From Google to BAM and Business Intelligence: Exploring the Spectrum of Web Analytics

Data warehousing began with extraction and transformation of structured data from LOB apps for processing by BI tools. Now, analytics has evolved with departments using content, event, and web analytics.

From Google to BAM and Business Intelligence: Exploring the Spectrum of Web Analytics
From Google to BAM and Business Intelligence: Exploring the Spectrum of Web Analytics

In the early days of data warehousing, data integration tools were used to extract and transform structured data from traditional LOB operational applications, which were then loaded into a data warehouse for processing by BI reporting and analysis tools. However, the landscape of analytics has since evolved, with various departments now deploying analytics, including content analytics, event analytics, and web analytics. The development of collaborative and social computing tools will likely lead to the emergence of collaborative analytics.

Many of the individuals building these analytical solutions may lack knowledge of BI and data warehousing, and may not feel compelled to acquire such knowledge. It is therefore unrealistic for the BI group to assume that they can fully integrate this influx of information into a data warehousing environment. Web analytics provides a clear example of this issue, as it can be developed by the web, applications development, or BI groups.

What is the Purpose of Web Analytics?

According to the Web Analytics Association (WAA), web analytics refers to the process of collecting, measuring, analyzing, and reporting internet data to optimize web usage. The WAA also establishes standard metrics that web analytics products should ideally support, such as page views, visits, unique visitors, new visitors, returning visitors, clickthroughs, and conversions. These metrics are mainly used to identify website visitors, understand their behavior, and measure the success of their visits (e.g., purchasing a product or service).

It is important to note that WAA metrics report after-the-fact summaries of past events and are primarily intended for tactical and strategic decision-making. To perform operational decision-making, such as fraud detection or real-time marketing campaigns, different tools are required. Additionally, identifying visitors can be challenging, as it often requires the use of cookies or customer relationship management tools to gather additional data.

Overall, web analytics provides valuable insights into website usage, visitor behavior, and online performance, which can help businesses optimize their online presence and improve customer engagement.

How do web analytics products function?

Web data can be collected using two primary methods: page tagging and log file analysis. Page tagging involves adding additional code, often written in JavaScript, to a web page to inform a third-party server when the page is rendered by a web browser. On the other hand, log files generated by the web server managing a website can be analyzed to gather data. While some products support network sniffing, it will not be discussed here.

Google Analytics is a prominent example of a product that utilizes page tagging. It is a free software-as-a-service (SaaS) offered by Google, which generates comprehensive visitor metrics for a website, aimed at marketers instead of webmasters. The product is useful for measuring the effectiveness of marketing campaigns utilizing Google's AdWords feature. Even websites with less than 5 million page views per month can use the service, even without an AdWords account. The Google Analytics JavaScript code collects visitor data and sends it back to Google data collection servers. The servers process the data periodically and generate reports that the website owner can access on-demand. Google also provides the fee-based Urchin Software for in-house use. Other SaaS and in-house products that compete with Google Analytics include Coremetrics, Omniture (recently acquired by Adobe) SiteCatalyst, Unica NetInsight, WebTrends Analytics, and Yahoo Web Analytics. CMS Watch offers an excellent report for purchase comparing these and other web analytics products, and their website has a free report appendix documenting how these products support WAA metrics.When purchasing a web analytics product, it is crucial to consider its ability

When purchasing a web analytics product, it is crucial to consider its ability to handle web pages with dynamic content that includes Rich Internet Applications (RIA) created with technologies such as Ajax and Adobe Flash. The capability to track RSS syndication readership and mobile users may also be essential for some organizations. Some vendors, such as SeeWhy, provide specific applications for web marketing. All of the products mentioned above support page tagging, while a few also support log file processing. The comparison table below highlights the differences between the tagging and log file approaches.

Log files can also serve as an ideal data source for a data warehousing environment, as they allow web data to be correlated with other types of enterprise data. Given the volume of data involved, some filtering and consolidation may be necessary before loading the log data into a data warehouse, which can be done using standard data integration tools that support flat files or using technologies like Hadoop MapReduce.

When real-time or near-real-time web analytics are required, there are two alternative approaches available from vendors. One is called business activity monitoring (BAM), which tracks and analyzes business transactions generated by web interactions as they pass through operational systems. BAM is useful for analyzing a continuous stream of business transactions and generating real-time reports and dashboards. For more complex processing of transaction and event streams, products that support complex event processing (CEP) can be used. CEP solutions can analyze and correlate multiple streams of current and historical data, identify patterns and trends, and predict potential outcomes. Examples of vendors in this area include Aleri, IBM (WebSphere Business Events, InfoSphere Streams), Oracle (Oracle CEP), Tibco (BusinessEvents), and Truviso. Note that some vendors use the term business event processing (BEP) instead of CEP, while others use terms such as continuous intelligence and continuous analytics.

To sum up, in today's fast-paced and ever-changing business environment, organizations need to monitor and analyze their web performance just as they would in a traditional business setting. Without reliable performance data, optimizing web operations and staying competitive is a challenging task. Various approaches and tools are available for producing web analytics, ranging from SaaS tools like Google Analytics to BAM, CEP, and data warehousing and BI offerings provided by enterprise software vendors. While SaaS solutions provide targeted web marketing solutions, enterprise solutions enable the merging of web data with other corporate data to provide a comprehensive view of web processing from an enterprise perspective.