high accessibility--the data science application is meant to release results to any kind of userespecially people who might only have browser at their disposal--your own customersfor instance data visualization in html runs fluently on mobile big pools of talent out there--although there aren' that many tableau developersscads of people have web-development skills when planning projectit' important to take into account whether you can staff it quick release--going through the entire it cycle might take too long at your companyand you want people to enjoy your analysis quickly once your interface is available and being usedit can take all the time they want to industrialize the product prototyping --the better you can show it its purpose and what it should be capable ofthe easier it is for them to build or buy sustainable application that does what you want it to do customizability--although the established software packages are great at what they doan application can never be as customized as when you create it yourself and why wouldn' you do thiscompany policy--this is the biggest oneit' not allowed large companies have it backup teams that allow only certain number of tools to be used so they can keep their supporting role under control you have an experienced team of reporters at your disposal--you' be doing their joband they might come after you with pitchforks your tool does allow enough customization to suit your taste--several of the bigger platforms are browser interfaces with javascript running under the hood tableaubusinessobjects webisas visual analyticsand so on all have html interfacestheir tolerance to customization might grow over time the front end of any application can win the hearts of the crowd all the hard work you put into data preparation and the fancy analytics you applied is only worth as much as you can convey to those who use it now you're on the right track to achieve this on this positive note we'll conclude this summary this focused on the last part of the data science processand our goal was to build data science application where the end user is provided with an interactive dashboard after going through all the steps of the data science processwe're presented with cleanoften compacted or information densedata this way we can query less data and get the insights we want in our examplethe pharmacy stock data is considered thoroughly cleaned and prepared and this should always be the case by the time the information reaches the end user