Self-Service BI - Drivers and Enablers

Enabling Self-Service BI
Following up on my last post, 10 Principles of Self-Service Business Intelligence, I thought I'd provide some links to other key articles on this subject. As you read below, self-service BI was defined as an alternative to complex BI solutions, overwhelmed IT departments with reporting needs, and error prone spread sheets. Gartner and TDWI's articles are particularly good reads for ideas and insights on BI-driven organizational transformation. 

  •  Gartner's Kurt Schlegel has a presentation, How to Deliver Self Service Business Intelligence with a nice slide (Slide 10) depicting why self-service. On one side, there is the "dull side" of analytics covering static and basic interactive reports and on the other, there is the "dark side" of data dumps processed in Microsoft Excel or Access. Self-Service BI lies in the middle of these extremes and allows data scientists to ask good questions and conduct data discovery tasks that lead to new insights.

  • TechTarget reports that Self Service BI reports that user interfaces must be intuitive and that IT departments should take responsibility to define a metadata dictionary and security policies.

  • Information Age reported on the impact of Self Service BI on key technology enablers. Tableau Software saw sales double in 2012 to $128 million. In April QlikTech reported 22% revenue growth year-on-year for its first quarter of the financial year up to $96.5 million. Meanwhile established BI vendors like MicroStrategy  reported a 6% decline in sales to $130 million in a recent quarter.

  •  In 5 Ways to Bring Self-Service BI to Small and Mid-Size Businesses, the issues with relying on spreadsheets are spelled out -
    Spreadsheets containing confidential data are often emailed around among employees, representing a potential data security breach. Spreadsheet formula syntax is not intuitive, making it difficult to find and debug errors. Macros, pivot tables and links to other spreadsheets are fragile and easily broken. Spreadsheet data can be typed over and changed so it is subject to errors.
    - however, the author notes that these spreadsheets are the best starting points for transforming to a BI self service program
  • Back in November 2011, TDWI's Introduction to Self-Service BI reported that 78% of survey respondents stated that they needed a faster time to value from BI solutions leading many businesses to seek alternative approaches. It lists two key themes — usability and consumability — that are the foundations of many self-service BI tools.
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10 Principles of Self Service BI - What Data Scientists Need


I'm a CIO, but when time permits, I'm also a data scientist and junky. I like to roll up the sleeves and perform data discovery looking for correlations that provide insight and guide decisions.

There is an increasing need to identify individuals in the organization with similar skills and goals. In my experience, there are more people in the organization with some analytical capabilities and capable of deriving intelligence from data, but may not have the mechanisms to perform these tasks. As CIO, one of my objectives is to identify these individuals, provide them the technical capabilities they need to excel at analytics, and partner with other leaders to cultivate a data driven culture. 

"Old school" Business Intelligence "solved" this issue by centralizing a group - sometimes reporting into IT but often not - responsible for analytics including  developing reports, establishing dashboards, and completing adhoc analysis. This was a reasonable approach when computing resources were expensive, analytical tools complex, and talent scarce.

Availability of talent is still an industry concern, but computing resources including cloud computing should not be the bottleneck for most data sets. New easier to use analytical tools provide scalable on ramps for more organizations to become more analytical and data driven. The analytical tools are marketing themselves as "self service BI" and include products from Microsoft, Tableau and QlikView. These tools have intuitive user interfaces and help analysts develop data visualizations without the need of a lot of (or any) programming or SQL. The "self service", implies the analysts can do all, or a majority of their work without IT resources or with services from other organizations or experts. The implications of self service is the potential for more users in different departments to localize their analysis to their needs. 

But these tools are only one aspect of establishing a self service BI capability. Here is my definition of what users have to do "easily" in order to deliver on this promise. A user wants to

  1. Know what data repositories exist in the organization and what type of data exist in them.
  2. Make requests to get access to data, get tools installed, or find out where documentation is stored without significant delay.
  3. Understand individual data repositories by leveraging easy to understand documentation that defines data fields, data flows in and out, connected applications, and data sources. 
  4. Comply with governance and rules on proper use of data. 
  5. Connect with "owners" or subject matter experts on data repositories to ask questions.
  6. Develop their expertise with analytical tools. Know how to request support from internal experts or from technology providers. 
  7. See working examples of dashboards, reports, or analysis performed on the data.
  8. Have some understanding on data quality issues and any efforts underway to make improvements.
  9. Escalate and resolve technical needs such as performance, linking data, or loading in new data sources. 
  10. Leverage organizational best practices on implementing visualization standards, collaborating with other data scientists, publishing and referencing findings, and sharing information with colleagues.

Looking at these as the "Principles of Self Service BI", my follow up posts will cover more details fulfilling some of them.
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About Isaac Sacolick

Isaac Sacolick is President of StarCIO, a technology leadership company that guides organizations on building digital transformation core competencies. He is the author of Digital Trailblazer and the Amazon bestseller Driving Digital and speaks about agile planning, devops, data science, product management, and other digital transformation best practices. Sacolick is a recognized top social CIO, a digital transformation influencer, and has over 900 articles published at InfoWorld, CIO.com, his blog Social, Agile, and Transformation, and other sites. You can find him sharing new insights @NYIke on Twitter, his Driving Digital Standup YouTube channel, or during the Coffee with Digital Trailblazers.