Business Intelligence | Power BI | Machine Learning & AI | SaaS & Enterprise portals
Transforming businesses with modern web and data solutions through data intelligence and AI products, portals, and services
Ever imagined if you could get to see all the amazing Power BI Features in one shot? Power BI is not just one product. It’s a suite of products.
Check this image to view end to end picture of Power BI!
Thanks to Coates Data Strategies for compiling this.
Whether it’s about team performance, or pipeline performance, or competitor or product performance, it all takes a few metrics to keep track to ensure business visibility.
How quickly will I achieve my goals?
Do I need to speed up my sales process?
Is their a team which needs more attention?
Is their an account that I’m losing?
Are we improving?
Metrics should be shown on a dashboard, big and visible! It can be shown on a TV or big displays.
Metrics include: Lead by source, Pipeline, Sales cycle, Closed opportunities, New business vs. upsell, Win/loss rate, Product gaps, Open opportunities, Open activities, Open cases, Opportunities past due and Sales by closed date.
Only 12 metrics and you are done with your sales tracking!
But,
Which tool should I use?
How do I extract the data?
How do I plot these?
Which visuals should I use?
How should I show sales deal stage funnel?
You will be amazed to see how the metrics have been placed with the right choice of visuals and the layout in this Power BI report developed by us using HubSpot CRM data.
If you see the report here it is divided into several views:
The CRM source could be HubSpot, SalesForce or even Microsoft Dynamics.
The tool we have used is Microsoft Power BI to pull, model and visualize raw data.
Interested in viewing the live Power BI report?
Image source and inspiration: Sales Force
By 2025, close to 30% of the data will be real time – IDC
We have 6 years before we hit that, but I must say real time data or rather ability to report it real time is key for business success even now.
Imagine a scenario where you could view production plant quality metrics real time on a TV?
A grid operator tracks electricity flows in real time. Metrics like Power, Voltage with trends and power lines plotted on a geo map and viewed LIVE!
Sensory data, campaign performance (as they happen), field sales activity, and many more examples of real time data and real time reporting needs.
Some of the above examples are definitely real time but customers also ask for near real time – as soon as my new data is available I need to view it in my reports and dashboards within couple of mins.
There are couple of solutions to achieve this:
Next, we will cover real time and near real time reporting scenarios and discuss possible solutions along with limitations/pros & cons. Each scenario and solution is unique, and you can use any of the proposed solutions based on your needs.
Scenarios:
When your dataset refresh time gap between two refreshes can be more than 30 mins, you can go ahead with scheduled refreshes. This assumes you have imported the data in a Power BI dataset and you would want to refresh that dataset.
Based on our experiences when you present schedule dataset refresh options to customers they are not taken aback. Scheduled refreshes have limits on the number of refreshes per day but once you schedule them to occur more frequently in business hours, it helps.
With Power BI Pro Licenses you can setup 8 refreshes a day per dataset. So you can setup refresh times to be: 7AM, 8AM, 9AM, 11AM, 2PM, 3PM, 4PM, 5PM
Total 8 refreshes spread out in business hours.
With Power BI Premium/PPU Licenses you can setup 48 refreshes a day per dataset. So you could do refreshes every 30 mins! Power BI Premium is definitely costly (read USD 60K/year) but you get more refreshes, more capacity and enterprise level features (AI and more). Update: March 10, 2021. Power BI Premium per user, a new offering, is priced at $20 per user per month.
Or, you can put the workspace where your dataset resides in an Embedded Capacity (Premium/Non-Premium) and get rid of refresh limits. In this case you will have to use Power BI REST APIs to refresh the dataset. Quite simple to invoke. Read more on this capability here.
You can even use Power Automate and trigger refresh based on events.
More on Power BI Refreshes in this Microsoft post.
Assume you have a data source which can be directly queried from Power BI, you can achieve this scenario.
Sales data stored in a SQL box (Azure or on-prem), you setup direct connection to this data source in Power BI. Your Power BI Report is now directly connected to the data source.
If you are viewing this report on a browser you can hit browser refresh and it would fire query live to the source, fetch latest data and show on your visuals.
You can even automate the browser refresh using a Chrome Plugin. Read more here on how to achieve this.
List of data sources which can be directly queried from Power BI Platform here.
This assumes the data source where your quality metrics are stored is not direct-queryeable by Power BI.
In this scenario you need to push data from your data source to either a Power BI Push dataset or to a database. This push can be done every 1 min or so. This push can be done by setting up Azure Functions.
Once the quality metrics are pushed to a Push dataset or to a database, you can setup Power BI reports on top of it and view the data live.
Power BI Push datasets have some limitations as mentioned here.
How to setup Push datasets? You can read this external blog post here.
If you need something real and live, something like electricity flows or sensor data, you can setup Streaming datasets in Power BI.
Once you have streaming datasets setup, you can view them through Power BI dashboards. These datasets do not store historical data. They will store limited data in temp cache.
You can open these dashboards in a browser and put them on a TV. No need of browser refresh as the tiles would refresh “live”.
How to setup Streaming datasets? You can watch this YouTube video by Patrick (MSFT) here.
