In today's tough economic times, more companies are looking for ways to do more with less. As economic growth slows competition grows more fierce. Instead of battling over the share of a growing market, companies struggle to maintain their client base. Competition becomes a zero-sum game; your competitors success likely means you lose clients.
In this environment businesses often stand pat. They eliminate or delay projects in order to cut costs and wait out the storm. Ironically this is what makes it so important to keep your eye on the prize during a recession. This is true as true for web projects as any other. Since so many businesses are cutting web projects, it makes web development services easier to find and less expensive.
I recently read Competing on Analytics: The New Science of Winning by Thomas Davenport and Jeanne Harris. Davenport and Harris argue that analytics make businesses more successful. Therefore, they believe, analytics will be a central part of future business competition.
The book is divided into two parts. Part one defines analytical competition, and argues that “analytical competitors” are more successful than their non-analytical rivals. Part two provides a primer for building analytical capability.
Competing on Analytics is a good book for anyone interested in learning about analytical approaches to decision making. It defines analytics, explains why analytics are gaining traction in the business world, and examines why analytics are important to business success.
Summary of Part I: Then Nature of Analytical Competition
In Competing on Analytics, the authors define analytics as "the extensive use of data, statistical and quantitative analysis, explanatory and predictive models, and fact-based management to drive decisions and actions." Analytical competitors are those businesses that use analytics most extensively.
Part one of the book explores the link between analytical competition and business performance. The authors argue that analytical competitors have an advantage over their non-analytical rivals. They use case studies, existing research, and the results of their own surveys to support their position.
Their argument is persuasive, though they do not adequately discuss the conditions under which analytics leads to better performance. Are analytics equally valuable for businesses of all sizes? For all types of decisions? The book does not address these questions.
What is Missing from Competing on Analytics
The book does a great job of introducing the reader to the benefits of analytics. But it does not help us understand when using analytics is most appropriate.
The authors' definition of analytics emphasizes quantitative and statistical analysis. There are two problems with this approach. First, not all useful evidence is quantitative in nature. Second, not all analytical reasoning is based on data or evidence. Yet these other forms of analysis are also valuable.
Non-Quantitative Evidence
Best practices are an example of non-quantitative evidence. For example, many websites use analytics to improve the user's experience. However before the website is built usage statistics are not available. Web developers initially rely on principles of good web and user interface design. Web writers similarly rely on established principles for good writing, such as those outlined in the classic Elements of Style.
Does Analytics Really Improve Business Performance?
The book's main argument is that analytics improve business performance. The authors reason that decisions based on evidence are more likely to be correct than those based on intuition. Since analytics provide evidence to decision makers, analytics help businesses make better decision and out perform their rivals.
We have seen a couple problems with this reasoning. First, the choice between evidence-based decision making and intuition-based decision making is a false one. Second, the extra benefit provided by analytics may not justify the extra cost associated with building analytical capability.
However, the authors make a strong case that analytical capability does boost performance. They offer three types of evidence to support their argument. Existing research on ROI of business intelligence projects, their own survey research, and the case studies discussed in the book. Together the evidence is compelling.
Poor people and minorities have always been less likely than average to use the web and less likely to have internet access at home. However, there is also a small business digital divide.
Smaller businesses with fewer resources generally make lighter user of information technologies. The same is true for minority-owned businesses. For example, HispanicBusiness.com reports that Hispanic-owned businesses are less likely than average to use the internet; information technology in general; have a website; or have an ecommerce strategy.
Legacy of the Digital Divide
While the digital divide has not disappeared, it has narrowed in the last several years. However, many of today's minority business owners came of age when the digital divide was much deeper than it is today. Consequently, they are not as familiar with the technology as their white counterparts. The Tomás Rivera Policy Institute, which studied the issue for IBM, says this lack of familiarity results in lower levels of technology use by minority-owned businesses.
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