Experimenting and Testing (WEB ANALYTICS)

Agile Analytics a New Buzz Word: What in the World is Agile Analytics?

How does it apply to Web Analytics?

By: Chris Preimesberger 2013

http://www.eweek.com/cloud/slideshows/agile-analytics-what-it-is-and-10-best-practices-for-using-it

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In the world of web analytics and the collection of visitor and customer data, this data can become

voluminous. Of course this data must be stored in a data warehouse for retrieval at some point.

Enterprises of all sizes are now realizing they have dormant data in silos that they can put to work for

them. For example, this so-called big data, if analyzed correctly, can help project sales spikes, predict

raw material needs and help companies understand their customers better, and this is just a sampling of

what it can do. However, too often, enterprises find themselves ill-equipped to gather, cleanse and

analyze this data, and therefore unable to act upon potential insights or gain competitive advantages.

Agile big data analytics focuses not on the data itself but on the insight and action that can ultimately be

drawn from nimble business intelligence systems. Rather than beginning with investment and platform

building, Agile analytics starts with learning and testing, so that companies can build their models and

strategies based on solid answers to their most crucial business questions. The sources for this slide

show include big data consultancy ThoughtWorks and eWEEK reporting.

Collaborate Across Business Communities

Data warehousing and business intelligence systems will live in a diversity of environments, not

just in the IT department. It's important to treat business owners, technical experts, project

managers and the many communities of users across the organization as members of the team by

allowing them to offer input and test working features as they're developed.

Educate Stakeholders

Because most stakeholders aren't well-versed in data warehousing and business intelligence, they

don't know what's reasonable for them to ask or expect, and often they change their minds as

they see the system put into action. Investing time in education both up-front and throughout

development will help clarify needs and goals, keeping the developed product useful and

relevant.

Continuously Deliver Working Features

In traditional development models, developers could work for months on a feature, only to find it

no longer applicable to a changing business environment. In Agile analytics, each iteration

should deliver a working feature to be tested by stakeholders and adapted in further iterations to

better suit the organization's needs.

Test Frequently

With so many stakeholders on board, it is crucial to test data warehousing/business intelligence

systems frequently throughout the development process. Integrate continuously and test systems

in pre-production or demo environments at various benchmarks throughout the project so there

are no surprises at the end.

Adapt to Changing Conditions

The core purpose of big data is to find key insights upon which an organization can pivot. In this

way, big data by definition demands agility. Listen to what users, tests and business conditions

are telling you, and work change into subsequent iterations.

Automate as Many Processes as Possible

The greatest manpower should be saved for developing new features and collaborating across

organizational and development teams. As such, it's important to automate as many regular

processes as possible, from testing to administrative tasks so that developers can focus intensely

on an iteration's set goals.

Foster Self-Organized Teams

Hire talented, motivated individuals who can set their own goals for each iteration and function

as effective self-managers. Then, trust them to do the job at hand, self-monitoring and adapting

as they go.

Adapt Agile Methods to Individual Projects and Teams

While Agile analytics has many guidelines, it is a style, not a process. More traditional tactics

aren't antithetical to Agile if they're effective in achieving iterative goals. Choose the tactics that

work best for each project and team rather than adhering to static rules.

Conduct Regular Reviews of Processes

Agile systems development requires just as much discipline and rigor as the traditional waterfall

method in order to stay on track. However, rigor should be applied not to adhering to rigid

systems and static goals, but to constantly re-evaluating the effectiveness of the methods and

styles at hand.

Constantly Learn

Keep up-to-date with the best data warehousing and business intelligence practices and

implement them fluidly into each iterative phase. This will substantially increase the

development team's agility and keep the organization ahead of its competitors.