Data Analysis vs. Analytics
In the customer relationship management class, we spend a good amount of time discussing the differences between data analysis and analytics. Interestingly, you can do a quick Google search on this very topic and get a wide range of perspectives, even from people in technical or educational roles that include these activities.
The following is a look at why analytics is much more sophisticated and specific than the broader concept of data analysis.
What is Data Analysis?
As explained by the Northern Illinois University Responsible Conduct in Data Management website, data analysis is statistical and logical evaluation of data. In general, the goal of data analysis is to infer or interpret similarities or commonalities among the data points included in the assessment.
Traditional spreadsheet tools, including Excel, are used to gather, structure and perform basic statistical analysis.
Predictable Patterns with Analytics
While many people use the term “analytics” to encompass any form of data analysis, the concepts of business intelligence and pattern predicting separate it from other statistical forms of data analysis. Business intelligence is the use of software programs to make more informed and accurate business decisions. A key reason leaders can make such decisions with confidence is the use of analytics reporting tools to detect predictable patterns. You might recall learning patterns in elementary math. For instance, identify the next number in the sequence “1,2,4,__”. The next number is predictably “8”, as each number in the sequence is double the one before it.
Analytics typically gets beyond such simple pattern detection. In the era of “big data”, many companies are collecting millions of values in their databases. Whereas a human can logically evaluate 10 to 20 manual profiles on an Excel spreadsheet, it takes advanced computer solutions to identify trends or patterns from millions of data points. The general goal is to create reports that allow you to detect points of correlation among data points, or predictable patterns that emerge from them.Consider the systems in place through popular online dating sites that attempt to match users with people who closely match their desired partner profiles. Over time, analytics is helpful in detecting the traits, interests and experiences of certain users with those of others. Such evaluation allows sites to more accurately predict successful matches, which should improve successful dating experiences and relationship matches.
Applying Analytics to Business
Analytics is used across a wide range of business systems, including management decision-making and talent management. It is often discussed in the context of a company’s customer relationship management system, which is most engaged by marketing, sales and service functions. Collectively, this business marketing system involves the input or collection of prospect/customer profile data, and systematic/programmatic analysis of the relationship between particular profile characteristics and transactional or behavioral data tracked over time.
Through predictive analytics, marketers can more accurately identify the traits of people in a target market, and then develop precise messages that speak to those people in a very personalized way. Salespeople can use materials developed by marketing and their own improved understanding of buyer profiles to sell more persuasively. In essence, companies can confidently identify the right audiences and messages for each solution rather than relying on hunches, educated guesses or more rudimentary forms of data analysis.
You might consider discussion of the differences between data analysis and analytics a semantics issue, but understanding the varied technological requirements, tools and purposes between statistical or logical evaluation and programmatic predictive analytics is essential in 2016 business marketing.