Self-Service BI Truths

The primary goal of self-service BI is to enable business people to perform business analytics in a timely and self-sufficient manner. A secondary goal for many business people is that the information they use in that analysis is consistent, comprehensive, correct and current.

Supporting objectives:

• More pervasive and in-depth use of analytics in decision-making
• Reduced time to insight
• Improved business and IT productivity

Self-Service BI Myths

There are too many myths and hopes that impede successful self-service BI efforts:

• Business people want to build their own reports. No they don’t, but they do want to perform their own analysis.
• You need to eliminate IT from BI. No, it’s not that simple. It’s naïve to think that you can give business people the latest wave of BI tools and avoid working with IT. Although an enterprise does want to get IT staff out of the perpetual queue of creating BI reports and dashboards, or writing custom ad-hoc queries to support new or modified business analytical requirements, business people will get frustrated without IT involvement (we’ll discuss in what areas later)  and likely create the next wave of data shadow systems that are data silos.

• All you need is the latest self-service BI tool. Not even close. This is a great sales pitch that both business and IT would love to be true. It would great if buying a BI tool eliminated the hard work of, for example, getting the data consistent and governed, but unfortunately if it was just technology then self-service BI would have been the norm years ago.
• The problem is that business people just cannot access the data they want. No, the problem is not access, it’s the data inconsistency and quality. This is the sales pitch in almost every BI demo I have seen for the last decade. The disclaimer on these demos should also be that their product works great as long as all your data is consistent, comprehensive, clean and current.

• One size fits all. No way. Business people have different degrees of analytics needs and skills, need to use a diverse data types and volumes, and are trying to answer a wide variety of business questions.  It’s short-sighted to assume that a single BI analytical style or tool could solve everyone’s problems.

While it’s true that each new generation of BI tools is more powerful and easier to use, when you get past the hype and look at real implementations, you’ll see that the most pervasive BI remains the spreadsheet (which is often embedded in data shadow systems or spreadmarts).

There’s nothing new here. People have been writing about the limitations of self-service BI for years:

•  Eckerson: Self-service business intelligence not a give-and-go affair, 2013
• The Elusive Promise of Self-Service BI, Sep 14, 2009
• Self-service business intelligence tools failing to make headway at most organizations, Jan 27, 2009
• Moving BI to the Enterprise, Sep 1, 2005

What does the failure of self-service BI mean for Big Data? Big problems. Unless self-service and pervasive BI become the norm, enterprises will never productively tap the business ROI from Big Data no matter how much they invest in it.

If enterprises are to implement self-service BI, whether for Big Data or the traditional data in their enterprise, then they have to understand what it is and what it is not. Once enterprises understand what they are trying to achieve, then they are likely to do things differently to avoid the traps that each wave of self-service efforts have encountered.

Stay tuned, my next few posts will explore these issues in more detail.

• When: Thursday, May 9, 2013
  12:00 p.m. - 5:00 p.m.
• Where: Deveber Conference Room
  Children’s Hospital Waltham
  9 Hope Avenue
  Waltham, MA 02453
• Costs: none
• Topics: "Implementing a new BI Stack at Children’s Hospital” by Ron Wilkinson, Children’s Hospital Boston
  “Top Trends Revitalizing BI” by John Bair, CTO, Launchpoint Corporation

"Implementing a new BI Stack at Children’s Hospital”
Children’s Hospital recently implemented a completely new BI stack consisting of Netezza data storage, Informatica ETL processes, and a MicroStrategy front-end. The presentation will cover architectural decisions, experiences migrating from an existing BI implementation to a completely new stack as well as lessons learned.

“Top Trends Revitalizing BI”
Attendees will gain insights into:

• Why Big Data, Advanced Analytics and Cloud Computing technologies matter
• Architectures for building new business analytic services
• Strategies for moving from passive to active analytics
• Approaches for helping your business stakeholders lead with BI

Read the full agenda.

Register http://tdwichapters.org/pages/boston/event-registration.aspx.

Solutions must be cost- and resource-effective - All enterprises need to operate within budgetary and resource constraints, whatever that means for their size and industry. The solution should be to be right-sized to meet the enterprise’s data and analytical needs along with the resources and skills that will sustain the solution.

Sufficient infrastructure must be available for conducting business analytics - The infrastructure for an analytical sandbox includes:

• Processing, such as PCs and servers (physical, virtual and cloud)
• Storage (physical and cloud)
• Integration capabilities (physical and virtual)
• Self-service BI tool(s)

This infrastructure must be scalable and expandable as the data volumes, integration needs and analytical complexities naturally increase.  Insufficient infrastructure has historically limited the depth, breadth and timeliness of analytics as business people used their PC and spreadsheets to fill shortfalls.

To read four more suggestions for analytical sandbox architecture plus detailed architecture options and advice, see the white paper Analytics Best Practices: The Analytical Sandbox.