Presented at #sqlpass summit 2015.
Here’s a link to download the Titanic data — http://lib.stat.cmu.edu/S/Harrell/data/descriptions/titanic.html — it’s really useful in analytics and data science projects. You can:
- Build a predictive model. Example: https://www.kaggle.com/c/titanic
- I also use this data set to create interactive dashboards on tools like Qlik and Tableau to understand their features.
If you liked this, you may also like other data sets that I have here: http://parasdoshi.com/2012/07/31/where-can-we-find-datasets-that-we-can-play-with-for-business-intelligence-data-mining-data-analysis-projects/
(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 saw this ad on a highway earlier today and my reaction: why would I switch to a network that has just “96%” coverage.
…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.
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!
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.
In this post, I am going to share five actions that you can take you if measure your analytics/business-intelligence solution usage:
I’ll highly encourage business stakeholders & IT managers to consider measuring the usage of their analytics/business-intelligence solutions. From a technical standpoint, it shouldn’t be a difficult problem since most of the analytics & business intelligence tools will give you user activity logs. So, what’s the benefit of measuring usage? Well, in short, it’s like “eating at your restaurant” – if you’re trying to spread culture of data driven decision-making in your organization, you need to lead by example! And one way you can achieve that is by building a tiny Business Intelligence solution that measures user activity on top of your analytics/business-intelligence solution. if you decide to build that then here are five actions that you can take based on your usage activity:
Let’s broadly classify them in two main categories: Pro-active & Reactive actions.
A. Pro-active actions:
1. Identify “Top” users and get qualitative feedback from them. Understand why they find it valuable & find a way to spread their story to others in the organization
2. Reach out to users who were once active users but lately haven’t logged into the system. Figure out why they stopped using the system.
3. Reach out to inactive users who have never used the system. it’s easy to find inactive users by comparing your user-list with the usage activity logs. Once you have done that, Figure out the root-cause – a. Lack of Training/Documentation b. unfriendly/hard-to-use system c. difficult to navigate; And once you have identified the root-cause, fix it!
B. Reactive actions:
4. If the usage trend if going down then alert your business stakeholders about it and find the root-cause to fix it?
Possible root causes:
– IT System Failure? Fix: make sure that problem in the system never happens again!
– Lack of documentation/Training? Fix: Increase # of training session & documentation
5. It’s a great way to prove ROI of an analytics/business-intelligence solution and it can help you secure sponsorship for your future projects!
In this post, you saw five actions that you can take if you measure your usage activity of your analytics/business-intelligene solution.
I hope this was helpful! I had mentioned user training in this article and so if you want to learn a little bit more about it, here are a couple of my posts:
In this post, I’ll list few examples from various industries to help you differentiate between business intelligence and data science problems.
Sometime back, I blogged about “Business Analytics Continuum” and in the post we saw that Every Organization has DATA but they use their business data at different levels because of their maturity level. Excel (or other transactional reporting tools) is usually the starting point for any organization – it helps them see WHAT happened. They advance to the next stage, where they get capabilities to slice and dice their data – To find out WHY – and usually this capability is delivered using Business Intelligence tools & techniques. Once the data culture spreads – Thanks to a successful Business Intelligence project – then they soon start to outgrow their business intelligence capabilities by asking problems that need predictive capabilities. This is advanced analytics and Data Science stage. To that end, here are 5 examples to help you differentiate between business intelligence and data science problems:
|Business Intelligence.(WHAT & WHY)||Data Science & advanced analytics.|
||Can you predict bike rentals on an hourly basis?|
||Can you predict the credit risk of the customer during contract negotiations stage?|
|Customer relationship management||
||Can you predict customer churn?|
||Can you predict whether a scheduled flight will be delayed by more than 15 minutes?|
||Can you classify a customer feedback comment into “positive”, “negative” or “neutral”?|
I hope this helps!