In the world of data engineering, the concept of “big data” has become a buzzword in recent years. While it is true that handling large amounts of data is a challenge that data engineers must overcome, the emphasis on the size of the data can sometimes overshadow the importance of the quality of the data. In this article, we will discuss why good data is more important than big data.
The Importance of Good Data
Good data refers to data that is accurate, consistent, relevant, and timely. It is essential for the business to understand that the quality of data is more important than the quantity of data. Poor data engineers have battled with finding, cleaning, and making data available for far too long, for the detriment of their health and happiness. Having good data means that the data can be trusted to make informed decisions and take actions that drive business success.
Accuracy
Accuracy is crucial when it comes to data. Good data is data that is free of errors and inconsistencies. When data is inaccurate, it can lead to incorrect analysis and flawed decision-making. For instance, if a company’s sales data is incorrect, it may make inaccurate projections for future sales, leading to overstocked inventory or lost revenue opportunities.
Consistency
Consistency in data means that the data is reliable over time. Good data should be consistent across all sources, formats, and systems. Inconsistencies can cause confusion and lead to errors in analysis. Consistent data ensures that the data can be relied upon for decision-making, leading to better outcomes.
Relevance
Relevance in data means that the data is meaningful and applicable to the business goals. Good data should be relevant to the problem or question at hand. Data that is irrelevant can be a distraction and lead to wasted resources. Relevant data allows for better decision-making and more efficient use of resources.
Timeliness
Timeliness in data refers to how quickly the data is available for use. Good data should be timely, meaning that it is available when needed. Outdated or delayed data can lead to missed opportunities or incorrect decisions. Timely data ensures that decisions can be made quickly and efficiently.
The Risks of Big Data
While big data has its benefits, it also comes with its own set of risks. The sheer volume of data can make it difficult to manage and analyze. Additionally, big data can lead to a focus on quantity over quality, leading to inaccurate or incomplete analysis.
Increased Complexity
Managing and analyzing large volumes of data is a complex task. Data engineers must ensure the data is stored, processed, and analyzed efficiently. The complexity of big data can lead to higher costs and longer processing times.
Incomplete Data
Big data can be overwhelming, and it can be challenging to determine which data is relevant to the problem at hand. Incomplete data can lead to inaccurate or incomplete analysis. This can result in missed opportunities or poor decision-making.
Biased Analysis
Big data can also lead to biased analysis. With so much data, it is easy to overlook biases and assumptions in the analysis. This can lead to incorrect conclusions and flawed decision-making.
The Business Needs to Take More Responsibility
Data engineers are in a difficult position. Unrealistic expectations and unmanageable workloads cause a great deal of frustration and an underlying knowledge that their skills are wasted trying to achieve the impossible. Luckily, some experienced data professionals champion the importance of data quality. People like Chad Sanderson are pushing for more dialogue between data producers and data consumers and placing an emphasis on quality.
However, as much as people like Chad focus on the right things, much of his audience is fellow data professionals and these messages don’t necessarily find the right audience. It’s the business that needs to take more responsibility. Forget buzzwords and focus on business goals. Prioritize good data over big data. While big data has its benefits, the emphasis on quantity over quality can lead to inaccurate analysis and flawed decision-making. Good data is accurate, consistent, relevant, and timely, and ensures that it can be trusted for informed decision-making.
In order to make this transition, business and domain leaders need to stop obsessing about big data and prioritize good data. This requires more conversation, bridging the IT and domain divide and understanding business goals and the data needed to fulfill them.
Data engineers can help drive business success and make a positive impact on their organization if they can disregard data that is meaningless and focus on quality over quantity. This can only be achieved with a clear specification of domain needs and not just an expectation that if ‘we have all the data, we can make all the decisions’.