Data lineage is the comprehensive tracking of data usage within an organization. This includes how data originates, how it is transformed, how it is calculated, its movement between different systems, and ultimately how it is utilized in applications, reporting, analysis, and decision-making.

With the increasing complexities of business technology, data lineage analysis has become essential for most organizations. This article provides an overview of the fundamentals, importance, uses, and challenges of data lineage.

The Fundamentals of Data Lineage

Data lineage facilitates improved data transparency, quality, and consistency by enabling organizations to track and understand the complete lifecycle of their data assets. It helps with decision-making when sourcing and using data. It also helps with transforming data, especially for larger organizations with mission-critical applications and intricate data landscapes.

There are several factors to consider with data lineage:

• ‍Origin: Where did the data originate? The origin might be an application, a database, or a spreadsheet. It could come from another part of the organization or a third-party source.  ‍
• Flow: How has the data moved across different databases, files, APIs, and internal and external business systems over time?‍
• Transformation: Data typically undergoes multiple changes over time due to changes in representation, cleansing, merging with other data, or when the data is generated by or used in a calculation. The changes can also come from data conversions, including ELT (extract, load, transform), ETL, and Reverse ETL processes.‍
• Destination: Where is the data now? Does it reside in an application database or data warehouse. Is it used in a report or an analysis? Has it been sent outside the organization?  It may be stored in multiple places.  

The Importance of Data Lineage

Data lineage plays a key role in keeping data valuable and effective in a business setting. Here are a few ways that data lineage can deliver benefits to an organization.

Transparency and Trust

Data has incredible value in an information age. To realize the full value, data must be accurate and accessible. In other words, it becomes trustworthy only when it can be understood by anyone using it, and when the processing steps keep the data accurate. Data lineage provides transparency into the flow of data. It increases understanding and makes it easier for non-technical users to capture insights from existing datasets, especially for aggregated or calculated data.

Compliance and Regulatory Requirements

Data management regulations are becoming more stringent each year. Regulatory standards are tightening, and effective data management is becoming increasingly important. Data lineage can help organizations comply with GDPR, CCPA, and other data privacy laws. The transparency of data lineage makes data access, audits, and overall accountability easier. Accurate data lineage is crucial for demonstrating compliance with regulatory requirements, thereby mitigating the risk of project delays, fines, and other penalties.

Data Governance

Data lineage enables stronger data governance by providing the data to monitor, manage and ensure compliance to issued standards and guidelines. Because data lineage offers traceability of origin, flow, transformation and destination, it allows businesses to improve data quality, reduce inconsistencies and errors, and strengthen data management practices.

Improved Data Quality

Data lineage allows companies to trace the path of data from its current form back to its source. Data lineage offers a transparent record, facilitating the understanding and management of data variability and quality throughout its journey, and ensuring reliable data for decision-making. This is particularly relevant for companies modernizing existing systems.

Facilitating Collaboration Across Teams

With data lineage, trust in data accuracy and accessibility, improved data quality, and stronger ability to govern data all triangulate for better collaboration across teams. Data lineage avoids data siloing and facilitates interdepartmental activity. When data engineers and analysts utilize the same set of data, it fosters cross-functional teamwork and minimizes errors due to bad or in consistent data. Data lineage encourages a sense of unification as team members across an organization work from the same, trusted data.

Real-world Applications of Data Lineage

There are multiple ways that data lineage can add business value to organizations.

Use Case 1: Data Migrations

Zengines has invested in data lineage capabilities to support end-to-end migration of data from existing source systems to new target business systems. Data lineage is often the first research step required to ensure an efficient and accurate data migration.

Use Case 2: Improving Data Analytics

Data lineage exposes data quality issues by providing a clear view of the data journey, highlighting areas where inconsistencies or errors may have occurred. This makes it easier to engage in effective, detailed data analytics.

Consider, for instance, a financial services company with decades-old COBOL programs. Data lineage provides insights for organizations trying to replicate reporting or other outputs from these aging programs.

Use Case 3: Troubleshooting and Root Cause Analysis

Data lineage makes it easier to identify and trace errors back to their source. Finding the root cause of an error quickly is extremely valuable in a world where time is at a premium.

Use Case 4: Enhancing Data Security and Privacy

An important aspect of data security and privacy compliance is keeping data safe guarded at all times. Data lineage provides an understanding of the data life cycle that can show information security groups the steps that must be reviewed and secured.

