Agile Business Intelligence Maturity Model

As outlined in my previous blog agility in business intelligence projects can’t be produced directly. Instead you should invest into professionalism, standardization and automation. In this post I’m showing an overview of concrete building blocks to support you on this way.

In my Agile Business Intelligence Maturity Model (ABIMM) I’ve collected many building blocks and arranged them in a practical sequence. An overview you can find in the following illustration:

Agile Business Intelligence Maturity Model

Agile Business Intelligence Maturity Model

We can extract a few key messages from this model:

  1. You can’t increase agility directly – you can only reduce the amount of needed upfront design. By doing this agility is increased automatically.
  2. A reduction of upfront design leads inevitably to higher risks – risks you need to deal with actively, e.g. by using a version control system or solutions for test automation (cf. my blog post here). As long as such basic infrastructure elements aren’t available you should be very cautious with introducing iterative, incremental procdures like e.g. Scrum. (A very illustrative presentation about Agility requires Safety you can find here)
  3. All beginnings are difficult: The building block “Agile Basics & Mindeset” represents an enormous hurdle in many cases. As long as an organization doesn’t experience a top down transformation towards agile values and principles (cf. e.g. the Agile Manifesto), it doesn’t make much sense to start with it bottom-up.
  4. The gulf can be overcome by buying the necessary tools for test automation, version control and training for employees. This can typically happen within the boundaries of the already existing infrastructure. But to overcome the chasm, todays often heterogenous, multi layered BI tool landscapes aren’t suited very well. That’s one reason why I’ve become a big fan of data warehouse automation and tools like WhereScape. Products like WhereScape RED institutionalize the usage of design patterns in an integrated development environment. Only for this reason e.g. refactoring on the level of the data model and hence iterative data modeling becomes feasible with realistic effort. At the same time tools like WhereScape provide you with an ultra high degree of automation for the deployment of new and changed artefacts.

A more detailed explanation of the Agile BI Maturity Model can be found in my recent article in the German TDWI journal “BI-Spektrum“.

Here you find the English translation of my article!

Many thanks to my company IT-Logix and all the great staff working with me. You are the indispensable foundation for all my BI related work. Don’t forget, you can hire me as a consultant 😉

Following you’ll find the literature list on which the different building blocks and the model itself is based on:

[AmL12] Ambler Scott W., Lines Mark: Disciplined Agile Delivery: A Practitioner’s Guide to Agile Software Delivery in the Enterprise, IBM Press, 2012

[AmS06] Ambler Scott W., Sadalage Pramod J.: Refactoring Databases: Evolutionary Database Design, Addison-Wesley Professional, 2006

[Bel] Belshee Arlo: Agile Engineering Fluency http://arlobelshee.github.io/AgileEngineeringFluency/Stages_of_practice_map.html

[BiM] Memorandum für Agile Business Intelligence: http://www.tdwi.eu/wissen/agile-bi/memorandum/

[Col12] Collier Ken: Agile Analytics, Addison-Wesley, 2012

[CoS11] Corr Lawrence, Stagnitto Jim: Agile Data Warehouse Design: Collaborative Dimensional Modeling, from Whiteboard to Star Schema, DecisionOne Press, 2011

[Hug12] Hughes Ralph: Agile Data Warehousing Project Management: Business Intelligence Systems Using Scrum, Morgan Kaufmann, 2012

[HuR09] Humble Jez, Russell Rolf: The Agile Maturity Model – Applied to Building and Releasing Software, http://info.thoughtworks.com/rs/thoughtworks2/images/agile_maturity_model.pdf, 2009

[Kra14] Krawatzeck Robert, Zimmer Michael, Trahasch Stephan, Gansor Tom: Agile BI ist in der Praxis angekommen, in: BI-SPEKTRUM 04/2014

[Sch13] Schweigert Tomas, Vohwinkel Detlef, Korsaa Morten, Nevalainen Risto, Biro Miklos: Agile maturity model: analysing agile maturity characteristics from the SPICE perspective, in Journal of Software: Evolution and Process, 2013 (http://www.sqs.com/de/_download/agile_maturity_wiley_2013_final.pdf)

Parts of this blog have first been published in German here.

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Steps towards more agility in BI projects

“We now do Agile BI too” – such statements we hear often during conferences and while discussing with customers and prospects. But can you really do agility in Business Intelligence (BI) and data warehouse (DWH) project directly? Is it sufficent to introdouce bi-weekly iterations and let your employees read the Agile BI Memorandum [BiM]? At least in my own experience this doesn’t work in a sustainable way. In this post I’ll try to show basic root cause relations which finally lead to the desired agility.

DWHAutomation

If at the end of the day we want more agility, the first step towards it is “professionalism”. Neither an agile project management model nor an agile BI toolset is a replacement for “the good people” in project and operation teams. “Good” in this context means, that the people who work in the development and operation of a BI solution are masters in what they do, review their own work critically and don’t do any beginner’s mistakes.

Yet, professionalism alone isn’t enough to reach agility in the end. The reason for this is that different experts often apply different standards. Hence the next step is the standardization of the design and and development procedures. Hereby the goal is to use common standads for the design and development of BI solutions. Not only within one team, but ideally all over team and project boundaries within the same organization. An important aid for this are design patterns, e.g. for data modeling, the design and development of ETL processes as well as of information products (like reports, dashboards etc.).

