12 Best Practices For Data Analytics Project! 📊💡

Key Takeaways

in today's video we are going to talk about 12 best practices that you can use to successfully execute any data analytics project we will divide these 12 practices into three categories pre-project during project and post implementation these are created by Haman and vadivel who is the co-founder of this YouTube channel when he was working as a data analytics manager in Europe he used this list for more than 30 plus Enterprise level projects so if you're a data analyst aspirant or working as a data analyst in the industry you will find this video to be very useful before you begin the technical work for the project you need to make sure four things are ensured number one is having a scoping meeting with business stakeholders let me give you an example of constructing a home let's say you want to construct a home you will call a home builder now you will give all the requirements how many bedrooms I want upstairs versus downstairs what kind of tiles do you want to use what kind of flow do I want to use similarly in data analytics project you have to tell your required environments and as a data analyst you have to ask these questions to your business user and get that list of requirements ready Point number two will be creating a clear list of requirements and aligning with your business users you can use Excel and list down all individual features and then you can put your own comments as a data analyst you can also have one more column where you can put the priority of each of these features this is going to be a collaborative afford so you will have communication with business user you will create this clear list of requirements and once this is created the third best practice would be to create a mock-up going back to home example once you have conversation with your home builder what they are going to do is create a map or maybe 3D drawing of how your home is going to look like and when you look at that map or a 3D drawing you will have more questions or you will maybe change your requirements because once you see something then you have more ideas or more feedback same thing happens in data analytics project let's say you're building a dashboard you need to build a mock-up either using Norton pen or PowerPoint or there are many mock-up tools out there on the internet you use that create a mock-up show it to your business users so they get like a preview of how that is going to look like and then they will give a feedback or they will give confirmation that okay this thing is final let's start the work on it the fourth best practice is do not comment on the deadline unless you have checked the data quality once again for our home building example if you are a builder and if you promise to a homeowner that your home will be ready in six months but then your bricks and all the raw material has not arrived yet when the raw material arrives let's say your tires and floor arrive you find that some of the pieces are broken okay and they are not as per the expectation in that case the Home Project will get delayed similarly for any data project the raw material is good quality data you need to ensure the data quality is good by writing some scripts or by doing quick validation in bi2 such as power bi and also you need to make sure all the data that is needed for the project is available most of the time you're pulling data from data warehouse and if that data is not available or is in bad quality you need to have a conversation with data engineers and business stakeholders to make sure data is available and also is in good quality only then you can decide the deadline for the project once the project is started you need to follow another four best practices number one is prepare uat document by working with your business users uat stands for user acceptance testing here I am showing you one example where after releasing a dashboard you have created a clear list of things that you are going to test and as a DA team or data analytics team you will test those things and if it looks good you will say pass then you will give that to your business users they will also test it and if they feel it is working as per expectation they will say pass now here you can see Point number six where we are saying that is this tool easy to use by your business users and three out of five sales manager who tested this they found that no it is not easy to use in that case that test will fail and the data analytics dashboard will come back to the DA team and the team will work on improving the usability or ease of use of that dashboard so this is similar to a test document which is you know prepared by QA team in software development world this is exactly same okay it's like a list of test cases and all you are doing is saying pass fail pass fill okay so first as a data analytics team you do the testing then you give it to business user to test it out second point to keep in mind is avoiding feature going back to home example let's say your home builder has started building the home now now as a homeowner you go to him and you say okay can you build this patio for me can you build this balcony or can you build this Porsche for me something that you did not decided during your scoping meeting and now what's gonna happen is the home builder can do that but the cost is going to increase and the deadline will extend so you want to make sure that your business user is not coming up with all kind of crazy ideas and you have to just keep on changing your data analytics dashboard so avoiding feature grip requires regular meetings with business users and that's Point number three those regular meetings are going to ensure that you are on the right track and the business users expectations are being met the fourth point is releasing a minimum viable product also called MVP to set of users you will usually be working closely with one business stakeholders and then you will be getting that weekly or bi-weekly feedback but at some point you want to release the first version of your project which we call MVP too little more users so let's say there could be five business users those score can be five sales managers or marketing managers and you release that dashboard to those five folks so that they can test it and give you the feedback so releasing minimum viable product to set of business users is going to help you a lot in your project once the project is implemented and let's say you release the dashboard to set of business users you need to make sure four things number one is you need to conduct a demo or training demo is important because if you just give that dashboard to your business user they might not know how to use it so you will conduct a meeting you will show them how to use this which button to click what is the meaning of different visuals or abbreviation and so on for our home example it is like once your home is prepared you know they do the tour of the home and the Builder will explain various things various systems how to operate different equipments as cetera so this is similar to that and once the demo and training is conducted you need to frequently schedule regroups with your users to make sure the user adoption because the business users are such that if they find data quality issues with your tool or if they find that the tool is not very easy to use they will stop using it and they will not tell you that we have we are not using your tool therefore as a data analytics team it is your responsibility you go to your business users and you make sure that you are conducting these regroups to ensure the adoption and as part of that the third point you need to remember is taking incremental feedback and creating a pipeline for next list of features for phase two so let's say you start living in your home and you find that there is a water leaking or less a cold air is coming because the window is not sealed properly these are various issues so there could be either issues or there could be enhancement requests let's say you want to to change things in a certain way in your home in that case you will create a list of features or list of bugs that you want your home builder to fix and then you will talk to your home builder and they will prioritize it's like okay which one do you want to tackle first similarly in data analytics project once business user starts using your tool they will give you feedback and then you will list down all the features it could be bugs or it could be enhancement requests and you will create a pipeline and you will prioritize that for phase two three and so on and the last one which is super important is to create a contingency plan to avoid a critical impact let's say your business users have started using your tool and they see some wrong data but based on that they are making some critical business decision that can have a revenue impact now you have to make sure that if that kind of situation arises what is your risk mitigation strategy this is something you can work out with your business users you can create all the steps and processes and make sure that in case of things going bad you're following that contingency plan to minimize the risk that's it folks these are the 12 steps that you can follow to make sure your data analytics project gets executed smoothly and efficiently in the video description below I have given a LinkedIn post which Hamilton did on this topic so you can download these steps from that and whenever you are working on your next data analytics project try to follow these and let me know in the comments how did you find these steps if you like this video you can give it thumbs up or maybe subscribe to our Channel because we also like growing in terms of our subscriber count if you have friends who are working on data analytics project they might also find this video useful so please share it with them thank you for watching thank you