Comprehensive data lineage makes it easier to demonstrate compliance with data privacy regulations.  For example, Banks and Payments Processors are subject to GLBA (Gramm-Leach-Bliley Act), PCI DSS(Payment Card Initiative - Data Security Standards), EU GDPR (European General Data Protection Regulation), and many other regulations that protect Personally Identifiable Information (PII). The knowledge of how any data element is used allows it to be protected, masked, or hidden when appropriate.

Use Case 5: Implementing Data Mesh and Data Fabric

Data Mesh and Data Fabric are advanced data architectures that help to decentralize data and integrate it across diverse data sources. Understanding the data lineage allows data management teams to make trustworthy data available to Data Mesh / Data Fabric consumers. Data lineage makes it possible to determine the correct data to store and use for a given purpose (decision making, analytics, reporting, etc.). Data lineage is typically part of any new Data Mesh / Data Fabric initiative.  

Challenges in Data Lineage

Data lineage is useful but can also face challenges. Here are a few potential issues.

Data Complexity and Fragmentation

Siloed data continues to be a major hurdle for tracing business data across departments and organizations. Consider when a security trade is being made. The security details are usually maintained in a reference data / Master Data Management application. The bid / ask information comes from many different market vendors and is updated continuously. The trading application computes the value of the trade, and any tax impact is computed in an investment accounting application. Is the same data being used across them all? Do they use different terminology?  Do the applications all use the same pricing information? For accurate reporting and good decision making, it is vital that the same data is used in every step.  

Mapping data lineage in increasingly complex environments is also a concern. Things like on-site and cloud storage, as well as remote, hybrid, and in-person work environments, make data complexity and fragmentation a growing issue that requires attention.

Resource Intensive

Historically, capturing and maintaining data lineage has been resource-intensive work performed by analysts with a deep understanding of the business. Given the quantity of data and code involved, a manual approach is prohibitively expensive for most companies. Most software solutions provide a partial view, only showing data stored in relational databases or excluding logic found in computer programs.

The best option is to find a balance between manual and automated solutions that enable cost-effective data lineage frameworks.

Evolving Data Systems

Data lineage is more than a backward-looking activity. Organizations also need to maintain up-to-date lineage information as systems are changed and replaced over time. In an era of constant change, data lineage teams are challenged to incorporate new forms of data usage or data transformation.

Investing in Data Lineage

Data lineage is becoming a critical part of any company’s data management strategy. In an information age where data and analytics are king, data lineage enables companies to maintain clean, transparent, traceable datasets. This empowers data-driven decision-making and encourages cross-collaborative efforts.

Data lineage addresses a central part of business operations. It provides a powerful sense of digital clarity as organizations navigate increasingly complex tools, systems, and regulatory landscapes.

Forward-thinking technical and non-technical leaders alike should be encouraging their organizations to improve their data lineage strategies. Investments in data lineage result in a valuable new data assets that provide greater business agility and competitive advantage.

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As a fellow SaaS CEO, I understand that building and delivering a positive reputation and brand drives revenue. On the surface, this sounds like a simple and relatively straightforward task: provide a good product, take care of your customers, and deliver on the promise you made to customers. What I discovered as an executive at Accenture is that no matter how capable your SaaS product is, if you can’t effectively and efficiently onboard a customer, the reputation and revenue from your product are at significant risk. This is why I cofounded Zengines.

The Onboarding Devil is in the Data Conversion Details

Data conversion is rarely the first concern for a CEO, but understanding how this seemingly “in the weeds detail” is a strategic risk for your revenue is the purpose of this blog. We conducted a survey, and two major items jumped out:

1. 68% of customers say converting data from old systems is the #1 challenge for moving to new platforms.
2. Over 90% said they are converting/consolidating data from multiple systems when they deploy a major new SaaS platform.

The “so what” is your reputation and revenue are at risk when you (or your partner) do not successfully onboard your customer’s required data to your SaaS platform to deliver the expected value to your customers. Three fundamental issues complicate the task of data conversion:

1. Manual guesswork: Data from multiple systems must be mapped and aligned in format or structure to be directly imported to your SaaS platform, this is manual, slow, and laborious guesswork.
   
2. Lack of experienced resources, especially when you need them: The skill sets and people required to accomplish this critical task are rare resources and are only scalable with some automation assistance.
   
3. Lack of repeatability: Because data conversions are a detail-oriented task requiring contextual understanding, onboarding teams will recreate the wheel each time.

