Data Quality for enhancing strategic decision-making and fraud detection
Over the years, many reports and articles have been published on data quality and how it is a key factor in business success. Now, more than ever, under the shadow of an unprecedented global economic recession, in a hyper-connected environment and with much more awareness that data is key to gaining a clear competitive advantage, it is ESSENTIAL to apply quality to data that will help us make decisions.
What do we mean by high-quality data, and why is it crucial? What steps can we take to enhance them?
Quality Data
DAMA (Data Management Association – an international non-profit organization dedicated to promoting information management and data quality) has a broad and applicable definition of data quality that focuses on three key aspects:
- Data quality is a set of characteristics and qualities of data that make it suitable for a specific use.
- Data quality is reflected in the value that data provides to an organization or an individual.
- Data quality depends on the context and intended use of the data.
For DAMA, data quality refers to the qualities of data that make it suitable for a specific use and that provide value to an organization or an individual, taking into account the context and intended use.
According to Gartner, data quality is the extent to which information meets the requirements for accuracy, completeness, timeliness and veracity necessary to support the intended use. It is important to note that data quality is not an absolute concept, but depends on the context and intended use of the data. For example, data may be of high quality for one purpose, but not necessarily for another.
Data quality is not an absolute concept, but depends
on the context and intended use of data.
Why you need Data Quality in your company?
In today’s business landscape, strategically leveraging data has become an unavoidable imperative to ensure continuity and success. In fact, effective data management ensures optimal positioning for the company, leading to analytical advantages and the ability to monetize information.
Top executives no longer harbor doubts about the importance of having high-quality information, and for the majority of them, it has become a strategic priority. However the challenge lies in how to efficiently address this issue and extract the maximum value from the data. What are the distinctive characteristics and advantages of data-driven companies? How can we maximize the benefits they offer?
Every year, poor data quality costs
Gartner
companies an average of $12.9 million
It’s been proven that the intelligent long-term use of data leads to significant revenue growth. Therefore, companies experiencing rapid growth are less likely to miss the opportunity to utilize data for strategic decision-making.
What sets these companies apart is their perception of data as a valuable and strategic asset. Their various processes, strategies, and daily decisions are based on data-backed information.
Furthermore:
- They consistently assess their results, often employing data science for analysis and pilot testing.
- They have a deeper understanding of their customers and the ability to anticipate their future needs and desires.
- They incorporate data into all aspects of their operations and leverage internal information for continuous improvement.
Companies that strategically employ their data experience a significant increase in their economic performance, boosting their revenues and improving operational efficiency. Consequently, adopting best practices allows them to maximize the value of data and optimize their chances of achieving success.
There are several ways to measure data quality, and each can focus on different aspects or dimensions. Some of the most common dimensions usually mentioned in relation to data quality are the following:
- ACCURACY: refers to the precision and veracity of the data. It is important that the data be accurate to avoid errors or confusion.
- COMPLETENESS/VALIDITY: refers to the presence or absence of data. It is important that the data are complete in order to use them in a useful and reliable way, confirming that the data contain the values they should and are structured correctly.
- INTEGRITY: refers to the completeness and logical consistency of the data. It is imperative that the data have all the necessary elements to be trusted and used reliably.
- CONSISTENCY: refers to the coherence and uniformity of the data. It is important that there are no conflicts between the same data values in different systems or sets of information. They must be consistent so that they can be compared and used in a coherent manner.
- UNIQUENESS: it is important to know if you have the same information in the same or similar formats within the information source. In other words, confirm that there is no duplication in the information units.
- TIMELINESS/UPDATEDNESS: this refers to the freshness and relevance of the data. It is important that the data be up to date in order to make decisions based on accurate and relevant information.
It is necessary to keep in mind that these dimensions are not mutually exclusive and that data quality may depend on a combination of them. In addition, the importance of each dimension may vary depending on the context and intended use of the data.
In brief, Data Quality is vital for business success. It ensures valuable information, competitive advantage, efficiency, and a good reputation. On the other hand, low-quality data leads to problems, losses, distorts decisions, and affects financial performance. To stay competitive, it is clue to implement real-time quality verification and effective diagnostic actions. Important, isn’t it?
OMMA DATA is a Data Reliability and Quality platform that streamlines the implementation of quality in your company’s projects. Moreover, it allows flexible enrichment of quality rules. With OMMA, you can enjoy a 20x reduction in implementation time, freeing up technical profiles for value-added contributions in governance, quality improvement, and new projects. Additionally, it facilitates the transition to a Data-centric mindset, enabling you to:
- Make informed decisions.
- Save time and money by increasing efficiency, sustainability, and profitability.
- Discover market opportunities, products, or innovations.
- Build trust and transparency in data for all users.
- Foster collaborative work and break down silos.
Remember:
Data Quality starts with OMMA!
Are you in?
