How do you make data useful for everyone?

Data Collection will be first step, Data cleaning will eliminate errors, duplicate and inconsistencies to improve quality. Data Storage allows structured, easily accessible format. Data Security, protect sensitive data. Data Accessibility, make data available to a wide audience. Visualization, create charts, graphs and dashboard to present. Data Analysis, with help of machine learning, statistical analysis we will get the insights. Data Sharing, data openly where appropriate, fostering collaboration & innovation. Data Education, promote data literacy & provide training to help people understand and use data effectively. Ethical Considerations, Feedback Loop, Customization, Transparency, Scalability and Regular updates major scopes.

After collecting the data, the right data format will be excel and csv. If the data is in huge then JSON, XML & SQL (Database) would be best way to have right format. So that user can be access those data easily.

Data classification should be the first step for security, not all the data requires same level of protection. Access Control, Data Encryption, Data Masking & Anonymization, Secure Storage, Data Governance will allows user to secure the data. Data Minimization, Privacy by design, Data Auditing & Logging, Data breach response plan, Compliance with regulations - will allow user to maintain the privacy. With the employee training, Vendor Assessment, Continuous monitoring and improvement will helps us in data security and privacy measures.

For Data Analysis, Define Objectives clearly, collect the data, clean and Exploratory Data Analysis (EDA), followed by testing as Hypothesis Testing, Creating new features of Engineering, Data Modeling & Evaluation and finally, Iterate and optimize will fine tune models and adjust parameters. Visualization, select the right tools & types, design clear and informative visuals. The visualization should be interactive and storytelling. Post feedback & collaboration, Data privacy & ethics, presentation, Iterate and refine, will continuously improve your visualizations

Share data, identify stakesholders, Data sharing platforms, data documentation and Data access control will be the initial steps to cover. Post that Visualization and Reports - will help us to imporve the present. Feedback mechanisms, Presentations & meetings, ask specific questions will allow user to get mor insights about the data. Finally, Feedback response, Continuous Engagement, Transparency, follow-up assessments and Data governance, will organiszations data governance policies.

When trying to make data useful, the most essential part is data cleansing. It can take up to almost 70% of your time, but if you skip that step, nothing after that will be correct. Take into consideration that cleansing data is not a linear process. You will likely need to come back to your dataset a few times before it is completely clear and complete. Part of that process also includes knowing how to categorize the information you′re working with. This means understanding which data is essential, necessary, easily understandable, and which data simply provides a wide range of examples to support the analysis. This will help you know in which order to correct, clean, and present the information.

I have spent years collecting, formatting, cleaning, and presenting data to marketing departments. It's mainly BI teams that collect and present the data. But BI teams often believe marketing is beyond their responsibilities since they should analyze existing customers, not prospective ones. Even when BI is willing to assist marketing, data projects usually take 3 to 4 months to complete. By then APIs have been updated, new advertising channels have been added, or new managers have been appointed, and the project is no longer relevant. As a result, the project gets stuck in data project wilderness and everyone is left frustrated. Hiring specialized consultants who understand marketing teams' needs is the only way to solve this problem.