Intentionality and Consistency are the most part. Six months ago, I started my tech journey as a data analyst. One thing stood out for me: setting my goals so high that I had to doubt if they would be accomplished because that's the only way I keep growing my knowledge and believing in God for more significant miracles. I also remind myself that if I try to stay the same without challenging the status quo or try to shy away from discussing it, other people worldwide have already started inventing more and more sophisticated solutions than ever existed to improve the quality of life. In turn, I was prompted to accept that they are already questioning the odds and shaking off the status - therefore, I should do the same.
My enthusiasm for analytics led me to research the processes used to examine, clean, transform, and model data to extract useful information, draw conclusions, and support intelligent decision-making. I started off by training my aptitude and attending Boot camps which helped me to learn various techniques and tools for analyzing and visualizing data the best way, such as statistical analysis, machine learning, and data visualization. My goal was to gain proficient knowledge on how to turn raw data into actionable insights that can be used to improve business operations, inform strategic decision-making, and drive business growth.
Essentially, data analytics is a multi-disciplinary field that encompasses a wide range of practicalities, critical thinking, methods, techniques, and analytical skills to extract insights from data. The range of usage cut across various industries such as finance, government, healthcare, retail, technology, agriculture, climate, and personal handling to list but few. There are typically six processes of data analytics: Ask, Prepare, Process, Analyze, Share, and Act.
Just a quick one; the first step is to ask the right questions by defining the problem, the purpose of the analysis, and what we hope to accomplish. The second step is to prepare the data, this involves brainstorming what type of data to use an example; qualitative and quantitative data. Also, there is a need to think about how to collect the data or if the data already exists in the client's database. It is important to note that data preparation is one of the major aspects of data analytics because the data should be able to answer business questions and deliver desired insights. It also entails acquiring data from various but reliable sources, such as business databases, spreadsheets, online databases, and web scraping. The third step is to process data, this entails cleaning the collected data. To me, it is the most fun and important part where you get to run all the quality assurance of your data by understanding the structure to ensure it is in the right format that can be analyzed. The fourth step is to analyze which also means exploring, and visualizing data in order to identify patterns and trends that may not be immediately obvious. Needful to mention that this step requires the analyst to be as fair, objective, and unbiased as possible. Once the data is prepared, it goes through the process of exploration and visualization. This step involves using statistical analysis, machine learning, and data visualization tools. These advanced techniques help to identify relationships and patterns in the data that may not be immediately obvious. Visualizing data helps to communicate insights effectively to the stakeholders. The fifth step is to share the high-level findings with the executive team and stakeholders. The aim is to let them have a landscape view of how the organization is progressing or declining at their fingertips, and we want to make sure that there aren't any surprises as they dig deeper and deeper into the data to understand how the business has turned out over a given period of time. The final step is act on the findings by making strategic decisions and taking appropriate actions that drive business strategies and growth. To me this is the most critical part, acting on the result is where the real work begins.
The process of analyzing data is a rigorous one and takes a lot of time to get the challenge fixed but I completely get so excited about just diving right into raw data and doing what I do best. The challenge is in determining not to skip steps, or else I am not going to elicit all the insights I look for in the data. I absolutely appreciate data and deeply admire analytics which is the powerful tool that helps organizations make informed decisions, improve operations, and drive growth which is an essential part of today’s data-driven business world.
Top comments (0)