Low data quality hurts business value, results in missed business opportunities and can represent risks in compliance requirements. According to a survey, 1 bad data quality costs medium-sized businesses an average of $ 9.5 million a year – making data errors more expensive for a business. This also presents negative impacts on efficiency, productivity, reliability, and reliability of data on MSEs. Costs are likely to increase in the digital and MSE business environment. Solving data quality challenges and building “trusted” data that creates new capabilities is critical to the success of digital MSEs systems.
The traditional method of data creation and analysis strategy begins with a years-long governance effort based on the mistaken notion that data must be fully quality and manageable before it can be useful. Targeted, duplicate data and mathematical strategy associated with business outcomes are a solution to improve organizational decision-making and demonstrate the value of data and mathematical functioning.
Solving data quality problems requires a multi-faceted approach that involves many key components – including roles and responsibilities, governance, processes, and technologies.
Poor data quality can cause many different problems. Dealing with data quality issues requires investigation into various aspects of these causes. To further prevent data quality problems from recurring, MSE CIOs should consider the following key factors when building data quality programs.
- Roles and responsibilities: Data quality systems are a team game and require close collaboration between all stakeholders. MSEs should establish data-related roles with the required skill sets and responsibilities. Specifically, data management is an important role that often comes from the business side – ensuring the use and oversight of data quality processes. In cases where it is difficult to hire someone new to this role, managers or senior users in business units may be able to assume the role of temporary data managers. Even a few temporary data managers can make a huge difference in efforts to improve data quality. Some roles, such as data designers or data quality developers, are better adapted to IT, and support the technical aspects of data quality operations.
- Data and analytics governance: Data and statistics management represents a set of guidelines, policies, agreements, priorities and processes that businesses need to agree on. In particular regulatory requirements (such as the GDPR, or CCPA), data and management statistics need to be transparent in business operations. An effective data quality system should define a number of acceptable data and processes based on management considerations.
- Company culture: Company culture can influence the way people within an organization interact and work together in terms of data and statistics. MSE CIOs need to raise awareness of data quality by linking it to business objectives and promoting collaboration between users to improve data quality.
- Data literacy: Data literacy is the ability to read, write and communicate data in a context. Data acquisition knowledge should include understanding the data sources and structures, analytical methods and techniques used. Businesses that can read well with data face significant data quality challenges.
- Processes: Implementing data quality is not a one-time effort, but a series of activities that take place throughout the data life cycle. It is important to perform data quality tasks as if they are part of business processes. In other words, MSEs should design process flow to ensure high quality data and perform these processes in an efficient and repetitive manner.
- Technologies and tools: Data quality services have real power without the tool. Topics experts need to investigate data quality problems, which are often solved by manual, and repetitive tasks. Given the rapidly changing business environment, it is almost impossible to measure data quality improvements to cover multiple operating conditions by adding more employees. MSE CIOs should use the latest technology to automatically perform processes to improve data quality.
We, in turn, need to identify skills and shortcomings that affect the success of our business’s data quality systems by including a list of existing data skills and processes. Also work with stakeholders to create a data quality system. Ensure and provide appropriate resources, and develop the skills, technologies and processes needed to implement a data quality system.
Improving Data Quality
While it may take some time to allocate resources needed to build an effective data quality system, MSE CIOs can begin their data quality campaign by first identifying the effects of poorly compromised business quality data. For example, you could point out that the goal of achieving a 20% improvement in customer satisfaction was unsuccessful due to increased shipping delays and customer inquiries caused by incorrect customer data. The next step is to identify “targeted” data quality by mapping the business result to specific data elements (for example, the main customer table, which stores important customer contact information).
Next, MSE CIOs must measure the quality of the data within the identified data element, in order to be able to obtain visibility in its actual quality. Data profile creation is the process of measuring data quality by reviewing source data, comprehension structure, content and interaction, and identifying potential data quality issues. Analyzing profile results can help verify the causal link between data quality and business impact. This means that appropriate remedial actions can be prioritized.
Profiling is one of the most common aspects of data quality solutions. This feature also exists in other data integration / ETL or data catalog applications. If these applications are already in the house, it is recommended that you check their profile profile feature. There are other open source data profile tools available from standard data quality vendors. MSEs can test these freemium tools first before making a financial commitment.
Data analysis results provide a good indication of possible causes of problems, such as an outdated data scheme, incorrect data entry or poor data interaction. Depending on the problems identified, appropriate remedies may include simultaneous data cleaning, permanent schema modification, or enforcement of data authentication in real time. However, the implementation of data quality is not a one-time effort, but a series of activities that take place throughout the data life cycle. It requires continuous improvement efforts.
We need to improve business conditions in order to improve data quality by identifying significant business outcomes that are adversely affected by low data quality and identifying the root cause of problems or identifying opportunities for improvement through data profile tools. And finally, establish a formal measurement process by setting targeted data quality improvement and monitoring progress over time.
Organization’s Appetite for Data Quality Improvement
Making people care about the data they create, and using it is an important part of data-driven culture. MSE CIOs can help raise awareness and sensitivity of data among all users.
- Linking business impacts to data quality.
- Reporting poor data results.
- Demonstrate how better quality improves productivity and results in better and faster decisions.
There is no mandate or motivation to improve data quality unless its impact on achieving certain business outcomes is well understood. In fact, create inspiration among users by showing them the business effects they can create with high quality data.
Data literacy is an important part of data-driven culture, which drives data quality. People with high data experience are more likely to use the data and communicate the data more efficiently. He may also be sensitive to the impact of data quality. The higher the percentage of users with data experience, the better the organization will be able to maintain good data quality. The success of data quality systems requires close collaboration between stakeholders and cannot be driven by IT alone.
Technologies driving Data Quality
The data quality market offers integrated solutions with a range of key functions, such as profile, partitioning, setting, cleaning, matching, enrichment and monitoring. Some leading vendors also use artificial intelligence (AI) technology to improve automation (for example, using automated data matching) and provide better data (e.g., using metadata acquisition).
The market for data quality tools has changed dramatically in recent years. The tools often support risky operating conditions (such as critical business operations, data migration and integration) that primarily require data validation or cleaning. Modern tools also support value-added application conditions such as 360-degree customer statistics and ML development by identifying data patterns and biases. Some also offer out-of-the-box data libraries that contain rich knowledge bases to support industry-based, country and language-based data quality operations.
The market for data quality solutions also emerges from pricing and licensing ideas. Traditionally, the price of data quality solutions has been calculated based on hardware as a major cost. Today, many retailers offer other price models like subscription-based or usage-based prices. These price models enable data quality as operating costs, which reduces entry costs.
The role of data and analytics is changing, from being a stand-alone discipline to a digital strategy or revolution. Kanoo Elite with its years of Data Analytics experience, provides expert consultation to Data and Analytics leaders to create a strategy and performance model that conceives data driven business opportunities and organizes business action. We also provide additional resources to assist you in communicating and marketing the program effectively to various stakeholder groups and implementing the strategy.