Data quality is more than just accuracy. Data quality must also be focused on data usage and its timeliness, relevance, and accuracy. The ultimate data should be fit for purpose. Today, data is being used to break long-standing bottlenecks as organizations are integrating new ways and technological advancements to handle the growing needs of data and utilizing the power of cloud capabilities to discover new insights.
With the mass adoption of data operations, the quality of internal databases and operational data stores needs to be managed. Incorporating essential tools in the data operations environment helps in deriving oversight and controlling the data across every process. Data should be made available in a structure that is easily understood and consumable for discovery or other purposes.
Delivering data with quality implies validating and describing the assembled data from the consumer's perspective. The purpose is to discover new elements in data and define how the data fit for purpose and discovery, to understand and to act. Leveraging automation enables organizations to derive insights into the quality of data with real-time views of data characteristics.
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Challenges to Make Data Available
Data and artificial intelligence are being used in innovative ways to break through long-standing bottlenecks. However, the challenges of making data available are far from new. All that matters is making data available at the right time and the right place with known quality. The timing is more challenging as the data must be current and up to date. The place where the data is required is also getting challenging as new collaborations across enterprises and their partners are developing.
Accelerated innovation requires the integration of complex workflows with different experts and stakeholders. Delivering data with known quality implies that it is validated and described from the consumer's perspective. This is challenging when integrating the data into the company culture. When defining data placement, organizations should consider data collaboration challenges and ensure that data placement is flexible, dynamic, and easy to move.
The data needs to be made available for it to be easily understood and consumable for the application - whether for discovery or other purposes. Organizations are integrating new ways to incorporate technological advancements to handle the growing needs of data and analytics, utilizing the power of data-driven capabilities.
Key elements in the success and repeatability of actionable analytics programs include-
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Business alignment to determine the context as well as the value of using information.
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Data understanding to better understand data assets and manage a driven data culture accordingly.
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Data quality to define the accuracy for the defined data purpose.
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Data-centric processes to increase an understanding of new data created as a part of an operational process.
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Data-centric resources to embed data-oriented knowledge and skills.
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For an organization, if the purpose of the data is to discover new insights, then data in any shape should be considered. However, they must define how the data can be fit for purpose to understand and act. To gain an even broader view of data, organizations need to ensure that the accumulated data is fit for purpose - from a quality standpoint, timing perspective, as well as for the user. It is important to understand the data to take action.
Exploring Beyond the Quality of Data
Data quality should concern more than just data accuracy. It presents a lens directed toward data quality while also focusing on its usage. The key data quality dimensions include the following-
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Completeness.
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Timeliness of the available data for use
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Conformity of the data values
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The uniqueness of the data within a given data set
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The integrity of data to identify if any important data elements are missing
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Consistency of data objects in multiple environments
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Accuracy of data that represents the real-world business vales
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Quality of the data
Another critical aspect of data quality is the intersection of business goals and its alignment with data understanding. An organization's goal should be to drive the prioritization of data quality dimensions and requirements. Data understanding will help in identifying whether the accumulated data meets the business requirements. If the data does not comply, organizations should indicate measures, including appropriate data steward, to determine the steps required to improve the data quality. Massive volumes of data dampen data quality issues, making it vital for organizations to give deliberate thought and engineering to the role of data quality and its effects in different analytics solutions.
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Determining the Need for Data
It is critical for businesses to understand the data landscape and the nature of their data. The next logical step is identifying the required data elements and their source. It is also important to understand the presentation and the data manipulation. The accumulated data is then processed through an algorithm that presents the results as an analysis or report. The algorithm helps in determining the data that will be required. The next step involves sourcing the data and applying the data quality dimensions to ensure that the desired source of data is appropriate for use. This helps in understanding if the data quality dimensions are relevant to the efforts. The elaborate algorithm requires adequate coverage and the need to be of the correct historical value.
Aligning Data with Business Needs
Once an organization has an idea of the use and source of the data, it can establish an operational context to dictate a higher level of data accuracy. They should ensure that the users of the data, as well as the resulting calculations, are clear to apply the results and act. To uncover new insights from unasked questions, organizations need to understand how the systems will react if new insights are uncovered.
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Final Thoughts
Sustaining the desired level of data quality is vital for organizations. Determining the dimensions that are to be used and setting a defined target of quality will enable businesses to make the data fit for purpose. With precise profiling and presentation of data quality levels, they can identify the data quality scorecard and accuracy and determine the data accessibility. They can also profile the data to categorize and measure quality aspects. All data quality processes should align with existing initiatives defined in the organizational data governance framework to ensure the data is sustainable and consistent across the organization.
The significant takeaway for data quality is not to look for the perfect data, as there is no economical way to do that. The key is to extract what the business processes require out of the existing and well-documented data landscape, determine the quality of data requirements that are likely to affect the brand's success, and then profile the data for it to be fit for the purpose.
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