Are Dashboards dead?… #Analytics


Let’s think through:

Are Dashboards Dead?

With lot of vendors pushing for democratizing analytics (a.k.a self-service), it may seem that the dashboards would soon be dead!

You need two things to make a org data driven. 1) Push 2) Pull.


…is where most of the analytics vendors are focused right more — it’s set of technologies that the business users want. The big idea here is to enable business users to pull whatever data they want, whenever they want without having to wait for Analytics/IT. Note that the business users are doing the heavy-lifting in analysis (of course you need a data platform to enable this but still it’s the business users using the platform and doing their analysis)


…is where there are dashboards which are built by central IT/Analytics and are ready to be consumed by the business users. This should be a governed environment where a lot of effort has been invested by Analytics/IT to make that the metrics are standardized & accurate. This is key to making this work — if the metrics on the dashboard are accurate and metrics are standardized then business users would trust these dashboards more than the self-service dashboards. This would also be their one-place to go view all key performance indicators for their org/department and then if they see something “interesting” (or better yet — get an alert!) then they can dive into the self-service environment and do their thing. You see, “Push” strategy is really great at getting the data to all business users and then “pushing” them to do use the self-service analytics platform.

[BTW: Putting bunch of reports in a grid layout is not what I am talking about here. I am limiting my definition of dashboard which have KPI’s and directs users to where they should be focusing on]

(Again, two things to do here to make sure the push strategy succeeds. 1) Having standardized & accurate data = earn trust! 2) Having KPI’s that align with the strategic plan of the org/dept)

Dashboards Push Pull Analytics Strategy

So now having understood what these strategies are let’s take a minute to put them to use to answer the question:

Are Dashboards Dead Yet?

So let’s imagine a scenario where a org does not a Push Strategy. They have implemented a self-service platform and are focused on evolving that. Now there are two problems that they will run into:

  1. For “casual” users — How do they get them the training they need? OR support that they need?
  2. For “power” users — Once they start creating their own calculated metrics then how do they make sure that they are standardized across what other power users are doing? (also, how do they validate if what they are analyzing is accurate?)

You see both of those problems can be partially (if not completely) solved by having Dashboards:

  1. Dashboards are a great way for casual users to look at their KPI and then they can figure out where they would focus on
  2. Also, Dashboards are a great way to provide standardized & accurate metrics so everyone could trust the number that they are looking at
  3. Note that it shouldn’t require you to start from zero! You should be using the data modeling layer built for your self service platform for the dashboards as well

And that’s why I think Dashboards are not dead yet.

PS: You might see some vendors that are pushing for a different approach where the platform would auto-magically go through the data and get you the “insights” — I think it’s a great approach. Usually they would target dashboards but I would argue that they compete more with “Pull” strategies rather than “Push” because now the business user won’t have to explore so many different variables but the platform could do that heavy-lifting and get them quick insights.

Qlik sense: How to see Data Load Editor scripts for apps developed by your Team members?


(This post first appeared on the Qlik Community. here)


So you just joined a Business Intelligence Team and one of the responsibilities include building apps for your business users. Eventually, you would have a need to see Data Load editor scripts for apps developed by other members in the team. So what permission do you need to be able to do that?

Credits: darkhorse

Qliksense Version: Enterprise Server 2.0

Source: can’t see a peer’s data load editor scripts


This a two-step process.

1) Get “content admin” access (or “higher” level access)

2) Double check if you have access to see data load scripts for ALL apps

Step 1:

The short answer is that you need “Content Admin” permission from your Qlik sense admin…But with this access level, you will have access to other developer’s app via QMC. If you need to do this via HUB as well then you will have to change the content admin role.

Here’s how Serhan ( darkhorse ) explained how to get this done:

QMC–> Security Rules–>Content Admin–> Edit–> Context–> Both in Hub and QMC

Qlik sense management console

Step 2:

Now, once you get the “content” admin access, you might want to double two things:

1) You can get access to data load scripts on published apps — (I was able to do this but there still seems to some open questions around some folks not being able to see the data load scripts for published apps. If this is the case for you, you need to duplicate the app on your “my work” area and see the scripts)

2) You can duplicate apps on your “my Work” area and see scripts — this is also useful if you want to make changes to published apps that are out there.


I hope this helps you resolve the permission issues and help you collaborate with your team members!

How to add Sparkline data visualization to Google spread sheets?


I like using spark lines data viz when it makes sense! It’s a great way to visualize trends in the data without taking too much space. Now, I knew how to add sparklines in Excel but recently, I wanted to use that on Google sheet and I had to figure it out so here are my notes:

1. Google has an inbuilt function called “SPARKLINE” to do this.

2. Sample usage: =SPARKLINE(B2:G2) — by default you can put line chart in your cells.

3. Then there are other options including changing the chart type. You can find them documented here:

4. One of the best practices that I advocate when you spark-line to “compare” trends is to make sure that you have the consistent axis definition. So the sample usage for that could like this:


(if you want to do this for excel then here’s the post: )

After you’re done, here’s what a finished version could like on Google sheet:

Google Sheet Data visualization spark line

Here’s the working google sheet:

A key driver for business intelligence adoption: Embedded analytics.


