Data analysis is a crucial skill for administrative managers who need to make informed decisions, monitor progress, and communicate results. However, analyzing data can be challenging, especially if you have a large, complex, or diverse dataset. How can you ensure that you are using the best methods and tools to analyze data for your project? Here are some tips to help you get started.
Before you dive into the data, you need to have a clear idea of what you want to achieve and what you want to learn from the data. What are the objectives of your project? What are the key performance indicators (KPIs) that you need to measure? What are the hypotheses or assumptions that you want to test or validate? What are the specific questions that you want to answer with the data? Having a well-defined goal and a set of questions will help you narrow down the scope of your analysis, select the relevant data sources, and choose the appropriate techniques.
A good way to get started is simply by asking the question: "What is the number one problem our business needs to solve?" This allows you to cut through the noise and hone in on brainstorming hypotheses and tests that can make the most impact to the business.
This is the most important step in the process of any BI-type project. I have stakeholders approach me all the time asking for vague, generalized reporting to "give insight into XYZ." That type of analysis is frequently in a category I call "fascinating, but not actionable." These days, in my discovery meetings, I push very hard to get clear, precise business questions written down. That's what we're trying to answer. If you can't articulate the question you're trying to answer, then the dashboard/report/tool that gets built is probably doomed to endless revisions as the stakeholder comes up with more fascinating, but not actionable metrics they'd like to add to it.
I've fallen victim to this in the past, so I want to share so others can learn from my experience. While you are defining your goals and questions it is also important to make sure you have the right size group of stakeholders involved because sometimes, if the group is too large it can result in clashing objectives for the project. It is possible to have a group too small as well where the focus is narrow with blindspots.
I guess that starting with this step, you are assuming that your data have all the variables required to give any responses you want, or that you live in a perfect world where everything is provided. Previous or intern step is to ensure that you are collecting the data that you will need to suggest the questions you want to solve. Some times that is not happening, so you will have to consider how to implement that collection to give that responses in the future. So my previous step before defining goals.. is to know the resources you have at the moment. That is also a good step to say "we could respond all this in the future if we start collecting these variables" and dont waste time with a second meeting.
Our questions MUST be as specific as possible from the get go. As a scientist working with lab generated data, we risk mining false insight by posing vague questions. Ask yourself: 1. Is this data qualified to answer my questions? 2. What final insight do i need from this data? 3. Which tool is applicable? 4. Last but not least, how do this new found information fit communal knowledge?
Depending on the type, size, and format of your data, you may need different tools and techniques to analyze it. For example, if you have quantitative data, such as numbers, percentages, or ratios, you may use spreadsheet software, such as Excel or Google Sheets, to perform calculations, create charts, or apply formulas. If you have qualitative data, such as text, images, or audio, you may use software, such as NVivo or Atlas.ti, to code, categorize, or visualize the data. You may also use specialized software, such as SPSS or R, to perform statistical analysis, such as descriptive, inferential, or predictive analysis. The choice of tools and techniques depends on your goals, questions, and data characteristics.
Choosing the right tools is a crucial step and in my experience learning something like excel pivots completely changed the game for me. I have found that increasing my skills in excel has been super beneficial to my daily work. The best thing is that LinkedIn Learning has multiple courses to teach you things you may not have known you could do. Like when I learned the paste format button.
It's good to keep in mind that the choice of tools will also depend on the technologies available to your team, and the skills and preferences of colleagues you collaborate with.
Before you can analyze your data, you need to make sure that it is accurate, complete, and consistent. This means that you need to check your data for errors, such as missing values, outliers, duplicates, or typos, and correct them accordingly. You may also need to transform your data, such as converting units, standardizing formats, or aggregating values, to make it more suitable for analysis. You may use the same tools that you use for analysis, such as spreadsheet or statistical software, to clean and prepare your data.
It's extremely important that you address any missing data in your datasets. Missing data can cause bias in your model and reduce the power of statistical methods. Therefore handling missing data is a crucial preprocessing step towards obtaining valid, meaningful insights.
Here it is important to avoid manipulating the raw data. Keep the raw data file intact, and store the cleaned data in a different file.
Once your data is ready, you can start exploring and visualizing it to gain insights and identify patterns or trends. You can use various methods, such as summary statistics, frequency tables, cross-tabulations, or correlation analysis, to describe and compare your data. You can also use various types of charts, graphs, maps, or dashboards, to display and communicate your data. You can use the tools that you use for analysis, such as spreadsheet or statistical software, to create and customize your visualizations. You can also use online tools, such as Tableau or Power BI, to create interactive and dynamic visualizations.
Nowadays more and more companies are using Tableau as their BI tool of choice. It's so easy to connect to a database or even an Excel spreadsheet that anyone can quickly build compelling dashboards and visuals. If you haven't already, it's worth learning Tableau as a data analyst or management consultant. If you are a student or have access to a college/university email address, you can even get a free one-year license of Tableau.
The final step of data analysis is to interpret and report your findings. You need to answer the questions that you set at the beginning of your analysis and explain what your data means and how it relates to your goals and hypotheses. You also need to report your findings in a clear, concise, and persuasive way, using evidence, arguments, and recommendations. You can use various formats and channels to report your findings, such as reports, presentations, infographics, or blogs. You can use tools, such as Word or PowerPoint, to create and edit your reports.
Data analysis is a valuable skill for administrative managers who want to improve their project performance and outcomes. By following these tips, you can analyze data for your project more effectively and efficiently.
You must design your analysis plan right from the start of the project. That will help in defining what data needs to be collected. For studies involving human subjects, knowing what demographic details will sharpen the analysis will decide the depth of participant metadata collected.
Grants Acquisition | Project Management | MEAL | Data Analytics | Machine Learning
Good data analysis starts with good analysis plan. To make analysis doesn't start when data has already been collected but it starts when you are making sets of questions that answers indicators that were to be measured. Don't forget the indepth meta-data that will make very insightful analysis when incorporated in data.