Data -> Insights -> ?

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.

I hope that helps! Just wanted to share that with you. What do you think?

-Paras

What percentage of users are authenticated? (Google Universal Analytics)

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

Google Analytics User ID Universal

4. On this report, you can see authenticated vs unauthenticated sessions:

Percentage of authenticated users google analytics Universal

Conclusion:

In this post, we talked about how to run a report that shows you percentage of authenticated users. (In google’s Universal analytics)

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!)

Conclusion:

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

Every Data Analyst Needs to check out this FREE excel add-in: Power Query!

Power Query is amazing! It takes the data analysis capabilities of Excel to whole new level! In this post, I am going to share three reasons:

1. it enables repeatable mash-up of data!

Have you every had to do your data analysis tasks repeatedly on the data with same structure? Do you get “new” data every other week and need to go through the same data transformation workflow to get to the data that you need?

What’s the solution? Well, you can look at MACRO’s! Or you can request your IT department to create a Business Intelligence platform. However, what if you need to modify your data mashup workflow then these solutions don’t look great, do they now?

Don’t worry! Power Query is here!

It enables repeatable mashup of data like you might have never seen before! You need to try it to believe.

It’s very easy to input new data to Power Query and it enables you to retrieve final output based on new data using a “refresh” feature.

Each data-mashup is recorded as steps which you can go back and edit if you need to.

Power Query Refresh

2. It’s super-flexible!

Any data mashup performed using Power Query is expressed using its formula language called “M”. You can edit the code if you need to and as you can imagine such a platform enables much-needed flexibility for the analyst’s.

3. It has awesome advance features!

Do you want to Merge data? How about Join? Are you tired with VLOOKUP’s! Don’t worry! it’s super easy with Power Query! Here’s a post: Join Excel Tables in Power Query

How about Pivot or Unpivot? Done! Check this out: Unpivot excel data using Power Query

How about searching for online & open data sets? Done!

How about connecting to data sources that “Data” section of Excel doesn’t support yet? (Example: Facebook) – DONE! Power Query makes that happen for you.

And That’s not a complete list!

Plus you can unlock the “Power” (pun intended) of Power Query by using it with other tools in Power BI Stack. (Power Pivot, Power View, etc…) OR you can use the your final output from Power Query with other tools too! After all it’s an excel file.

Action-Item!

If you haven’t already then check out Power Query! it’s free and works with Excel 2010 and above.

Author: Paras Doshi

Top two key techniques to analyze data:

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:

1. Benchmarking

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?

Benchmarking data providing context in analysis

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.

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:

segmentation data Improve profitability low margin service offerings customers

I hope that helps to show that segmentation is a very important technique in data analysis!

Conclusion:

In this post, we saw segmentation & benchmark techniques that you can apply in your daily data analysis tasks!

Five actions that you can take if you measure your analytics/business-intelligence solution usage:

Summary:

In this post, I am going to share five actions that you can take you if measure your analytics/business-intelligence solution usage:

Five actions!

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

downward trend line chart

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!

Conclusion:

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:

1. http://parasdoshi.com/2014/05/05/presented-at-sqlsat-305-dallas-ba-edition/

2. http://parasdoshi.com/2014/05/07/how-to-train-your-users-to-create-their-own-business-intelligence-reports-5-of-5-post-training/

Business Intelligence system – Customer Complaints – B2B company:

Business Intelligence system – Customer Complaints – B2B company:

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

Sales Bookings vs Quota Dashboard for a B2B company:

Sales Bookings vs Quota Dashboard for a B2B company:

Business Goal:

Need a daily report delivered in sales team’s inbox that shows Sales Team’s Bookings vs Quota for current & next month.

Brief Description:

Ability to see Bookings vs Quota in near real-time is a key to effectively manage performance for any sales team. Before the project, analyst(s) would have to manually put together this report and since the report took more than a day to put together they couldn’t afford to run it daily and so they delivered this report bi-weekly/monthly basis to the sales team. After the project, the process was automated and the sales team received an email with a report on a daily basis and this helped them see Bookings vs Quota in near real-time. As a famous saying goes “if you can’t measure it, you can’t improve it” (by Peter Drucker) – in this case, the report helped them measure their actual numbers against their goals and helping them improve their sales numbers which directly hits their top-line!

Tools used: SharePoint report subscription, SQL server analysis services, SQL Server Integration services, SQL server reporting services & Excel.

Mockup:

Note: Drill down reports are not shown and the numbers are made up.

Sales Team bookings vs quota dashboard

Cost Driver’s Dashboard for a Supply Chain Executive:

Cost Driver’s Dashboard for a Supply Chain Executive:

Summary:

Profitability equals revenue minus costs – To that end, A supply chain executive is mostly focused on optimizing cost elements to drive profitability. Here’s a mock up of a dashboard created for an executive to help him keep an eye on the overall health while making sure he gets alerted for key cost categories.

The Dashboard was created using profitability data-set & also had drill down capabilities to analyze numbers for cost buckets like Raw materials, manufacturing & logistics.

Mockup:

Supply Chain Cost Drivers Profitability Dashboard

Time Intelligence in MDX: last N days

it’s a common requirement to create a report that shows last N days of a business metric – so I thought I’ll post a template here for SQL server analysis server’s MDX query:


WITH
  MEMBER [Measures].[Sales_last_15_days] AS
    Sum
    (
      {
          [Calendar].[Date YYYYMMDD].CurrentMember.Lag(14)
        :
          [Calendar].[Date YYYYMMDD].CurrentMember
      }
     ,[Measures].[Sales]
    )

   MEMBER [Measures].[CurrDate] as
      "[Calendar].[Date YYYYMMDD].[" + Cstr(Year(Now())*10000+month(now())*100+day(now()))  +"]"

SELECT
  {
     [Measures].[Sales_last_15_days]
  } ON COLUMNS
FROM 
[CubeName]
WHERE
STRTOMEMBER([Measures].[CurrDate])

Here are things that you’ll need to adjust to make it work for your scenario:

1. Date Dimension Attribute & it’s format. The example shows yyyymmdd but you could have different format of the date.

2. Measure name. Instead of [Measures].[Sales] you’ll have to replace it with your business metric. Also, make sure you are using the right aggregate function, in the example above I have used SUM but you’ll have to change this based on your requirement.

3. Create a parameter and use it in index for the Lag function.

4. change [cubename] to your cube name.

I hope this gives you a good starting point to create last N days for your business metric.