Archive for Reporting tools

Coolest New Power BI Features Revealed!

Recently the Microsoft Business Applications Summit 2019 highlighted new Power BI features and these are the coolest features to note IMO:

1. New Power BI App Workspace Experience in Preview Power BI App Workspaces were introduced to enable collaboration amongst the data/business analysts within an organization. The new experience introduces numerous improvements to better enable a data-driven culture including:

•   Managing access using security groups, distribution lists, and Office 365 Groups
•   Not automatically creating an Office 365 group
•   API’s for Admins, as well as new tools for Power BI Admins to effectively manage workspaces

2. Printing Reports via Export to PDF
You can now easily print or email copies of reports by exporting all visible pages to PDF.

3. Bookmark Groups
Now you have a way to organize bookmarks into groups for easier navigation.

4. Python Integration in Preview
Now data scientists can use Python in addition to R within Power BI Desktop.

5. New Visual Header
More flexibility and formatting options have been added to the header section of each visual.

6. Tooltips for Table and Matrix Vizs
Report page tooltips are now available for the table and matrix visuals

7. Many to Many Relationships in Preview
You will now be able to join tables using a cardinality of “Many to Many” – prior to this feature, at least one of the columns involved in the relationship had to contain unique values.

And now I’ve saved the best for last!

8. Composite Models in Preview
With this feature, you’ll now be able to seamlessly combine data from one or more DirectQuery sources, and/or combine data from a mix of DirectQuery sources and imported data. For example, you can build a model that combines sales data from an enterprise data warehouse using DirectQuery, with data on sales targets that is in a departmental SQL Server database using DirectQuery, along with some data imported from a spreadsheet.

As you can see there are many new features to digest but it would be well worth your while to follow the links provided.

On a closing note, I’d like to give you a teaser for two new features coming up soon that will have a huge impact on self-service data prep and querying for big data:

  • Dataflows
  • Aggregates

Stay tuned!
Mario Di Giovanni, BASc, MBA, CBIP
Director, Business Analytics

More about Mario

 

An Approach to Analytics both Hamilton and Jefferson Could Embrace

Happy 4th of July!  What a great time to think about data independence, democratization, and governance for your association.  In this post we’ll talk about the balance between the central management of data by IT and data directly managed by association staff.
Leading analytics tools provide great capabilities to empower people to make data-guided decisions. The ability to analyze diverse data from a breadth of sources in a usable way for association staff is a key feature of these tools. Examples include Power BI Content Packs and Tableau Data Connectors. These range from pre-built data sources based on specific applications such as Dynamics, Google Analytics, and Salesforce; to relatively rarer “NoSQL” sources such as JSON, MarkLogic, and Hadoop data. These tools rapidly make data from specific applications available in formats for easy reporting, but can still lead to data silos. Tools such as Power BI and Tableau provide dashboard and drill-through capabilities to help bring these difference sources together.

Downstream Data Integration

This method of downstream integration is commonly described as “data blending” and “late binding”. An application of this approach is a data lake that brings all data into the environment but only integrates specific parts of data for analysis when needed. This approach does present some risks, as the external data sources are not pre-processed to enhance data quality and ensure conformance. In addition, business staff can misinterpret the data relationships that can lead to incorrect decisions. This makes formal training, adoption, and governance processes even more vital to analytics success.

What about the Data Warehouse?

When should you consider using content packs and connectors and how does this relate to a data warehouse and your association? The key is understanding that they do not replace a data warehouse, but is actually an extension of it. Let’s look at a few scenarios and approaches.

  • Key factors to consider when combine data is how closely the data is linked to common data points from other sources, the complexity of the data, and the uniqueness of the audience. For example, people throughout the association want profitability measures based on detailed cost data from Dynamics, while the finance group has reporting needs unique to their group. An optimal approach is to bring cost data into the data warehouse while linking content pack data by GL codes and dates. This enables finance staff to visualize data from multiple sources while drilling into certain detail as part of their analysis.
  • Another consideration is the timeliness of data needed to guide decisions. While the data warehouse may be refreshed daily or every few hours, staff may need the immediate and real-time ability review data such as meeting registrations, this morning’s email campaign, or why web content has just gone viral. This is like the traditional “HOLAP”, or Hybrid Online Analytical Processing, approach where data is pre-aggregated while providing direct links to detailed source data. It is important to note that analytical reporting should not directly access source systems on a regular basis, but can be used for scenarios such as reviewing exceptions and individual transaction data.
  • In some cases, you might not be sure how business staff will use data and it is worthwhile for them to explore data prior to integration into the data warehouse. For example, marketing staff might want to compare basic web analytics measures from Google Analytics against other data sources over time. In the meantime, plans can be made to expand web analytics to capture individual engagement, align the information architecture with a taxonomy, and track external clicks through a sales funnel. As these features are completed, you can use a phased approach to better align web analytics and promote Google Analytics data into the data warehouse. This also helps with adoption as it rapidly provides business staff with priority data while introducing data discovery and visualizations based on actual association data.
  • Another important factor is preparing for advanced analytics. Most of what we’ve described involves interactive data discovery using visualizations. In the case of advanced analytics, the data must be in a tightly integrated environment such as a data warehouse to build predictive models and generate results to drive action.

It’s not about the Tools

The common element is that using data from sources internal and external to your association requires accurate relationships between these sources, a common understanding of data, and confidence in data to drive data-guided decisions. This makes developing an analytics strategy with processes and governance even more important. As we’ve said on many occasions: it’s not about the tools, it’s the people.
Your association’s approach to data democratization doesn’t need to rise to the level of a constitutional convention or lead to overly passionate disputes.

Association Data Visualization: 5 Steps You Need to Know

5 Steps to Visualize Association Data

BI for AssociationsBig Data, business intelligence, analytics…..these terms have been buzz words headlining most association conferences this past year. When DSK presented at the ASAE Tech Conference last December, 80% of respondents to our straw poll noted their #1 question was “How do I get started with BI?” DSK has defined 5 Steps that form a framework for analytics and business intelligence initiatives for associations:

1. Scope your Project
Start small and define success with your stakeholders. Outline what you already know and document your business questions.
2. Collect your Data
Identify your data sources then describe your business rules in plain English. Inventory and integrate your data for future projects as well.
3. Clean your Data
Determine how clean is “clean” for your organization. Correct duplicate, missing, and inconsistent data and create standard processes to prevent future data issues.
4. Analyze your Data
This is the fun part! Create data visualizations and evaluate the results based on your business context. Did you answer your original questions or discover new questions that ‘you didn’t know you didn’t know?’ It’s an iterative process that will evolve based on feedback.
5. Communicate your Results
Describe the results with meaningful stories to start the conversation. Make sure the level of detail is appropriate for the audience and document new questions that surface for future analysis.
This brief video further illustrates how your association can harness the benefits of BI and demonstrates Tableau data visualization software.

Debbie King is the CEO and founder of DSK Solutions, Inc. Debbie holds a Bachelor of Science degree in Decision Science and Management Information Systems and is a graduate of the Entrepreneurial Master’s Program at MIT. She is certified as a Project Management Professional (PMP), Certified ScrumMaster (CSM) and Balanced Scorecard Professional (BSP). She holds six current Microsoft SQL certifications, including MCITP Business Intelligence Developer. Debbie will complete her Master's Degree in Leadership at Georgetown University's McDonough School of Business in March of 2014. She is frequent speaker on the subject of analytics and serves on the ASAE Technology Council.

Debbie King is the CEO and founder of DSK Solutions, Inc.