Did you know most business intelligence (BI) solutions are under-utilized? Your BI solution might be one of them — I definitely had some BI solutions that were not as widely used as I had imagined! Don’t believe me? Take a guess at “number of active users” for your BI solution and then look up that number by using your BI server logs. Invariably, this is Shocking to most BI project leaders = Their BI solution is not as widely used as they had imagined! Ok, so what can you do? Let me share one key driver to drive business intelligence adoption: Embedded analytics.

Embedded analytics

#1: what is Embedded analytics? 

Embedded analytics is a technology practice to integrate analytics inside software applications. In the context of this post, it means integrating BI reports/dashboards in most commonly used apps inside your organization.

#2: why should you care? 

You should care because it increase your business intelligence adoption. I’ve seen x2 gains in number of active users just by embedding analytics. if you want to understand why it’s effective at driving adoption, here’s my interpretation:

Change is hard. You know that — then why do you ask your business users to “change” their workflow and come to your BI solution to access the data that they need. Let’s consider an alternative — put data left, right & center of their workflow!

Example: You are working with a team that spends most of their time on a CRM system then consider putting your reports & dashboards inside the CRM system and not asking them to do this:

Open a new tab > Enter your BI tool URL > Enter User Name > Enter Password > Oops wrong password > Enter password again > Ok, I am in > Search for the Report > Oops, not this one! > Ok go back and search again > Open report > loading…1….2….3…. > Ok, here’s the report!  

You see, that’s painful! Here’s an alternative user experience with embedded analytics:

They are in their favorite CRM system! And see a nice little report embedded inside their system and they can click on that report to open that report for deeper analysis in your BI solution.

How easy* was that?

*Some quick notes from the field:

1) it’s easy for users but It’s not easy to implement! But well — there’s ROI if you invest your resources in setting up embedded analytics correctly!

2) Don’t forget context! example: if a user is in their CRM system and is looking at one of their problem customers — then wouldn’t it be great if your reports would display key data points filtered for that customer! So context. Very important!

3) Start small. Implement embedded analytics for one subject area (e.g. customer analysis) for one business team inside one app! Learn from that. Adjust according to your specific needs & company culture AND if that works — then do a broad roll out!

Now, think of all the places you can embed analytics in your organization. Give your users an easy way to get access to the reports. Don’t build it and wait for them to come to you — go embed your analytics anywhere and everywhere it makes sense!

#3: Stepping back

Other than Embedded analytics — you need to take a look at providing user support and training as well…And continue monitoring usage! (if you’re trying to spread data driven culture via your BI solution then you should “eat at your own restaurant” and base your adoption efforts on your usage numbers and not guesses!)


In this post, I shared why embedded analytics can be a key drive for driving business intelligence adoption.

How to assign same axis values to a group of spark-lines in Excel?


Spark-line is a very handy data visualization technique! It’s great when you are space constrained to show trends among multiple data points.

Here’s an example:

Spark Line Trend Excel Data Visualization

But there’s an issue with above chart! Axis values for these group of spark-lines do not seem match – it could throw someone off if they didn’t pay close attention. So a good practice – when you know users are going to compare segments based on the spark-lines – is to assign them same axis values so it’s easier to compare. Here’s the modified version:

Excel Sparkline data visualization same axis

And…here are the steps:

1. Make sure that spark-lines are grouped.

Select the spark-lines > go to toolbar > Sparkline Tools > Design > Group

Excel Sparkline Group

2. On the “group” section, you’ll also find the “Axis” option – select that and make sure that “same for all axis” is selected for Vertical axis minimum and maximum values:

Excel Spark Line Data Viz same min max value


That’s about it. Just a quick formatting option that makes your spark-lines much more effective!

Author: Paras Doshi

Dashboard – Asset management & planning for a global crisis response team:



Asset (Volunteers, Field offices & Equipments) management & planning for a global crisis response team.


Working in a team, we created statistical surveys for field works to collect data about current state & estimated future needs. We also helped them with data gathering & cleaning tasks. After that, we helped them analyze & visualize the data to find actions for executives leading the global crisis response team.

Here’s a mockup of one of the ten data visualization created for them:

Asset Management Global crisis response

Business Intelligence system – Customer Complaints – B2B company:

Customer complaint dashboard quality feedback

Analyzing customer complaints in crucial for customer service & sales teams. It helps them increase customer loyalty and fix quality issues. To that end, here’s a mockup:

Note: Drill down reports are not shown, details are hidden to maintain confidentiality and numbers are made up.

Customer complaint dashboard quality feedback