(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?
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 saw this ad on a highway earlier today and my reaction: why would I switch to a network that has just “96%” coverage.
T mobile ad — example of data puking
…instead of converting a potential buyer, this ad actually made me more nervous. You know why? Its a case of what I like to call “data puking” where you throw bunch of numbers/stats/data at someone hoping that they will take action based off of it. So what would have helped in this ad? It would have been great to see it compared against someone else. Something like: we have the largest coverage compared to xyz. My ATT connection is spotty in downtown areas so if it said something like we have 96% coverage compared to ATT’s 80% then I would have been much more likely to make the switch.
I wrote about this adding benchmark in your analysis here
Takeaway from this blog: don’t throw data points at your customers. Give them the context and guide them through the actions that you want them to take.
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.
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
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:
It shouldn’t come as a surprise that an “Analytics” virtual chapter is using data-driven marketing techniques! 😉
There are three different implementations that you could have with Google Analytics:
Classic Google Analytics
Universal analytics with Google Tag manager.
If you fall under “2. Universal analytics” then this post is for you since I’ll walk you through steps that you need to take see UserID’s on your google analytics report.
First why do you want UserID on your GA reports?
1) Data blending is powerful. If you can combine your transactional datasets with web traffic data then you can extract some powerful insights! To be able to join your transactions data with web traffic data, you need some field that is common across those datasets. UserID might be one of the most useful fields that you could use for data blending.
2) Also having UserID in your reports let’s you perform some user behavior analysis at individual level and that could be pretty powerful too!
There are three steps:
Identify the User ID
Create a Custom Dimension on Google Analytics
Modify your tracking code to send data for the custom dimension
Step 1. Identify the User ID
The first step is to identify the ID that you are going to send to Google analytics. Remember that you can’t send PII (personally identifiable information) so you can’t send something like an email id but you can send other ID’s that may be used in your database.
Step 2: Create a custom dimension on Google Analytics
Go to google analytics admin section > Select your account & property > Go to Custom dimensions
Now, create a new custom dimension. Give it a name, scope it and make sure it’s marked active.
Step 3: Modify your tracking code to send data for the custom dimension
Notice that once you create the dimension, it will also show the example code (on the right side in the picture above). Send this to your web developer!
Just a note on this: The custom dimensions for which you are passing value using the tracking code are referenced as dimensionxx and you don’t use the Name like “Custom User ID” — if it’s the first custom dimension that you will refer to it as dimension1 in the code.
There are two type of things I learned in Graduate school:
2. Not Useful! (useless)
This post is NOT about discussing useless learning’s! So let me share one of most useful things that I learned in my two years at a School of management: How to solve business problems? Sounds cliched but that was, I think, one of the most important skills I picked up there. In particular, I learned about Frameworks used to solve business problems, one of them was called “MECE” which is what I want to share with you in this post.
(Side-note: Most folks learn this at some strategy consulting firm like McKinsey but unlike them, I learned about it in school)
Before we begin, I want to share about why you should care and then I’ll talk about what is it.
No matter which team you work for, you are solving problems. You wouldn’t have a job if you’re not doing that — so why not get better at it?
If you want to find a root cause of a business problem (& find the solution faster!) then you need to break it down…to break it down, you need to structure it. Now, they are many ways (or Frameworks) to structure a problem — MECE is one of the most effective frameworks out there. So lets learn about that:
(side-note: MECE framework may sound like a simple idea BUT it’s NOT easy to apply!)
What is MECE?
It’s an acronym and it stands for “Mutually Exclusive and Collectively Exhaustive” which means that when you break a problem into sub-items then they should be:
1. Not overlap with each other (mutually exclusive)
2. If you add up all sub-items then it should represent all possible solutions (collectively exhaustive)
Let’s take an example:
Say that you are asked to analyze “why is Profitability declining?”
Here’s a non-MECE way:
Find Top 10% profitable products [does NOT pass the collectively exhaustive test]
Out of them find products that are have declining profits
Try to find reasons why those products would have declining profits
Here’s a MECE way:
Visual for MECE principle
Break it down to Revenue & Cost
let’s start with cost, let’s say it’s constant = revenue must be going down for declining profits
further break down revenue into 1) Revenue from all non-usa locations 2) USA locations (Note the use of MECE principle here)
let’s say that revenue for non-usa locations is increasing, then it must be USA locations that’s the problem! (Note how effectively are able to narrow down and find the root cause faster!)
Let’s further break down to product categories for USA locations…Continue breaking down the sub-items in a MECE way till you find the root cause
I hope that gives you a good overview of MECE principle.
MECE is one of the few effective frameworks that you can use to solve a business problem. If you want to get better at structuring your ideas (to solve business problems), consider practicing MECE as there are ample resources available online that would help you master this!
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.
How would get first/last transaction details for each user account?
Here’s a design pattern:
select AllTransactions.user_id,AllTransactions.purchase_amt from user_transaction_details AllTransactions inner join ( select user_id, min(transaction_timestamp) as first_transaction_timestamp from user_transaction_details group by user_id ) FirstTransaction on AllTransactions.user_id = FirstTransaction.user_id and AllTransactions.transaction_timestamp = FirstTransaction.first_transaction_timestamp
To get the last transaction details for each user account, replace min function with max and that should be it.
Note: Depending on your data model and how you used it in the join, it might be that there would be multiple rows marked as “first/last” transaction and so would want to spend some time figuring out how to deal with these multiple row situation especially if you’re planning to further join this data.
Conclusion: In this post, I shared a design pattern to write a SQL query to get first/last transaction details for each user account type.
Question for you: How would you solve this problem? Share you query in the comments section!