What are the best data analysis tools for your problem?

1. Data cleaning and preparing the data is crucial to ensure the accuracy and reliability before any analysis. Determining the credibility is vital to ensure not only data trust within an organization, but the ability to make accurately informed decisions. 2. Data ethics and data privacy are important to keep top of mind when collecting data and using it. It’s important to adhere to ethical considerations while protecting personal user information.

One aspect of data analysis is the time it takes to translate data into a programmable format and then to convert it to a bunch of visuals. For this I loved Alteryx, Power BI and Tableau. There are more such less coding intense softwares that can be helpful for your tasks and are worth trying. The libraries that come with these softwares are growing as well which makes it more robust. That being said Python and R-programming are always going to be ‘data scientist first’ tools in my opinion.

When evaluating the results its important to remember that the business needs it to be understandable by non-quants (non-analytics people) and it must provide actionable insights. Couple this with effective design elements and the business case will write itself. Making the data understandable is as simple as writing in lamens terms what you found - there's no need to go into the various cleansing, normalising and analysing techniques, that can be saved for an appendix or supplementary information. Following this, be sure to bring in some potential solutions or actions that the business can undertake and back it up with research articles that support your claim. Lastly, colour is key - keep everything muted except your key finding!

Ensure to show actionable insights from the analysis and the results. Also show verifiable predictions based on the analyzed data.

I think it’s also important to identify any limitations within the data. This provides opportunities to integrate other data sources to not only fill in any gaps where data is missing but to get a more holistic view.

There is a saying that “If you torture the data long enough , it will confess “ - Simply not true in many cases. We often end up not having relevant datasets to address particular business questions. Data investigation and preliminary analysis should be done so thoroughly and business objective should be so clear that you can be confident enough to convey the stakeholders, management and C-suite leaders that the answer they are looking for will need more or different type of data and why.