How does a detective solve a case? She pulls together all the evidence she has, all the data that\u2019s available to her, and she looks for clues and patterns.<\/p>\r\n\r\n\r\n\r\n
At the same time, she takes a good hard look at individual pieces of evidence. What supports her hypothesis? What bucks the trend? Which factors work against her narrative? What questions does she still need to answer… and what does she need to do next in order to answer them?<\/p>\r\n\r\n\r\n\r\n
Then, adding to the mix her wealth of experience and ingrained intuition, she builds a picture of what really took place – and perhaps even predicts what might happen next.<\/p>\r\n\r\n\r\n\r\n
But that\u2019s not the end of the story. We don\u2019t simply take the detective\u2019s word for it that she\u2019s solved the crime. We take her findings to a court and make her prove it.<\/p>\r\n\r\n\r\n\r\n
In a nutshell, that\u2019s the difference between Exploratory and Confirmatory Analysis.<\/strong><\/p>\r\n\r\n\r\n\r\n Data analysis is a broad church, and managing this process successfully involves several rounds of testing, experimenting, hypothesizing, checking, and interrogating both your data and approach.<\/p>\r\n\r\n\r\n\r\n Putting your case together, and then ripping apart what you think you\u2019re certain about to challenge your own assumptions, are both crucial to Business Intelligence.<\/p>\r\n\r\n\r\n\r\n Before you can do either of these things, however, you have to be sure that you can tell them apart.<\/p>\r\n\r\n\r\n\r\n Exploratory data analysis (EDA) is the first part of your data analysis process. There are several important things to do at this stage, but it boils down to this: figuring out what to make of the data, establishing the questions you want to ask and how you\u2019re going to frame them, and coming up with the best way to present and manipulate the data you have to draw out those important insights.<\/p>\r\n\r\n\r\n\r\n That\u2019s what it is, but how does it work?<\/strong><\/p>\r\n\r\n\r\n\r\n As the name suggests, you\u2019re exploring<\/i> – looking for clues. You\u2019re teasing out trends and patterns, as well as deviations from the model, outliers, and unexpected results, using quantitative and visual methods. What you find out now will help you decide the questions to ask, the research areas to explore and, generally, the next steps to take.<\/p>\r\n\r\n\r\n\r\n Exploratory Data Analysis involves things like: establishing the data\u2019s underlying structure, identifying mistakes and missing data, establishing the key variables, spotting anomalies, checking assumptions and testing hypotheses in relation to a specific model, estimating parameters, establishing confidence intervals and margins of error, and figuring out a \u201cparsimonious model\u201d – i.e. one that you can use to explain the data with the fewest possible predictor variables.<\/p>\r\n\r\n\r\n\r\n In this way, your Exploratory Data Analysis is your detective work. To make it stick, though, you need Confirmatory Data Analysis.<\/p>\r\n\r\n\r\n\r\n Confirmatory Data Analysis is the part where you evaluate your evidence using traditional statistical tools such as significance, inference, and confidence.<\/p>\r\n\r\n\r\n\r\n At this point, you\u2019re really challenging your assumptions. A big part of confirmatory data analysis is quantifying things like the extent any deviation from the model you\u2019ve built could have happened by chance, and at what point you need to start questioning your model.<\/p>\r\n\r\n\r\n\r\n Confirmatory Data Analysis involves things like: testing hypotheses, producing estimates with a specified level of precision, regression analysis, and variance analysis. In reality, exploratory and confirmatory data analysis aren\u2019t performed one after another, but continually intertwine to help you create the best possible model for analysis.<\/p>\r\n\r\n\r\n\r\n Let\u2019s take an example of how this might look in practice.<\/p>\r\n\r\n\r\n\r\n Imagine that in recent months, you\u2019d seen a surge in the number of users canceling their product subscription. You want to find out why this is, so that you can tackle the underlying cause and reverse the trend.