Read more here on Power BI Push and Streaming datasets.
Did we cover all the possible solutions? Ok, let’s check it once more.
Additional Scenario 1: We are a global organization and we would like to view our Google Analytics data every 1 hour. The dataset is shared with our global marketing team spread across every continent.
Solution suggested: In this case instead of going with scheduled refreshes, you can go with pushing GA data to either a Power BI Push datasets or to a database and setting up direct DB connection. Or, if you are an enterprise with Power BI Premium licenses you can set 30 mins refresh intervals.
Additional Scenario 2: I have more than 5 plants where I need to view live data. Should I setup one dashboard with filters?
Solution suggested: You can view data from streaming datasets only in a dashboard. Dashboards do not support filters yet. In your case you can setup 5 dashboards and share these URLs.
Any other solutions you want us to include?
Let us know.
Thanks
Ranbeer Makin
Key driver/influencer analysis using the newly released Power BI “Key influencers” visual.
Key driver analysis or key influencer analysis is critical to understand what factors impact an outcome and/or what is the relative importance of a factor. Example:
What influences employee attrition? Overtime? Job Level?
What influences employee attrition in the Sales Executive role? Distance from home?
What influences customer attrition? High call rate? International Voice plan?
Knowing answers to above helps in decision making.
If employees in Job Role “Healthcare Representatives” leave the most because of the distance from home, maybe offer them fuel reimbursement or maybe offer them accommodation expenses if they stay near to office?
The newly released Power BI “Key influencers” visual (released as part of Feb 2019 Power BI Desktop release) aids such analysis very very quickly with no code! Crazy!
We applied this new visual to analyze what drives employee attrition, and I must say, I’m blown away by the outcome, ease of use, and comprehensiveness of the visual.
Download Power BI report and play with the visual.
But, how does the result look like?
From the visuals above we can clearly see what influences our variable Attrition=Yes. OverTime, MaritalStatus, YearsAtCompany, JobSatisfaction, and so on.
Not only that, the visual also provides the values of the factors which influences our variable of interest the most.
The likelihood of attrition increases by 2.93x if employees are doing overtime. Or, Attrition is 2.93x more likely in the employees who are doing overtime.
Hmm… if you do overtime, you may quit. This is obvious.
The attrition is 2.18x more likely if employees are single!
Attrition is also high if the Department is Sales.
And so on.
The left-hand side view of Key influencers shows all the factors influencing our “Attrition=Yes” by a factor of 1.0 and above.
The right-hand side view shows the distribution of data with respect to the selected factor and Attrition either as a column chart or a scatter plot.
The dashed line shows Avg. Attrition % of all values except for the key influencer one (in this case except for OverTime = Yes)
There is another view of this visual where we can see Top segments with high attrition % and their characteristics.
The visual identified 4 segments with high attrition % along with population count. Clicking on a bubble shows us the characteristics of that segment.
Segment 1 with Attrition % as 57.6 has employees in Department Sales, DistanceFromHome > 11, JobLevel is high and OverTime is Yes.
Wow!
You can further drill down this segment by clicking on “Learn more about this segment” and see what other factors influence this segment.
Can I filter this visual?
Yes, you can. Example: Why are employees in job role “Healthcare Representative” leaving the company?
Is the visual interactive?
Yes, you can select individual influencing factors and see the distribution of Attrition % by the selected factor.
Can I hover over the values in the scatter plot above?
Yes, you can!
Can I see the logic or p-values associated with factors or key influencers?
No, not yet. This visual is in preview mode. Power BI team may add this feature in the future. Not sure about this.
Can I just see the top X key influencers?
No, not yet. This visual is in preview mode. Power BI team may add this feature in the future. Not sure about this.
I do not see my key influencer in this visual?
Yes, this can happen. Based on my R code using RandomForest, Age should also be an influencing factor for attrition but doesn’t show up in Power BI visual.
See this scatter plot. If Age decreases, Attrition % increases. Maybe Power BI just checks how the “increase” direction of a factor increases Attrition % or Maybe the number of data points for lower age and high attrition is less.
Can I export the data for segments?
No, not yet. This visual is in preview mode. Power BI team may add this feature in the future. Not sure about this.
Does this visual analyze multiple factors and provide conclusions?
I do not think so. In the example below, the likelihood of Attrition % increases by 11.58x if monthly income goes up. But why is that so? Could it be because for those employees the YearsAtCompany is also more?
Maybe Power BI visual needs to remove outliers.
Power BI visual, currently, doesn’t analyze this for us.
I want to set this up for my data?
Ok, here are steps to achieve this.
Step 1: Download and Install Power BI Desktop Feb 2019 from here.
Step 2: Enable this visual from “Preview features”.
Step 3: Restart Power BI Desktop. Click on the visual highlighted to put it on the canvas.
Step 4: In the visual data options, drag the field to analyze in “Analyze”, and possible influencers in “Explain by”.
Note: The visual is evaluated on the table level of the field being analyzed. In this case, we are analyzing Attrition, and hence the visual runs at an Employee level. So you may not need aggregations on “Explain by” field. Otherwise, appropriate aggregations are required.
