Data Lineage Archives - We Bridge Worlds LLC

In today’s enterprise environment, it’s difficult to overstate the value of high-quality data.

Data has worked its way into nearly every aspect of enterprise decision-making. It informs operational efficiency, marketing strategies, and compliance success. It enables organizations to increase adoption and improve collaboration.

Before it can do any of these things, data needs to be interpreted in a cost-effective way. Enterprise-level organizations and their stakeholders need to agree upon a common, well-documented understanding of how data moves throughout the organization. This framework must also stipulate who has access to data and why.

Organizations that establish clear data governance policies set themselves up for highly streamlined operations, reliable profitability, and resilience against uncertainty. Over the past few years, data governance has become so important that many enterprises dedicate a C-suite position solely to managing and governing data.

This is a new phenomenon. Nearly 50% of publicly-traded companies with a Chief Data Officer appointed their first CDO between 2019 and 2022.

One of the primary responsibilities these executives have is establishing a data governance framework that enables enterprise data to generate value. They must then align people, processes, and technology to that framework in ways that help the organization achieve its strategic goals.

What Does a Well-Designed Data Governance Program Look Like?

Competitive enterprises must constantly gather feedback on day-to-day operations as well as long-term strategic initiatives. For a corporate data governance program to streamline this process, it must be established on multiple levels. A typical enterprise data governance system may include the following:

A Governance Team. Since core governance directives are informed by executive strategy and insight, the governance team is often led by a C-suite executive. In some cases, a Director-level leader is provided with a department and charged with reporting to company executives.

A Steering Committee. Data is embedded in nearly every aspect of daily operation, which means data governance must accommodate insights from many different sources. Steering committees are multi-disciplinary governing bodies that direct and prioritize data governance initiatives, while overseeing their execution.

Operational Data Stewards. Data stewards are responsible for the management and oversight of organizational data assets. They coordinate policies and procedures to ensure data is accurate, consistent, complete, secure, and discoverable.

Accomplishing these goals requires a broad skill set that includes data architecture, data modeling, and information security. A comprehensive data governance program consists of 10 different components:

People

The success of any data governance initiative relies on the expertise and professionalism of the executive leaders, steering committees, and data stewards who execute it. Hiring top talent with demonstrable data governance expertise can make a significant difference in every aspect of the governance framework.

Strategy

High-level enterprise requirements must be comprehensively described in a strategic document that guides the governance roadmap. Establishing a strategic roadmap protects against scope creep and keeps the governance team focused on high-priority goals.

Processes

Data management processes include monitoring the quality of data, sharing analytics insights, and tracking data lineage, among many others. These processes are key to unlocking the value that enterprise data represents, and help inform policies that augment that value.

Policies

Data policies start with high-level statements declaring the expectations of data governance processes. They then inform lower-level operational guidelines about how individual users should treat data on a day-to-day basis. Policies are guided by security best practices and regulatory compliance, as well.

Standards

Documented, standardized processes keep “tribal knowledge” out of the data governance framework, ensuring everyone knows exactly how to format and communicate information effectively. ISO 3166 is an example of a popular, international data standard that specifies the definitions of country codes, as well as their dependent territories and other subdivisions.

Security

Data must be accessible to the people who need it, and inaccessible to those who don’t. Information security policies play an incredibly important role informing how governance policies differentiate between authorized and unauthorized users, and how suspicious activities should be handled.

Communication

Digital communication is a major element of data governance, but not the only one. Written and spoken communication also fall under governance frameworks, especially when considering sensitive data that requires additional security. Employees must know what format to use when communicating information, based on the context and sensitivity of the data being transmitted.

Integration

Organizational policies and structures should fit the overall framework established by data governance initiatives. Even internal office culture and water-cooler talk (or the lack thereof) must be taken into consideration when establishing new standards of behavior.

Analysis

To capture the value of data governance initiatives, leaders must analyze the right key performance indicators and business metrics. These are unique to each organization, but often include insights drawn from support tickets and security event logs, among other sources.

Technology

All data governance programs rely on various technologies to capture, manage, and analyze metadata in accordance with a governance framework. The larger and more complex the program is, the more robust its technology stack needs to be.

Each of these components has to work together smoothly in order to generate value with consistency in the enterprise environment. Poorly implemented data governance solutions can deeply impact the user experience for employees, customers, and business partners alike.

Managing the Flow of Data is Crucial to Effective Governance

Many data governance technologies focus on establishing consistent, secure frameworks for managing data and reducing organizational silos. This is an important aspect of overall governance, but it only produces optimal results when combined with an optimized approach to controlling data flow throughout the organization as well.