<\/p>\r\n\r\n\r\n\r\n\r\n\r\n This would begin as exploratory data analysis. You\u2019d take all of the data you have on the defectors, as well as on happy customers of your product, and start to sift through looking for clues. After plenty of time spent manipulating the data and looking at it from different angles, you notice that the vast majority of people that defected had signed up during the same month.<\/p>\r\n\r\n\r\n\r\n On closer investigation, you find out that during the month in question, your marketing team was shifting to a new customer management system and as a result, introductory documentation that you usually send to new customers wasn\u2019t always going through. This would have helped to troubleshoot many teething problems that new users face.<\/p>\r\n\r\n\r\n\r\n Now you have a hypothesis: people are defecting because they didn\u2019t get the welcome pack (and the easy solution is to make sure they always get a welcome pack!).<\/p>\r\n\r\n\r\n\r\n But first, you need to be sure that you were right about this cause. Based on your Exploratory Data Analysis, you now build a new predictive model that allows you to compare defection rates between those that received the welcome pack and those that did not. This is rooted in Confirmatory Data Analysis.<\/p>\r\n\r\n\r\n\r\n The results show a broad correlation between the two. Bingo! You have your answer.<\/p>\r\n\r\n\r\n\r\n Getting a feel for the data is one thing, but what about when you\u2019re dealing with enormous data pools?<\/p>\r\n\r\n\r\n\r\n After all, there are already so many different ways you can approach Exploratory Data Analysis, by transforming it through nonlinear operators, projecting it into a difference subspace and examining your resulting distribution, or slicing and dicing it along different combinations of dimensions\u2026 add sprawling amounts of data into the mix and suddenly the whole \u201cplaying detective\u201d element feels a lot more daunting.<\/p>\r\n\r\n\r\n\r\n The important thing is to ensure that you have the right tech stack in place to cope with this, and to make sure you have access to the data you need in real time.<\/p>\r\n\r\n\r\n\r\n Two of the best statistical programming packages available for conducting Exploratory Data Analysis are R and S-Plus; R is particularly powerful and easily integrated with many BI platforms. That\u2019s the first thing to consider.<\/p>\r\n\r\n\r\n\r\n The next step is ensuring that your BI platform has a comprehensive set of data connectors, that – crucially – allow data to flow in both directions. This means that you can keep importing Exploratory Data Analysis and models from, for example, R to visualize and interrogate results – and also send data back from your BI solution to automatically update your model and results as new information flows into R.<\/p>\r\n\r\n\r\n\r\n In this way, you not only strengthen your Exploratory Data Analysis, you incorporate Confirmatory Data Analysis, too – covering all your bases of collecting, presenting and testing your evidence to help reach a genuinely insightful conclusion.<\/p>\r\n\r\n\r\n\r\n Your honor, we rest our case.<\/p>\r\n","protected":false},"excerpt":{"rendered":" How does a detective solve a case? She pulls together all the evidence she has, all the data that\u2019s available to her, and she looks for clues and patterns. At the same time, she takes a good hard look at…<\/p>\n","protected":false},"author":4,"featured_media":8329,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"_acf_changed":false,"_searchwp_excluded":"","footnotes":"","_links_to":"","_links_to_target":""},"categories":[50,44],"tags":[469,479,552],"application":[46],"buyer-role":[56,47],"buyer-stage":[87,109,62],"department":[],"industry":[],"topic":[63],"class_list":["post-7179","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-bi-best-practices","category-tech-talk","tag-big-data","tag-data-analysis","tag-exploratory-data-analysis","application-bi-analytics-teams","buyer-role-bi-analyst","buyer-role-it-rd","buyer-stage-awareness","buyer-stage-evaluation-decision","buyer-stage-research-consideration","topic-hack-your-analyst-skillset"],"acf":[],"yoast_head":"\nWhat is Exploratory data analysis?<\/h2>\r\n\r\n\r\n\r\n
What is Confirmatory Data Analysis?<\/h2>\r\n\r\n\r\n\r\n
In this way, your confirmatory data analysis is where you put your findings and arguments to trial.<\/p>\r\n\r\n\r\n\r\nUses of confirmatory and exploratory data analysis<\/h2>\r\n\r\n\r\n\r\n
Exploratory data analysis and Big Data<\/h2>\r\n\r\n\r\n\r\n