Presented at #sqlpass summit 2015.
(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?
Qliksense Version: Enterprise Server 2.0
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
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
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!
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: https://support.google.com/docs/answer/3093289
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: http://parasdoshi.com/2015/03/10/how-to-assign-same-axis-values-to-a-group-of-spark-lines-in-excel/ )
After you’re done, here’s what a finished version could like on Google sheet:
Here’s the working google sheet: https://docs.google.com/a/parasdoshi.com/spreadsheets/d/1EJYDTxOifeEL-YwW1a0oxXw7tFG1iAVQlwjo4EU8R-s/edit?usp=sharing
I was at the HP Big data conference last week and I heard something during the keynote that’s worth sharing with you.
As Data & Analytics professionals, we spend a lot of our time on finding insights, trends & patterns out of the data but the keynote speaker (Ken Rudin, Facebook) encouraged everyone to take that a step further = Think about Driving impact based on the insights. It’s simple yet a powerful idea! Over past few months, I have started working closely with decision makers and helping drive impact vs just “handing-off” insights.
Don't strive for actionable insights but focus on taking it to next level: drive impact – Ken Rudin #HPBigData2015
— Paras Doshi (@paras_doshi) August 12, 2015
I hope that helps! Just wanted to share that with you. What do you think?
It’s been amazing to see the growth of Business Analytics community over the past couple of years as one of the chapter leaders on the PASS Business Analytics Virtual chapter…Here’s a data viz that I put together to analyze effectiveness of our marketing campaigns:
Here’s the chart:
May’14 = 100 attendees. Jun’15 = 223 attendees. % Diff = 123%
With this growth rate, we should have ~500 attendees in our future virtual chapter meeting in Jun 2016. Can’t wait! :)
A lot of work by Dan English (current president) and Melissa Demcsak (Immediate past president) went into growing this chapter!
You’re using Google’s Universal Analytics — That’s great! They key to make sure that you get the most out of it is to make sure that you incentivize your users to log-in aka authenticate. First step in doing that is to figure out percentage of users that are authenticated…Here’s how you can see that report:
1. Login to Google Analytics
2. Select your view > Go to “Reporting” section
3. Navigate to Audience > Behavior > User-ID coverage
4. On this report, you can see authenticated vs unauthenticated sessions:
In this post, we talked about how to run a report that shows you percentage of authenticated users. (In google’s Universal 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.
#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.
I’m at PASS BA conference this week to learn about current & future state of business analytics world. I would categorize today’s keynote as a session that gave me an insight what’s our “future”.
Before I begin describing my takeaway’s, I would provide some context by providing keynote speaker’s background.
The keynote speaker was Carlo Ratti who directs MIT’s seanseable city — a group that focuses on how to use data to understand our cities and works on research projects to make it better!
So, the theme of the keynote was to show some of the research projects and inspire audience on how big data was used to come up with actionable insights to make our cities better. The projects are usually at the intersection of Internet of things and Big Data.
With that context, here’s are my notes:
– What is Big Data?
(I thought that was funny but behind it, there’s an interesting takeaway: we need NEW tools and approach to deal with Big Data! btw, MIT has their in-house tools to generate data viz’s on bigdata — one such example: Data Collider http://datacollider.io/)
– Internet of things & Big data
Carlo also showed the relation between Internet of things and Big data by using data to show exponential growth that we have seen in mobile connections and how it’s poised to grow further in coming decade because of internet of things.
– Big Data = opportunistic + user-generated + purposely sensed.
Carlo then categorized his projects in three categories: Opportunistic, User-generated & Purposely sensed.
couple of examples shared by the speaker:
1. Re-drawing Great Britain’s map from a network of human interactions:
2. A better way to handle traffic at traffic lights. (Project Wave) http://senseable.mit.edu/wave/
1. using user-generated content to figure out where’s the best party in Barcelona? (project World’s eye)
2. analyzing user tweets during an event. (Project: Tweetbursts)
– purposely sensed
1. Having sensors in the bike that gives you info like pollution, traffic congestion and road congestions in real-time.
(Project: Copenhagen wheel.)
2. Following e-waste around the world. Sometimes the energy put into discarding the waste is more than we can ever get out of it after its recycled. This should be improved! (Project: Trash Track)
That’s it for the notes.
You can check out all of their projects on their site!
After seeing the demo’s, I can’t wait to live in one of this “smarter” city. What a fascinating application of data! — The future is awesome!
you want to create a funnel chart of how your new users move from their landing page to your desired destination. Ideally it’s goes something like this:
Stage 1) lands on your home/landing page
Stage 2) goes to a product page
stage 3) goes to a checkout page
stage 4) sees a thank you page
Now, if you want to analyze the conversion among these stages for a “new” user then you will need create custom reports in google analytics. You will basically need to create a report for each specific page that you want to analyze. So how to set one up?
1) Navigate to Google analytics profile
2) On the top of the go to “customization” section and click on create a new custom report
3) here’s how you can set up a custom report that will use you new users by a specific page (notice the page filter?).
In this post, I outlined the steps that you need to take to setup a custom report in google analytics that shows you new users by specific pages.
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:
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:
And…here are the steps:
1. Make sure that spark-lines are grouped.
Select the spark-lines > go to toolbar > Sparkline Tools > Design > 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:
That’s about it. Just a quick formatting option that makes your spark-lines much more effective!
Author: Paras Doshi