Step 5: In the visual select “Yes” in Attrition value. In your case, select the value you want to analyze.
Step 6: Share analysis with your boss/team/company, and say Thank you to us 🙂
No code, drag and drop solution to key influencers analysis in Power BI!
Simple, huh?
Thanks
Ranbeer
PS: This visual is currently not supported in Power BI Embedded, Publish to Web and Power BI Mobile scenarios.
Download Power BI report and play with the visual.
I’m glad to inform my readers that Microsoft is adding new AI capabilities inside Power BI. And, these are no-code solutions.
Let’s check these 4 new exciting capabilities in detail and in the order of quick wins as per me.
In this post, I will explain uses cases with examples from multiple industries for each of the new capabilities coming in Power BI. This will be followed by a general approach to solve such problems, and then the new AI + Power BI approach to solve such problems.
Capability 1: Key Driver Analysis – or Key Influencer Analysis
Note: Per Microsoft this would be available to all Power BI users.
Suppose you have a dataset of employee attrition which includes details of the employees who are in the company, who left the company along with age, gender, salary, job role, satisfaction, education, years with current manager etc.
Your task is to find factors influencing attrition. Why are employees leaving the company? What segments of employees are leaving?
A general approach for answering such questions would be to use R or Python, fit a model (say using Random Forest algorithm) or use techniques like RFE (Recursive Feature Elimination) to find out top factors affecting our label – Attrition. More details on this general approach and how we did this using R and Power BI is mentioned in detail in our case study here.
With new AI capabilities in Power BI, this would be just a click away. The outcome of the analysis from Power BI would be shown as a kind of “lollipop” chart as shown below.
Example: When Parental encouragement is true, the probability of a student to plan to attend college increases by 1.8x,
Or, when the employee has spent more than 2 yrs with current manager and his job satisfaction is low then attrition increases by 2.3%
From the screenshot it is not clear how multiple driver analysis can be performed: Ex: When parental encouragement is true and Gender is male – what happens then?
A contingency matrix would have helped in this case.
Capability 2: Azure Cognitive Capabilities – Sentiment Analysis, Image tagging, object detection in Power BI
Note: Per Microsoft this would be a Power BI Premium capability
You started a campaign on Twitter and would like to analyze your users sentiments – positive/negative.
For a call center company you would like to analyze chat script and identify key items customers are talking about right within your BI reports.
Or, an E-Commerce company would like to detect objects in the images attached with customer reviews, and identify which product/brand is causing negative sentiments or causing pulling “Andon Cord”.
A general approach would be to use Azure Cognitive APIs inside your Power BI report using Power Query (more about this later) using calls such as: Web.Contents(AzureAPICallWithParams).
Another general approach would be to develop and use custom Deep Learning models. A Twitter sentiment analysis (racial vs non-racial tweet) model was developed by us and is hosted in our GitHub repo.
With new AI capabilities in Power BI, this could be just a matter of invoking a function from Power BI ribbon. We do not know yet how this will be invoked by users. But, definitely this will make our BI reports comprehensive and improve decision making.
A snippet of such comprehensive report is attached below.
When this comes out in preview we will have to see if Microsoft has provided ability to not fire API calls for items already tagged/analyzed – otherwise you will have to pay for every API calls (even for repeats).
Capability 3: Automated Machine Learning models
Note: Per Microsoft this would be a Power BI Premium capability
Imagine in your Power BI report along side Sales Oppty data I provide you a confidence score or probability score against each Oppty data. The Oppty owner can look at this number and decide which Oppty are more likely to be won so he/she can then focus his/her efforts on top highly likely Oppty.
A general approach to add this would be a data scientist developing such models and a developer integrating it inside the Power BI report, and a business analyst consuming the report.
With new AI capability, Microsoft is targeting business analyst so they can build, train, and apply the models right within Power BI service without writing a single line of code. Isn’t that waow?
From the initial screenshots by Microsoft, it looks like this will be part of DataFlows (another new capability, which I will talk about in later posts)
When this feature is out in preview, we will have to see how easy will it be to do feature engineering – feature selection, normalization, pruning, binning etc. But, this is for sure going to ease out the effort in long term.
Capability 4: Use your existing Azure ML Models in Power BI
Note: Per Microsoft this would be available to all Power BI users.
This capability is more of easing out collaboration between a data scientist and a business analyst.
Typically a data scientist builds models in Azure ML platform and publishes the model as API endpoint. A data analyst or engineer uses that model endpoint to predict outcomes and populate the data inside the BI report. This BI report is then consumed by a business analyst.
In the new AI approach the models developed by data scientist would easily be searching in Power BI, and a new interface would be provided in Power BI to hook to that model and use it in reports.
There are no screenshots for this capability by Microsoft.
—–
The public preview of these capabilities will be launched towards the end of Nov 2018.
We would evaluate these capabilities and posts about it when they arrive.
What thoughts you have on these capabilities? How are you going to use these capabilities?
Let us know.
Thank you,
Ranbeer Makin
References: