Do you ever feel like you're swimming in data but still not making progress toward your business goals? You're not alone.
Even the most prominent businesses feel the crunch of overwhelming amounts of data. Implementing data analytics is vital for taking your business to new heights, but it can be tricky to know where to start.
That's why we've created this tech roadmap for smaller companies just starting on their data analytics journey.
This analytics implementation guide will walk you through the basics of data analytics and provide you with helpful tips, advice, and resources for getting started. Whether you're just dipping your toes into data analytics or looking for a refresher, read on…
Data analytics is the process of collecting, organizing, and analyzing data to uncover patterns and trends. Businesses use data analytics to improve decision-making, optimize operations, and identify new opportunities.
Analytics can provide insight into everything from customer behavior to sales trends to which marketing campaigns are most effective. However, simply having data is not enough. In order to reap the benefits of analytics, businesses need to implement data analysis in a strategic and effective way.
This is easier said than done. Implementing analytics can be a complex and daunting task, especially for businesses that are new to analytics. Fortunately, there are resources and tools available to help businesses through the process.
Get started by identifying the business problem you want to solve, followed by this four-step process:
You can collect data from a variety of sources including sales figures, customer surveys, and financial records. It requires a methodical approach to ensure accuracy and completeness. There are a variety of data collection tools, including survey tools, focus groups, interviews, observations, and measuring devices.
This step involves identifying and correcting errors, filling in missing values, and transforming data into a format that is more suitable for analysis.
Data that is collected manually is more likely to contain errors than data that is collected electronically and therefore may require more extensive cleaning. Additionally, data that is collected from multiple sources may need to be "normalized" before it can be analyzed—meaning that it needs to be transformed into a consistent format.
There are a variety of tools that can be used for data preparation, depending on the type and quality of the data. For example, data that is stored in a relational database can be cleaned using SQL (Structured Query Language).
This step usually involves using statistical methods and an analytics platform to find patterns in the data. Once the data has been prepared, the analyst will use one or more analytical techniques to examine it. Commonly used techniques include qualitative data analysis (such as content analysis) and quantitative data analysis (such as regression analysis).
Data visualization is the process of converting data into a graphical format, such as a chart or diagram. Some common data visualization techniques include scatter plots, line graphs, bar charts, and pie charts.
There are many different data visualization tools available, both free and paid. If you're interested in software to help you create data visualizations, check out the top 10 data visualization tools for startups.
Data analytics provides a distinct competitive edge in today's data-driven marketplace. By using data analytics solutions, businesses can glean critical insights from their data and take advantage of new opportunities as they arise.
With the proper planning and execution, data analytics can provide immense value to your business and improve decision-making across all departments.
Implementing data analytics can be daunting, but with the help of GetApp's comprehensive database of software solutions, you can find the right tools for your business objectives.
Check out these related posts for more information on managing data:
Explore by topic