Presented at #sqlpass summit 2015.
Presented at #sqlpass summit 2015.
Presented at #sqlpass summit 2015.
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.
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!
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!)
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:
Here’s a MECE way:
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!
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.
There are many techniques to analyze data. In this post, we’re going to talk about two techniques that are critical for good data analysis! They are called “Benchmarking” and “Segmentation” techniques – Let’s talk a bit more about them:
It means that when you analyze your numbers, you compare it against some point of reference. This would help you quickly add context to your analysis and help you assess if the number if good or bad. This is super important! it adds meaning to you data!
Let’s look at an example. CEO wants to see Revenue numbers for 2014 and an analyst is tasked to create this report. If you were the analyst, which report would you think resonated more w/ the CEO? Left or Right?
I hope the above example helped you understand the importance of providing context w/ your data.
Now, let’s briefly talk about where do you get the data for benchmark?
There are two main sources: 1) Internal & 2) External
The example that you saw above was using an Internal source as a benchmark.
An example of an external benchmark could be subscribing to Industry news/data so that you understand how your business is running compared to similar other businesses. If your business sees a huge spike in sales, you need to know if it’s just your business or if it’s an Industry wide phenomenon. For instance, in Q4 most e-commerce sites would see spike in their sales – they would be able to understand what’s driving it only if they analyze by looking at Industry data and realizing that it’s shopping season!
Now, let’s shift gears and talk about technique #2: Segmentation.
Segmentation means that you break your data into categories (a.k.a segments) for analysis. So why do want to do that? Looking at the data at aggregated level is certainly helpful and helps you figure out the direction for your analysis. The real magic & powerful insights are usually derived by analyzing the segments (or sub sets of data)
Let’s a look at an example.
Let’s say CEO of a company looks at profitability numbers. He sees $6.5M and it’s $1M greater than last years – so that’s great news, right? But does that mean everything is fine and there’s no scope of optimization? Well – that could only be found out if you segment your data. So he asks his analyst to look at the data for him. So analyst goes back and after some experimentation & interviews w/ business leaders, he find an interesting insight by segmenting data by customers & sales channel! He finds that even though the company is profitable – there is a huge opportunity to optimize profitability for customer segment #1 across all sales channel (especially channel #1 where there’s a $2M+ loss!) Here’s a visual:
I hope that helps to show that segmentation is a very important technique in data analysis!
In this post, we saw segmentation & benchmark techniques that you can apply in your daily data analysis tasks!
A quick blog post to let you know about a #sqlpass webinar on 1/15.
Description: The world is becoming more efficient. Today, seventy percent of the companies that graced the Fortune 1000 list a mere decade ago have vanished. Agility and survival are function of innovation, culture, and the ability to predict the future. To that end, data analytics offers a lifeline, a means of survival that will drive productivity and continue to disrupt and redefine business. However, the resources available to today’s business leaders sit on two vastly different ends of the spectrum. On the one hand, highly technical academic resources and on the other largely fluffy overviews of value propositions and potentials. The state of the industry shouldn’t be surprising. The same dynamics played out in early years of the internet. Software providers, technical leaders, and consulting firms greatly benefit from mystifying the world of data analytics into something that is incomprehensible. That lack of conceptual understanding is incredibly risky and propels the cost of analytics initiatives upwards. This webcast aims to bridge that gap between the technical data scientists and business leaders. Ultimately, this understanding will help to: – Connect the strategic goals of business leaders with the capabilities of technical advisers – Focus investments and initiatives within analytics and technology – Distill immensely complex subject matter into comprehensible examples – Accelerate the path to value and increase the ROI of analytics initiatives
Alex is a Predictive Analytics Architect in the Oil and Gas industry with a passion for distilling complexity into insights and evangelizing data science. His work has been featured on KDNuggets and he was recognized by DataScienceCentral as a top 180 blogger in 2014.
I hope to see you there!