Tag: Data Lineage

Companies are dealing with overwhelming amounts of data, and this data must be governed, compliant, and secure, especially when working in the financial, healthcare, and insurance sectors. As the complexity of data environments increases, traditional data governance approaches largely fail to address these challenges adequately and lead to the emergence of what many researchers refer to as Data Governance 2.0. undefined Laying its foundation is the metadata-driven data fabric, which represents a highly transformative approach to data management and governance, compliance, and security.

Expanding on the concept of data fabric architecture and elements, this article focuses specifically on the use of metadata layers to improve governance and compliance for businesses operating in highly regulated environments.

In this blog, we will also discuss the concepts, opportunities, and risks of constructing a metadata-driven data fabric to enhance compliance and security.

The Evolution of Data Governance: From 1.0 to 2.0

Data Governance 1.0: Legacy Governance Models

The conventional view of the data governance process was mainly concerned with data adequacy, control, compliance, and the ability to store data securely in isolated databases. This was primarily a rule-governed and manual approach. The governance policies we had were far from dynamic and flexible to adapt to the evolving needs of the current organizations.

Legacy systems in Data Governance 1.0 face several limitations:

Manual processes: Some of the measures of security are checked manually, and this leads to slow processes and errors because it is done by human beings.

Siloed data: Data resides in multiple systems and silos, which causes issues with governance alignment.

Static policies: Governance rules do not adapt to the emergence of new data scenarios and the constantly evolving compliance requirements.

Why Data Governance 2.0?

The data environment has changed, and it is now imperative for organisations to sort data through hybrid and multi-cloud solutions, and address increasing concerns of compliance and security. This phenomenon is has therefore resulted to what is now known as Data Governance 2. 0, a governance model designed for the modern data ecosystem, characterized by:

Real-time governance: Managing a multilayered set of governance policies for both cloud and on-premises & hybrid solutions.

Data integration: Integration management of distributed data and assets with out leaving their original location.

Proactive compliance: Engaging metadata and AI to enforce compliance in a dynamic manner.

To Know More, Read Full Article @ https://ai-techpark.com/how-metadata-driven-data-fabric-ensures-compliance-and-security/

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In an era where data is heralded as the new oil, there’s an inconvenient truth that many organizations are just beginning to confront: it is therefore important to realize that not all data is equal. With the increasing digitalization of the economy and an imperative to increasingly rely on data in products and services, the focus has been traditionally on the sheer amount of data that can be gathered to feed analytics, provide clients with personalized experiences, and inform strategic actions. However, without this policy to embrace data quality and data lineage, even the strenuous data collection would result in disastrous results.

Let us take an example of a general merchandising retailer chain that, to sustain and overcome its competitors, started a large-scale acquisition-based customer loyalty campaign with help of their gigantic data warehouse. High expectations of the initiative and great investment to make it work reached a deadlock when the issue was revealed: the data behind the plan was unreliable. The promotions of the retailer were wrong since the wrong customers were being targeted, and this eroded the trust of the customers.

This is not an unusual case. In fact, all these issues will sound very familiar in most organizations, yet often with no realization regarding potential hidden costs in the form of poor data quality and a lack of understanding in terms of data lineage. If data is to become a true strategic resource, then organizations have got to go beyond what appears to be mere numbers and down traceability of data. Only then can they establish the much-needed trust in today’s world to answer the diversified needs of the customers and the regulating bodies.

The Hidden Truth About Data: It’s Only as Good as Its Quality

The question is: Who would not want to work with data? The truth is that data is full of errors, inconsistencies, and inaccuracies. Data quality is an issue that ultimately touches upon the decision-making process, organizational compliance, and customer trust.  Let’s consider the following:

For instance, consider a marketing team working on creating a marketing campaign that was based on customer information that might have been entered incorrectly or not updated for several years. The result? Incorrect targeting, resource expenditure, and perhaps the antagonizing of clients. It therefore underlines the significance of sound data—a factor that is relevant both in making decisions and in customer relations.

Key Elements of Data Quality:

Accuracy: The data used should be accurate and depict the true worth and facts.

Completeness: All necessary data should be included without any gaps, i.e., all important data must be there with no breaks in between.

