In a highly competitive bid landscape, data analytics can give you an edge over your competition. Data analysis systematically uses applied statistics and local techniques to evaluate data. Using these tools can mean faster and better business decisions, reduction of overall business costs, and developing new products and services.
Main Data Analysis Techniques
Using data analytics to its fullest advantage means combining several types of analytics to give you the complete “big picture.” The main types of analytics include:
Text Analytics uses machine learning and natural language processing to organize data, extracting insights from unstructured text data. It offers in-depth views and interpretations of patterns and trends to show why something is happening.
Statistical Analysis uses data collection, analysis, and interpretation to answer the question, “What happened?”
Diagnostic Analysis builds on the information received from statistical analysis, identifying patterns in data to answer the question, “Why did this happen?”
Predictive Analysis uses patterns in data from past and present events to predict future events. It answers questions involving the future, or “What’s most likely to happen?”
Prescriptive Analysis combines all data and analytics to offer the most appropriate course of action. It is a combination of all a company’s data and analysis results to analyze scenarios, predict their outcomes, and choose the best option for the company based on the results. Used by less than 3% of companies, Prescriptive Analysis is the most complex and comprehensive of the analysis techniques.
Seven Steps for Analyzing Data
Using data analytics to your highest advantage may seem a little overwhelming at first, but it’s far easier to break down into steps. Here are seven steps to get you started, help you organize the data, and put it all together for a deeper analysis.
1. Define Your Goals
Understanding what you’re trying to discover through the data analytics process will help you decide which types of data are best to help you achieve your goals. What questions are you trying to answer?
2. Integrate Data Analysis Tools
Application Programming Interface (API) might be necessary to integrate the tools you need to perform your data analysis.
3. Collect and Organize the Data
Gather all collected data into one location, such as an Excel spreadsheet, so it can be easily located and analyzed. APIs and integrations are used to connect data sources directly to data analysis tools.
4. Clean the Data
In this instance, cleaning data means removing special characters, punctuation, and duplicate records. While it seems a tedious step, it’s an important one for accurate results.
5. Analyze the Data
Your data analysis approach will depend on your goals and the questions you need answered. Make this step easier by using software and tools, including Microsoft Power BI, SQL, Python, or Excel.
6. Interpret Data and Draw Conclusions
With all the data laid out, it’s time to dive in, gain insights, interpret your data, and identify your best course of action according to your results.
7. Data Visualization
It’s time to organize the data for presentation. Analysis tools frequently have wide selections for data visualization; choose the best methods based on the data you have collected, what you wish to highlight, how you’d like to present it, and your intended audience.
Applying Data Analysis to Win a Government Contract
Diagnostics analysis can be used to analyze a competitor’s past performance on similar contracts to help understand the competitor’s capabilities and make assumptions on future contract’s performance.
Predictive Analytics allows you to forecast outcomes and identify the likelihood of those outcomes happening so your team can better anticipate future performance. This means your business can avoid unnecessary risk, plan better, and set realistic goals.