Employing the Power of Data Quality
There are no doubts about this – data runs the world today. Enterprises are striving to figure out how much data they have, how much data is enough, how to start or pick the right data, how to measure return on investment (ROI) and what value they can harness for their data. But above all these scenarios one major concern floats over the surface – Data Quality. Did you know that poor quality data cannot only cost your business revenue but also negatively impact business decisions?
Just give a thought about it!!!
Poor data quality has bad outcomes – analysis is flawed, data insights are unreliable, and if data quality is not right, there are chances you would end up leading your business into unexpected conundrums. So, Don’t Let Data Fool You!
Your data needs to be Reliable, instantly Available, and Secure!!
With the increasing innovation and automation, it’s possible to collect, store and access more information than ever before. But yet data quality proves to be a consequential hurdle for many enterprises.
When we talk about data quality, what does it mean? Data quality can be defined in many different ways. In the 1990s many scholars suggested different definitions of data quality and division methods of quality dimensions. Data quality always depends on the context in which it is used, leading to the conclusion that there is no absolute valid quality benchmark.
According to some data experts, data quality depends not only on its features but also on the business environment using the data, including business processes and business users. Does data that conforms to the subsequent uses and meets requirements, be considered qualified (or good quality) data? OR would you rather lean on the idea that data quality standards can be set up from the perspective of data producers?
Before we continue let’s go through some major growing challenges for data quality in today’s organizations.
- Incorrect data seeds false facts, erroneous data management, and bad decisions in many data-driven environments.
- Lack of uniform standardization for data quality leads to inconsistencies and can threaten business perspective.
- Lack of data quality guidelines develops multiple data copies that can be cost heavily to clean up such data.
- Maintaining the uniform data for internal as well as external data sources so that systems can talk to each other.
We are aware that having apt data quality directly correlates with an organization’s ability to make the right decisions and guarantee its economic success. But how to know that data we are utilizing is appropriate, as data quality issues along with a lack of standardization can be a real – threat!! So, here in this article, we will talk about several parameters that are used for evaluating data quality. And if u get to know about these data quality parameters or elected we can assure you that Data can’t fool you!!
FIVE ELEMENTS OF DATA QUALITY:
For data quality standard these five elements are a must –
- Presentation quality
1. Availability projects the degree of convenience for users to obtain any particular data and related information. Availability is further branched out into the two elements- accessibility and timeliness.
> Accessibility is closely linked with data openness. The higher the degree of openness degree, the more data types obtained, and the higher the degree of accessibility.
> Timeliness refers to the time delay from data generation and acquisition to the utilization of the data. For meaningful analysis, data should be available within this time delay.
2. Reliability refers to whether the data can be trusted. This condition depends on data accuracy, consistency, completeness, adequacy, and auditability elements.
3. Usability means whether the data are useful and meet users’ needs. It includes data definition/documentation, credibility, and metadata.
4. Relevance is the degree of correlation between data content and users’ expectations or demands in which adaptability is its quality element.
5. Presentation quality is the integral section that refers to data’s valid description method, which allows users to fully understand the data. Its elements are readability and structure.
With the advent of the web, social media, big data, and cloud technologies, data is speckled all over the place, both on-premise and off-premise. Without proper visibility, analysis, governance, and control, your organizational data is practically unusable and sometimes even risky. For BI professionals, new technologies are only valued if the core data quality elements are well looked after.
ENGAGING DATA GOVERNANCE, DATA QUALITY, AND ANALYTICS TOGETHER
a. Businesses can protect data quality by implementing an enterprise data intelligence platform with a comprehensive set of abilities. To gain more value from their data and ensure the quality of the data across systems and processes, data governance, data quality, and analytics capabilities can be made work together.
b. Enterprise can use a data intelligence platform to provide a solid data governance framework. As a result, it will help to ease practices that are projected to ensure data quality throughout the data supply chain.
c. Data need to be easily understood, accessed, and trusted by stakeholders at every step of the process. This allows for the data to be used to generate meaningful insights and drive business decisions.
According to a recent BI survey, data quality has been voted among the top three problems for BI software users every year. Quality data lies at the heart of the tactical, strategic, and operational steering of every enterprise.
In the age of digitization, reliable data is viewed as an imperative as well as a decisive production factor. From disparate applications to data silos, in the context of business intelligence, sustainable data quality management will pay off. Companies experiencing data quality puzzles should start defining a short set of guidelines in which they acknowledge that data.
Reach out to us if you have data quality challenges that you need help with.
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