Data stewards and their governance partners need to be able to track the lineage of data as it travels through the organization. The ability to investigate how data arrives at its destination, and examine the processes that take it there are incredibly valuable for meeting information security and compliance objectives.

Enterprises and institutions that handle extremely high volumes of data throughput on a daily basis cannot adequately conduct these investigations in real-time without automated data lineage reporting. Data visualization solutions like DataHawk can help stewards, steering committee members, and executives gain visibility into how data flows throughout the organization and why.

Discover how to improve the efficiency and accuracy of your data lineage reports using a highly automated data governance platform with advanced search functionality. WeBridge provides governance technologies like DataHawk to enterprises that demand advanced, secure data lineage management solutions.

Data is necessary for your business to thrive. With so much to focus on, it might feel impossible to find the time to figure out exactly how well your data is working for you.

Data is most valuable when the company understands its origins, how it got to their business, and how it moves through the company. Data lineage analyzes data sources and where they are used, so that managers know if there are any problems or inefficiencies.

Let’s look at what data lineage is and explore how crucial it is.

Understanding data lineage

Data can be traced back to its origins, and all the stops it had within that journey. This makes documenting anything easier, for instance what is being used on a day-to-day basis in every system or to fix errors.

Data lineage vs. data provenance

A record keeper for data’s historical origins, data provenance is a tool that provides an in-depth description of where this data comes from, including its analytic life cycle. The dataset’s origin has been recorded and the quality assessed by using machine learning technology.

Using data provenance helps track errors, update processes and identify sources. It also sorts data in a data warehouse and identifies audit trails for governance. Data Lineage is considered “why-provenance” and centres around the flow of data.

Data provenance provides the ability to track data, which ensures its reliability.

The importance of data lineage

Increasing data streams require new ways to sort through and manage these large amounts of information. A Data Lifecycle provides access to information, which aids in decision-making and company development.

Source tracking can facilitate error resolution. Data quality will be enhanced by knowing who made a change, how something was updated, and which process was used. Data lineage gives people confidence in the data they’re using.

Businesses need data. Every department – such as marketing, manufacturing, management, and sales – relies on your company’s data. Collected data helps refine design, improve product availability over time, and make decisions about products and sales. With detailed data lineage, businesses can also have continuous self-education around products if needed.

By using data lineage, companies can track when data changes, and adjust accordingly. This enables firms to properly use data in increasingly changing environments.

Business owners or IT professionals with teams who need to create new programs should have data lineage tools on hand. This allows them to do more of what they need by finding the data sources needed and providing a comprehensive list.

If you’re looking for the data, who created it and what systems it entered the system in, Data Lineage can help. It’s a way to trace the data back to its origin: making differentiations on how that data is being used — helping reduce risk.

For firms operating in the healthcare and finance industries, rigorous regulatory reporting and transparency have become priorities. They need to be able to show that they are using accurate data, and they must also maintain its lineage. This means recording where data came from, when it was accessed by whom, who created it and how it was used.

Learn about the cloud and the future of data lineage

The internet has made it easy to gather and access data, but difficult to manage.

The cloud makes data governance important, because it helps businesses to understand their data and make the most of it. Data lineage is one of the ways that data governance is done. It gives businesses a way to check the quality of their data, ensuring accuracy and saving time.

Data lineage will become increasingly important as the cloud evolves. Although data governance efforts protect data and gives companies better insight into their technology, it also grows in size, affects governance policies and slows down data access which can affect time to market.

Does your organization have the skillset to input data as it comes in? This new, reactive approach to working will give you critical situational insights so that you can make more informed decisions.

New systems have huge potential for errors so data lineage plays an important and effective role. By understanding where data comes from, there is more transparency which can tackle governance and accuracy problems head on.

A data lineage solution reduces the cost and complexity of using cloud storage. It also provides scalability, data quality, a simple data exchange system for collecting multiple sources of information, and spaces to store all of your data.

Where to start with data lineage

Data lineage is a data governance strategy that will help you to understand how data flows through your system. The General Data Protection Regulation (GDPR), which took effect in May of 2018, requires companies to create a data sound environment for their clients.

Data lineage is the best way to ensure data quality, although it may be tedious and time-consuming.

Don’t waste time and money sorting through a data system. Instead, there are comprehensive solutions that can easily sort your data automatically.

A better data lineage solution

Most companies use ETL-centric data mapping definition document for data lineage management. This is where DataHawk is different.

DataHawk is a data lineage management solution that automatically collects and analyzes data lineage of mission critical data – visualizing data flow and derivation rule from data source to target.

To learn more, click here.

Category: Data Lineage

Automated Data Governance Unlocks the Value of Enterprise Data Flow