The tech industry has gone abuzz with big data, with every sector looking to tap into its benefits. However, as the amount of data being collected increases exponentially, techniques of ensuring big data quality are crucial in helping businesses and organizations make accurate, reliable, and effective business decisions. Unfortunately, data quality remains a major challenge in most data modeling projects, as unexpected issues, such as typos and missed entries, can arise anytime.
What is Big Data Quality Control?
As the name suggests, big data quality management is the practice of maintaining the quality of collected information. This ranges from implementing advanced data collection measures to the effective distribution of analyzed data. Effective data quality is essential during data analysis since the quality of collected data is important in deriving actionable and accurate insights.
Fortunately, there are a lot of strategies for improving data quality. These measures are engineered to prepare organizations to face any presenting challenges in the current digital data age anytime they appear. Below are insights on the importance of data quality management, pillars of good data management, and quality data management measures.
Why Do You Need Big Data Quality Control?
While the current digital age has successfully provided endless amounts of data, it has also led to a data crisis, which refers to the presence of low-quality data. For starters, data quality describes the state of collected data relative to its intended purpose and ability to serve the desired objective. Data can be categorized as either low quality or high quality based on its accuracy, consistency, timeliness, and completeness.
Generally, the quality of any data is crucial in fulfilling the organizations’ operations, plans, and decision-making. Currently, more and more organizations rely heavily on data, increasing the demand for quality data. Conversely, low data quality is a leading cause of failure in most data and technology-reliant initiatives, costing American businesses up to $97 million annually.
Quality data control has beneficial ripple effects ranging from supply chain management, customer relationship management to resource planning. With high-quality data, companies can establish strategies for predicting trends and future strategies.
Pillars for Controlling Large Volumes of Data
Evidently, ensuring quality control of large volumes of data is beneficial for most businesses. Below are some building blocks of big data quality control.
1) The People/Personnel
Technology can only be efficient if those implementing it are efficient. Even with a technologically advanced setup, poor human oversight may render the technology obsolete. Therefore, controlling large volumes of data requires efficiency of the following people;
- Program manager – the DQM program manager, should be an experienced leader with great oversight skills. The manager should be hands-on to oversee daily activities, such as data scope and implementation of various programs. They should have a clear vision for quality data.
- Change manager – the main role of a change manager is to organize or ensure great data organization. They should assist your team by providing clear and insightful data technological solutions.
- Data analyst – business data analyst, defines data quality needs of an organization. This person should communicate data quality theories to other members of the team.
2) Data Profiling
Data profiling is another essential process in data quality management. This involves a comprehensive review of the collected data, comparing and contrasting data subsets, running collected data through statistical models, and generating reports on data quality. The main goal of this process is to develop insights about the existing data and comparing it with its goals.
3) Defining Data Quality
Defining data quality is the third pillar of managing data quality. This stage involves defining quality rules based on your business or organizational requirements. These are basically technical rules that data should comply with before being considered as viable. For this, business requirement takes the front seat, since crucial data elements are industry dependent. Developing quality rules is important for the success of the DQM process. Well-developed guides can easily detect and prevent data contamination.
4) Data Reporting
Data reporting involves recording and removing any compromised data. Data reporting should follow the natural processes outlined in designing data rules. Following these rules make it easy to identify and capture exceptions in data subsets. Generally, reporting and monitoring provide visibility of the state of data in real-time. Businesses that identify exceptions in data can easily plan remediation processes.
5) Data Repair
Data repair involves identifying the best way of remediating data and the most efficient manner of implementing change. Root cause analysis is the main element of data remediation, as it helps businesses identify why, how, and where the defected data originated from.
Measuring the quality of large volumes of data requires several data quality metrics. While this can be complicated, working with quality data assures of making solid and evidence-based plans. Compromised data hinders your ability to make accurate business decisions.
As mentioned, there is high demand for big data specialists. This has also led to a surge in institutions offering quality training for big data analysts and other experts. High schoolers who are interested in career paths can take online classes.