This article is part of the series for creating interactive dashboards. If you have not checked the post for Interactive Dashboards – why not do in on your own? , I suggest that you start from there. Every analytical process starts with asking questions. After we have gone through the basic analytics questions, we should move on to the topic-specific ones. Dashboards take more time to prepare than the average static report and the execution required varies depending on what t

Any analysis or report begins with getting the data which you need into the tool, you intend to use. In general, this is a one time exercise: analyses are repeated infrequently and usually need to be completely re-done based on new information and findings. In case data mining models require automation it is for the scoring part and not for the development. Some reports are one-off data delivery. In all those cases, the data extraction mechanism is not important, as any autom

Dashboards are all the rage right now when it comes to Business Intelligence. Nobody wants reports anymore, everyone needs Dashboards. Companies left and right, offer dedicated dashboard solutions, promising to increase your business productivity and insights. While I am not claiming this is wrong, let’s first understand what this stands for. A Dashboard is: “A graphical summary of various pieces of important information typically used to give an overview of a business“. So

… and this is just the “marketing” subset of terms. If we deep dive further into the technical details, you will hear about classification, clustering, neural nets, supervised and unsupervised learning and so much more.  While each of those bears some form of distinction from the others, in the end, I would argue that they all boil down to one thing – discovering patterns in large data sets.  This is done by using a set of data analysis techniques, which at their very core ar

I cannot speak for all industries as a whole but in Finance when presenting a report or results of a study or anything really you need to make sure that: a) your data is correct and findings are relevant b) the presentation is clear and as good looking as possible. Of course, having the right results is the most important thing, but the final success of your presentation is truly shared 50/50 between the data and the way you chose to display it. I need to stress again, that w

A problem occurs in a few scenarios. If you try to do your data analysis in your data extraction tool. For example in SQL. Likewise, if you try to dump raw data in your analysis tool before aggregation. In this article, we will go over the basic formats for reporting and analysis. We will do this by answering a simple question from our sample dataset. “What is the company profit over time and region?“ Main dataset components There are a lot of ways to classify data. However,

A good analyst delivers useful insights. While a great analyst will first ask all the right questions. One of the frustrations in analytics is wasting a lot of time on analysis just to redo it all over again. Tell me if this sounds familiar: “I like it! But I needed something else.” “That’s interesting! But we needed this data two days ago.” “I know I asked for this. However, it doesn’t really solve my problem.” This usually occurs when someone needs urgent results and no one