the process, then multiple smaller containers. This can make navigating to a specific process difficult and cumbersome to move back and forth between two spots. It does offer screen recording, but it often was inaccurate and selecting the actions lead to a faster set up. It had the option to use OCR Text reading, but that was unreliable. While it would work in development, it would cause random errors in production where you would have to reset its target in order to get UiPath to run again. 

Here is an example of custom coding options in UiPath, this provides the ability to set up a HTTP Request as a single action in UiPath.

This is a high level screen shot of the actions provided in UiPath. Each section has multiple sub sections which then provides each set of actions.

Power Automate

Microsofts option while lacking in the custom coding aspect, had a more reliable recording feature and made testing different pathing options easier to get a more consistent RPA process. Recording a process and having it replay through Web UI Flow was fast, took minimal editing, and was done in a fraction of the time it took with UiPath. It also provides integration with Microsoft Power Automate so it is easy to incorporate those actions and triggers in the RPA. This means that if you use O365 products such as SharePoint, you can create a process to download a pdf, upload it to SharePoint, and send an email to yourself confirming it was uploaded. This allows UI Flow to not only do RPA processes, but easily manipulate data and send out confirmations so you know the process completed. One drawback I found when recording basic tasks, is it required a few delays to help webpages to load before proceeding with certain actions. Currently the biggest drawback from Desktop UI Flow is it cannot interact with multiple monitors, double click, or interact with windows File Explorer, Task bar, and startup. The Web UI Flow drawbacks are that it cannot right click, and its wont store session information like cookies, you must script sign in processes.  

Conclusion 

If you need to only automate one process, then Microsoft is the way to go. It provides an easier set up, and you can integrate it easily with Power Automate. If you are deploying to a large enterprise with multiple RPA processes, then UiPath will be the better route. You will have more interaction with File Explorer, and deeper custom coding integrations. It is an exciting time for RPA and the advancements are only pushing its capabilities further. 

In this article I will discuss a client that needs a CSV to list all the billing data activities from an API. The data can be easily pulled and put into a CSV by Logic Apps, but the API resource returns 79,871 rows of data and the API call returns it 200 rows at a time. Then the API provides the next page of data with 200 more rows. That would mean dealing with 400 separate CSVs in order to have all the data present.

Problem

The challenge was having Logic Apps create one CSV that required no user editing and had all the data from the API in it. This meant the first API data pull had to have its headers set, but the subsequent API data pulls could not have headers. Solving this problem would save time and allow all data to be pulled from an API.

Solution

Use a for each loop in Logic Apps to append the csv data in one file until all the data is copied over. The goal is to create a variable that has all the API calls data without headers in it and create a OneDrive file with that data to our specified folder so it can be renamed. This is achieved by using Split and Trim in Logic apps. 

Note: This assumes you have an Azure subscription active, a OneDrive account, and knowledge of the API that will be used.

Create the initial API call

Step 1 

Create the trigger for your Logic App. I chose a recurrence trigger because this CSV will be updated weekly.

Step 2

Create the GET HTTP Request

Step 3

Parse the HTTP Requests
In this step I create a Parse JSON command. Its content should be the body from the HTTP Requests. This is where we get the next page URI, and the data to input into the CSV.

{
  "type": "object",
  "properties": {
    "data": {
      "type": "array",
      "items": {
        "type": "object",
        "properties": {
          "activity_description": {},
          "date": {
            "type": "string"
          },
          "matter": {},
          "quantity_in_hours": {
            "type": "number"
          },
          "type": {
            "type": "string"
          },
          "user": {
            "type": "object",
            "properties": {
              "name": {
                "type": "string"
              }
            }
          }
        },
        "required": [
          "quantity_in_hours",
          "date",
          "type",
          "activity_description",
          "matter",
          "user"
        ]
      }
    }
  }
}

Step 4

Create variable for the loop to continue.

 "meta": {
    "paging": {
      "next": "https://app.clio.com/api/v4/activities.json?fields=quantity_in_hours%2Cdate%2Ctype%2Creference%2Cactivity_description%7Bname%7D%2Cmatter%7Bdisplay_number%7D%2Cuser%7Bname%7D&limit=200 "
    },
    "records": 79871
  }

I used this parsed JSON data to create my Next variable. This will be used in the loop to let it know when there are no more pages of records.

Note: The API I am using has a next page token, if that was not available, I would create a count variable and increment it inside my loop.

Step 5

Create a CSV table from data operations and use the data from parsed JSON to create the CSV table.

Create two more variables and name one of them CSV and the other CSVloop. Set CSV to the data from the CSV table that was created.

Creating the Until Loop

1. Create until loop and HTTP loop requests.
2. Repeat step 3 from the initial API call to parse the HTTP request.
3. Create a new CSV table
4. Set CSVloop variable to CSV table
5. Compose the CSVloop with the split function
6. Create a for each loop
7. Append CSV with CSVloop
8. Set the next variable to the JSON parsed next
9. Set folder path to where you want it
10. Choose a name for the file and set .csv at the end and the file content is the CSV variable.