Presented at #sqlpass summit 2015.
You have your SQL Server Reporting Services environment in native mode — and you want to modify the data source of a report there.
- Navigate to Report Manager.
- Navigate to the Report that you want to Manage and run it
- After the report renders, you will have a breadcrumb navigation on the top right
- Click on the Last Part of the Breadcrumb Navigation
- It should open up the “properties” section of this report
- On the properties section, you should be able to manage the data source
- Make the changes that you wanted to the data source settings of this SSRS report — and don’t forget to click “apply”
Author: Paras Doshi
Are you trying to import an Excel file into SQL Server using SQL Server Integration services…And ran into error that has words like “Non unicode” and “unicode”? Then this blog is for you.
Why does this error occur?
Well it turns out that things like SQL Server and Excel have encoding standards that they follow which provides them a way to process, exchange & store data. BUT turns out that SQL Server and Excel use different standards.
So, the solution is simple right? Import the data from Excel into non-Unicode format because that’s what you need for SQL Server.
So how do you that? Between your Source and Destination tasks, include a task called “Data conversion” and do the following for all columns that have text:
And in the destination task, you’ll have to make sure that the mapping section using the new output aliases that you defined in the “data conversion” step.
In this post, we learned about how to solve a common error that pops up when you try to import excel file to sql server using SSIS. Hope that helps.
Author: Paras Doshi
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
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.
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
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.
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.
If you haven’t already then check out Power Query! it’s free and works with Excel 2010 and above.
Author: Paras Doshi
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!
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: