What does analyzing data entail? While the types of data analysis may differ between companies, at its core, this process uses data to find statistical patterns and insights. In an age where data is the world’s most valuable resource, research through data analysis has become routine across all business functions to uncover everything from the best-performing products to the most effective marketing strategy.
Analyzing data is an integral component of business decision-making, yet up to 73 percent of company data goes unused. Why? For starters, the answer to “what is data analysis?” is a lot simpler than the reality of its execution. To truly answer this question, we must understand that fully analyzing data is a multi-step process that, in most cases, involves varying skills and coordination with key stakeholders. For example, defining the objectives of the analysis project must stem from business users, but data collection, especially in larger organizations, is often controlled by IT organizations. We must also understand that there are different types of data analysis that correspond with the needs and goals of a business. Finally, the outcomes of data analysis are often presented to business stakeholders to act upon. That leads us to another answer to the question “what is data analysis?”, one that is often overlooked: an organization-wide effort.
As discussed, data analysis is often an organization-wide effort, but what step in the data analysis process requires the most collaboration? While that could depend on the type of data analysis, more often, it’s the step where business and IT teams converge: data preparation.
Data preparation is critical—without data that has been cleansed, enriched, and structured to fit the objectives of the analysis, you simply won’t yield accurate results. Too many people regard wrangling as janitorial work; as an unglamorous rite of passage before sitting down to do “real” work. But the truth is that data preparation is as much a part of the data analysis process as the final results. Proper data preparation gives you insights into the nature of your data that then allows you to ask better questions of it, which drives an iterative, ever-improving evaluation of your data.
Historically, data preparation collaboration has looked something like this: a business user asks IT for data requirements. The IT team prepares these requirements (along with their long list of other daily responsibilities). The requirements come back and the business user realizes they could have transformed the data differently or have asked for additional data. And the process begins all over again, causing a lot of friction between the two teams.
However, instead of seeing data preparation as a hindrance to collaboration, today’s organizations have begun to view it as the biggest opportunity for change. By implementing modern data preparation platforms that cater to both business and IT teams, organizations have seen new levels of collaboration efficiency. These platforms allow IT teams to regulate the flow and access of data by business users, while business users can easily prepare the data they need for any given data analysis project. In the process, data preparation—long considered the most time-consuming part of any data analysis project—is reduced by up to 90%.
The reality is that data analysis can come in many forms, depending on the type of data analysis. But in the quest for successful data analysis, organizations should remember two important points: data analysis is often a team effort, and organizations must adopt tools that understand that.
Trifacta is routinely recognized as the leader in data preparation and was specifically designed with both end-users and IT teams in mind. To learn how you can use Trifacta for your data preparation needs, schedule a demo of Trifacta today.