Consistency: Data should not only be uniform with all the systems and reports of the company, but also the format used should be uniform.

Timeliness: Data should be in real-time, and this data should be accessible whenever it is required.

Validity: The attributes should be of the right format and within the right range.

To Know More, Read Full Article @ https://ai-techpark.com/data-quality-and-data-lineage-elevate-trust-and-reliability/

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As organizations continue doubling their reliance on data, the question of having credible data becomes more and more important. However, with the increase in volume and variety of the data, high quality and keeping track of where the data is coming from and how it is being transformed become essential for building credibility with the data. This blog is about data quality and data lineage and how both concepts contribute to the creation of a rock-solid foundation of trust and reliability in any organization.

The Importance of Data Quality

Assurance of data quality is the foundation of any data-oriented approach. Advanced information’reflects realities of the environment accurately, comprehensively, and without contradiction and delays.’ It makes it possible for decisions that are made on the basis of this data to be accurate and reliable. However, the use of inaccurate data leads to mistakes, unwise decisions to be made, and also demoralization of stakeholders.

Accuracy:

Accuracy, as pertains to data definition, means the extent to which the data measured is actually representative of the entities that it describes or the conditions it quantifies. Accuracy in numbers reduces the margin of error in the results of analysis and conclusions made.

Completeness:

Accurate data provides all important information requisite in order to arrive at the right decisions. Missing information can leave one uninformed, thus leading to the wrong conclusions.

Consistency:

It makes data consistent within the different systems and databases within an organization. Conflicting information is always confusing and may not allow an accurate assessment of a given situation to be made.

Timeliness:

Data is real-time; hence, decisions made reflect on the current position of the firm and the changes that are occurring within it.

When data is being treated as an important company asset, it becomes crucial to maintain the quality of the data and to know its origin in order to build its credibility. Companies that follow data quality and lineage will be in a better position to take the right decisions, follow the rules and regulations set for them, and be in a better position compared to their competitors. If adopted in their data management process, these practices can help organizations realize the full value of their data, encompassing certainty and dependability central to organizational success.

To Know More, Read Full Article @ https://ai-techpark.com/data-quality-and-data-lineage/

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Data veracity and quality are equally essential in the current world that is characterized by unbounded data generation and utilization. Analyzing the information being used in the operations of companies today, it is apparent that businesses have developed a high dependence on data, hence the need to make it accurate and reliable. Two ideas central to this effort are data governance and lineage. Data availability, usability, integrity, and security of data within an organization can collectively be referred to as data governance. Contrarily, lineage tracking refers to one’s ability to follow the life cycle of the data, starting from its source to the current status. Altogether, these practices are needed for a small business as well as for large and middle-scale enterprises, as they offer the base for managing the information and meeting the requirements.

Understanding Data Governance

What is data governance?

Data governance refers to the culture, norms, rules, and guidelines that govern the organization’s data resources. These are ownership of data, quality assurance, and governance, where components include obtaining commitments on ownership of data/information, defining data quality requirements, and ensuring the agreed data access and security provisions are in place. Because of this, data governance’s crucial function is in covering the regulatory requirements and sources of risk that need to be addressed so that better decisions can be made.

Benefits of Data Governance

Ensuring Data Quality and Accuracy: Enumerating best practices as well as standardizing procedures promotes the credibility, integrities, and sanity of the data.

Enhancing Decision-Making and Operational Efficiency: Optimization of workflow solutions improves with high-quality data, leading to overall effectiveness of operations.

Protecting Sensitive Information and Maintaining Privacy: Data governance is aimed at data security so that the organization complies with the data privacy laws and reduces the risks of data leakage.

Data governance and data lineage are crucial methods that add value to an organization through data accountability. This means that through the formulation of appropriate data governance frameworks, as well as timely and accurate lineage solutions, business value can be realized from data assets.

Evaluate your current data management state and think about implementing the data governance and data lineage management concepts for your organization’s profitable improvement.

Availing more details to more people in the future, the trends in data governance and lineage tracking will include AI and ML.

Thus, by optimizing such practices, it will be possible to manage the effectiveness of organizational data and use it as a lever for the company’s success.

To Know More, Read Full Article @ https://ai-techpark.com/optimizing-data-governance-and-lineage/

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