Businesses around the world are striving every day to become more data-driven, and as such, how they collect and manage this data is evolving. One important topic that has arisen out of this shift is data governance. In this post, we set out to answer the following questions:
Data cleaning is part of a greater effort to achieve the highest data quality possible in used in business decisions and operations. It requires organizational effort and participation throughout a business and when done correctly, can help to provide valuable insights and analytics for decision making. A few additional benefits associated with data cleaning include:
- Streamlined business practices
- Increased productivity
- Faster sales cycle
- Better analytics
Given the ever growing quantity of data for many businesses, automation is required in data cleaning. The right data tool can fill in these gaps and manage a number of issues automatically before they have a chance to become truly problematic. This can ultimately help businesses to become more efficient and more profitable in their efforts.
Choosing the right data cleaning tool for your organization is essential to getting the most utility for your investment. To help in your decision making, this post answers the following:
With more of our decisions and activities becoming data driven, we need to ensure the quality of the data that we’re using. Data cleaning (or data cleansing, data scrubbing) broadly refers to the processes that have been developed to help organizations have better data. These processes have a wide range of benefits for any organization that chooses to implement them, but better decision making may be the one that comes to mind first.
Some common questions related to data cleaning that we cover in this post include:
Humans are naturally visual creatures, so it is no surprise that we search for better ways to portray and understand what data is telling us. Data visualization is one of the most effective ways to achieve this because it enables us to take data or ideas that are abstract and difficult to understand as written, and turn them into something easily understood in a more graphical or visual format. To learn more about data visualization and how organizations use it, see our blog post.
However, for any data visualization to be effective, it requires the right tool – which in many cases can mean a multifunctional tool that includes capacity for both visualization and analysis.
As we become better at collecting and analyzing data, we also have to become better at explaining what it all means. Data visualization is the use of algorithms to create images (graphical and pictorial) from data so that humans can more effectively understand and respond to that data. It is important because it enables us to digest large amounts of complex data that would otherwise be overwhelming or difficult to understand.
To better understand data visualization, there are several questions that we will answer: