MySQL is one of the most widespread open-source relational database management system (RDBS) in the world. Deploying a database and using a program such as MySQL allows you to store, manage and retrieve your data more efficiently and handle substantial amounts of information in one place. As an aggregation of data records or files, it can store anything from sales transactions or customer profiles to a photo gallery.
However, performing data analysis with MySQL might seem complicated for most non-technical users, as the system is based on an own programming language, Structured Query Language (SQL), but all in all it is not too different from the tables we build in Excel or even Word. The process remains identical: collecting data, cleaning it to have the best data quality possible, and visualizing it in an understandable format for the human eye through dashboard templates. All you need to setup these processes in MySQL are the right tools and practices. Let's examine this process in more detail, and break it down into four steps.
Step 1 – Data collection
As a company, you might experience a massive aggregation of data of all kind – stored in different places, different formats, different files. It makes things even more difficult when the moment comes and you want to have a global overview of what is happening in your business, and you need to bring all of these data streams together to be able to analyze it and turn it into actionable insights.
MySQL has the means to aggregate data from various sources and facilitate an effective analysis. CSV files, Excel, XML files, or many others, you can collect your data through many tools and store them in your database to make use of them afterwards.
Step 2 – Data Quality
Data quality is like antibodies in our body: it identifies and corrects inconsistencies among valuable data. This applies to every type of data, and MySQL is no different: you will need to setup processes to clean the tables, by deleting and correcting incorrect, irrelevant or incomplete parts and replace, modify or delete them.
Data quality is what every data-driven business should aim at. It might take time and resources, but will ultimately increase Return on Investment and save you a lot of money. Indeed, a TDWI report estimates that low-quality data costs over $600 billion to US businesses solely. At world-scale, the figure must be hardly fathomable. Bad data quality has both tangible and intangible costs, the biggest of them being misinformed decisions.
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There are a couple of principles to follow for a good data quality management (DQM). First are the people: you need to assign specific roles to specific people. A DQM manager will be responsible of the general supervision of the data quality project. An organization change manager will provide insights into data technology solution. Finally, a data analyst will define the data quality needs, and quantify them into models, while also communicating them to the development team. These needs will depend on the industry, and will encompass core business requirements. Hereafter are a couple of useful SQL functions during the cleaning process:
- LIKE() is a simple pattern matching
- REPLACE() is replacing the occurrences of a specified string
- CONCAT() is for using concatenate strings
- FORMAT() converts a number with a specified number of decimal
- TRIM() removes leading and trailing spaces.
Related Post: SQL string functions
Step 3 – Analysis and Visualization
Once your datasets have a quality level high enough, you can start with the exciting part: analyzing. Any analysis you will perform is ensured to be valuable as the data you are working with is accurate. To bring this examination a step further, if you store data from different data sources in one place you can also perform cross-data source analyses, and get a bigger overview of what is happening in your business.
For the analysis of your data sets, it does not take much to tweak your data in ways to get valuable insights in just a few minutes. For instance, you can query your sales revenue with a simple SELECT statement, group by customer name, order the resulting revenue with a DESC function, and LIMIT the results to 10 so as to have your top 10 customers by sales revenue. You can also display your sales per region, country, product, gender; or you can calculate some rates, retention, or growth.
To understand your findings better, it is advisable to visualize them through different charts and graphs. Choosing the right data visualization is important, as different views answer different questions! A pie chart will not speak as well as a line chart if your data must be seen over the course of time. Some graphs are better at telling a specific story, therefore you should choose your data visualization carefully for an efficient reporting.
Step 4 – Dashboards and Reports
After analyzing and visualizing your insights, an efficient way to communicate your findings is to display them all under powerful dashboards and reports. There are a lot of ways to create dashboards, but it is not always a piece of cake when one doesn’t have the appropriate tools. Data visualization software are here to help you create compelling reports for you to share. An effective MySQL Reporting tool will let you work on your MySQL database, and even build your own MySQL queries without needing to write a single line of code! It is possible thanks to an intuitive drag & drop interface that turns your interactions automatically into queries.
Gleaning insights from your data is not as easy as it seems – but it is worth it. Not only will it improve the quality of your decision-making, now better informed, but it will consequently bring to your company a greater ROI and take your business forward.
Mona Lebied - Content Manager at datapine
Mona is a passionate writer about data analytics and innovative business reporting techniques. She is working as Content Manager for datapine, in the bustling startup landscape of Berlin, Germany.
https://www.datapine.com. Connect with her on LinkedIn.
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