Standardization again is a prerequisite for the next and I’d say the most important step towards more agility: The automation of as many process steps as possible in the development and operation of a BI solution. Automation is a key element – “Agile Analytics” author Ken Collier dedicateds even multiple chapters to this topic [Col12]. Because only if we reach an high degree of automation we can work with short iterations in a sustainable way. Sustainable means, that short iterations don’t lead to an increase in technical depts (cf. [War92] and [Fow03]). Without automation, e.g. in the areas of testing, this isn’t achievable in reality.

Now we are close to the actual goal, more agility. If one can release new and changed features to UAT e.g. every two weeks, these can be released to production in the same manner if needed. And this – the fast and frequent enhancement of features in your BI solutions is what sponsors and end users perceive as “agility”.

(this blog was originally posted in German here)

Event hints:

Literature:

[BiM] Memorandum for Agile Business Intelligence: http://www.tdwi.eu/wissen/agile-bi/memorandum/

[Col12] Collier Ken: Agile Analytics, Addison-Wesley, 2012

[War92] Cunningham Ward: The WyCash Portfolio Management System, http://c2.com/doc/oopsla92.html, 1992

[Fow03] Fowler Martin: Technical Debt, http://martinfowler.com/bliki/TechnicalDebt.html, 2003

Testing for BI & DWH

Since ever testing is part of every IT project plan – that’s true as well for Business Intelligence (BI) & Data Warehouse (DWH) projects. The practical implementation of testing in the BI / DWH environment has confronted me with troubles in the past again and again. Often I’ve had the impression that the BI / DWH world is still back in the Stone Age regarding development processes and environments. At least it is significantly behind the maturity level I know from the software engineering domain. The below chart illustrates this gap:

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Cultural differences between the software development and BI community (Source: http://agiledata.org/essays/culturalImpedanceMismatch.html)

If there is something tested at all, typically in the BI frontend area things are tested manually. In the DWH backend we see – besides manual tests – self coded test routines, e.g. in the form of stored procedures or dedicated ETL jobs. However the integration into a test case management tool and systematic evaluation of the test results doesn’t happen. This is heavily contrasting with the software engineering domain where automated regression testing combined with modern development approaches like test driven design are applied. At least for some time we find first inputs regarding BI specific testing (cf. the (German) TDWI book here). Concepts and paper are patient though. Where are we with regard to a possible tool support, namely for the area of regression tests?

Since summer 2014 we at IT-Logix are actively looking for better (tool based) solutions for BI specific testing. We do this together with the Austrian company Tricentis. Tricentis develops the Tosca product suite, one of the worldwide leading software solutions for test automation. In a first step we run a proof of concept (POC) for regression tests for BI frontend artefacts, namely typical reports. One of the architectural decisions was to use Excel and PDF export files as a base for our tests. With this choice of a generic file interface the efforts to develop BI product specific tests were omitted. And this way we reduced the implementation effort to about two days in the POC. The goal was to run “Before-After” tests in batch mode. We took 20 reports for the POC case (these were actually SAP BusinessObjects Web Intelligence reports, but you can imagine whatever tool you like as long as you can export to PDF and / or Excel). A current version of the PDF or Excel output of the report is compared with a corresponding reference file. Typical real life situations where you can use this scenario are:

  • recurringly scheduled regression tests to monitor side effects of ongoing DWH changes: The reference files are created somewhen e.g. after a successful release of the DWH. Imagine there are ongoing change requests on the level of your DWH. Then you want to make sure these changes only impact the reports where a change is expected. To make sure all the rest of your reports aren’t concerned by any side effects, you now run your regression tests e.g. every weekend and compare the hereby produced files with the reference files.
  • BI platform migration projects: If you run a migration project for example to migrate your SAP BusinessObjects XI 3.1 installation to 4.1, you’ll want to make sure reports still work and look the same in 4.1 as they did in XI 3.1. In this case you create the reference files in XI 3.1 and compare them with the ones from 4.1. (As the export drivers vary between the two versions, especially the Excel exports are not very useful for this use case. Still, PDF worked pretty fine in my experience)
  • Database migration projects: If you run a database migration project for example migrating all your Oracle databases to Teradata or SAP HANA, then you want to make sure all of your reports show still the correct data (or at least the data as was shown with the original datasource…)
rbra_testing1 (3)

Sample configuration of a test case template using the GUI of Tosca (Source: IT-Logix POC)

Tosca searches for the differences between the two files. For Excel this happens on a cell by cell basis, for PDF we used a text based approach as well as an image compare approach.

rbra_testing1 (2)

Depending on the chosen test mode the differences can be visualized differently (Source: IT-Logix POC)

Using the solution implemented during the POC we could see very quickly which reports were different in their current state compared to the reference state.

Another important aspect of the POC was the scalability of the solution approach as I work primarily with (large) enterprise customers. If I have not only 20 but hundreds of reports (and therefore test cases), I have to prioritize and manage the creation, execution and error analysis of these test cases somehow. Tosca helps with the feature to model business requirements and to connect them with the test cases. Based on that we can derive and report classical test metrics like test case coverage or test execution rate.

rbra_testing1 (1)

Requirements and test cases are tightly related (Source: IT-Logix POC)

In my eyes an infrastructure like Tosca is a basic requirement to systematically increase and keep the quality in BI / DWH systems. In addition advanced methods like test driven development are only adaptable to BI / DWH undertakings if the necessary infrastructure for test automation is available.

In this blog post I’ve shown a first, rudimentary solution for regression tests for BI frontend tools. In a next article I’ll show the possibilities to implement regression test for DWH backend components.

Event recommendation: Learn about a real life scenario to run a SAP BusinessObjects migration project in an agile manner. Hence test automation is key and explained in some more details. Join me during sapInsider’s BI2015 at Nice by mid of June. Find more information here.

(This blog post was first published by me in German here)