Analytics Data Driven Testing
There is saying among business insiders that “If your data is not up to the minute, then it is not up to the job.” The data that you receive from Google BI tools and other sources comprise your business intelligence and help you make vital business decisions such as your market share, target audience, adjustments that need to be made to your product or service and much more.
Now let’s take a closer look at how to properly use BI testing, how to introduce and apply the results obtained to make smarter decisions.
Introduction to Business Intelligence
Business intelligence is the process of assembling unfiltered data, analyzing it and sorting everything out to produce thoughtful insights which you can use to make proper business decisions and sound business strategy. Everybody wants to plan ahead in order foresee market changes and make adjustments to their product accordingly, but these future plans need to be based on something much more than a crystal ball. This is where the use of business intelligence comes in to provide clear, hard facts.
The way business is conducted has shifted significantly over the past couple of years. For example, the reports that used to be offline are now live business integration.
BI in Testing
Once you obtained data raw from Google Analytics or any other tool, you must validate this data. It is very important to do due diligence when BI Testing is performed to increase the quality of the report itself as well as adoption by the user. Keep in mind that testing BI projects vary from web application testing we are used to seeing because the reports will be automatically produced by the business intelligence tool based on metadata. The BI testing process follows a methodical order:
- The Data Quality Assurance Process – Different sources and data formats exist for the business data, therefore you must make sure that the source data matches the data that is being sent. This is basically the extract stage, transform and load known as ETL.
- Testing Data Transformation – At this stage, the raw data is transformed into pinpoint usable business information.
- Load Data Testing – Where will the data be stored: in a data mart or data warehouse? Here you must conduct experiments to form a decision. You should also experiment with the system where the information will be stored for performance, because as the system grows complexity increase as well, so be sure to check for performance issues. Keep in mind, that as the number of data increases, the system you are using must be scalable to handle the volume of the data.
- Testing BI Report – When experimenting with business intelligence information, it is necessary to affirm that the report itself is applicable to business. The information contained needs to be customized, sorted and grouped to improve readability.
As the IT industry becomes increasingly complex, QA testing can be the decisive factor needed to make long-term and smart decisions. The quality of a BI solution depends on the quality of the data put in and the value of the delivered results. Proper testing can confirm that the data is credible via a comprehensive testing strategy which encompasses test planning, infrastructure, and QA teams, developers and business users.
Implementing BI with Web Analytics
When working with information obtained from Google Analytics or another similar platform your website’s performance is put in perspective of different variables. Web analytics and business intelligence both assist companies in perfecting decision making based on hard facts, but it better to distinguish them by which one is based on data-driven analytics upon review. Web analytics looks at a website’s data and aims to enhance your overall internet presence i.e boosting the total web traffic i.e. the number of visitors, total visits and page views.
Also, web analytics can optimize your site’s performance since there is no point in having a website with a lot of traffic if it does not lead to increased revenue growth. For optimal benefit, it is important to verify it will lead to more customers by increasing lead generation. Do not forget to confirm that the calls to action (CTAs) are effective can be a good route to take. Web data analytics can provide data on conversion rates, which you can optimize CTAs, opening the door for growth in overall leads.
Even though web analytics shines a spotlight on website improvements, business analytics can enhance your entire business. The sky is the limit with business intelligence. With Big Data, you can delve into all necessary business processes that are related to custom software development or your client’s, workers etc.
Business intelligence and Big Data work in a similar way because you can foresee future results and performance. Forecast models and reports can be created from your data, which can show you invaluable insights. They can be used for finding trends and the predictive models can guide in preparing for the future.
Also, BI requires more due diligence and better analysis, which involves techniques such as statistical and quantitative analysis. The tools shine a spotlight into why a certain phenomenon is happening. For example, there could be a connection between extended days off by workers and an increase in late shipments. The results can be used to introduce changes and use A/B testing if needed. Remember that, predictive modeling can predict future results which can be used for preparing in advance.
Since online projects are getting complex every day, can we anticipate web analytics to morph into business intelligence at some point? While it may be a tad premature to pretend to have the answer, however, there is no need to turn a practical, quick, and simple and powerful solution into a business intelligence tool, which is what many people want in a majority of the cases. Such a world is constantly advancing, but only where it absolutely necessary, to new features, and a world which requires reactivity.