Why Data Conversion is a Good Task For AI

We started using AI at Zengines long before AI became the year's buzzword. Pattern recognition and anomaly identification are at the heart of the data conversion process, which is ideal for an AI-based tool. Zengines AI helps you accelerate and scale onboarding, whether your professional services teams, a Big Four consulting firm, or another partner is implementing your platform. Our system automates the data mapping and transformation rules, identifies anomalies, lets the experts confirm and augment the transformation changes required, and executes the data conversion to create a successful onboarding process.

Data Conversion Drives Revenue Generation

Every legacy platform integrated into your platform is more revenue for your company. This can’t happen without the ability to quickly understand, move, and convert your customer’s data. Whether you get paid by the number of users (Salesforce.com), the number of transactions (Zapier), the number of modules implemented (HubSpot), or the amount of data you manage (Splunk), your revenue growth is limited if the data in legacy systems is not converted to your platform. This is why data conversion drives revenue generation.

Zengines AI-Driven Data Conversion to Accelerate Onboarding

Zengines helps your company deliver your value faster by accelerating onboarding, optimizing your professional services teams, and adding repeatability to the process.

• Accelerate Time to Value - Zengines is an end-to-end data conversion solution that gets the job of data conversion done so customers get to value faster.
  
• AI-based Data Conversion is 6X Faster - Zengines AI-based automation is 6X faster than traditional CSV file-based data conversion methods.
  
• Scale Your Limited Resources – Zengines makes data conversion a highly repeatable process to scale your team and optimize resources.
  
• Optimize and Automate Onboarding – Zengines AI automates the data conversion, including profiling, mapping, loading, and testing data conversion to optimize onboarding.

Conclusion

As CEO, your priority is delivering value to your customer in the best way to enhance your company’s reputation and top- and bottom-line. In that light, this is question you should be asking when it comes to data conversions: is your customer onboarding fast, and repeatable?  Zengines is here to achieve that with you.

Caitlyn Truong • Zengines, Inc. • CEO, CO-FOUNDER

Introduction

System migration is a necessary skill that modern business operations teams must master. Digital transformation programs are moving fast and require organizations to keep up with the evolving technological landscape. While migrating to a new system can offer numerous benefits such as revenue growth, increased efficiency, and adaptability, the process is challenging and requires significant staff time - often augmented with system migration consultants. This article will explore the top five tips business teams can use to ensure success for their system migration programs.

Tip 1. Gain user buy-in on how the new system will change their business processes

One of the most significant steps in a system migration project is a comprehensive understanding of the new software's requirements and how your business users may need to change how they work. The impact of changes will likely radiate beyond the new system you are implementing as data flows to downstream products and processes. Users need to see how the new system represents their data. Seeing familiar data within the new system helps with the configuration of screens and business needs. The following steps can help organizations address this challenge:

• Conduct a thorough analysis of the new system, including its technical capabilities, limitations, and integration requirements.
• Engage stakeholders from different departments to understand their specific needs and expectations.
• Develop a detailed migration plan that outlines the steps required to meet the new system's requirements, including data conversion, training, and process redesign.
• Setting expectations for timelines, reporting changes, compliance concerns, third-party risk management, process changes, staff training, and delivering on expected ROI (return on investment).

PRO TIP: Loading a first cut of data into the new system early can positively influence the buy-in from business users while helping them experience the new system with familiar data. Loading data early in a system migration will help with configuration and provide early visibility to owners of downstream systems and processes.

Tip 2. Efficient Field and Data Mapping for Seamless System Migration

The next step in the system migration is mapping the fields and their data from the original system to the new one. This process is not always straightforward and requires both the source and destination systems and the outcomes to ensure the project meets expectations. To streamline this step in the process:

• Create a comprehensive mapping document that outlines the relationships between fields and data elements in the original and new systems.
• Leverage advanced data conversion tools and methodologies to automate the mapping process, reducing manual effort and minimizing the risk of errors.
• Validate the mapping by conducting extensive testing and subject matter expert reviews to ensure accuracy and alignment with project expectations

PRO TIP: Data conversion platforms, like Zengines, dramatically speed up matching fields from source to target by applying artificial intelligence to solve the problem quickly and accurately.

Tip 3. Seamlessly Transform Data to Meet New System Requirements

Data transformation is a critical aspect of system migration, as the data from the original system may not be directly compatible with the new system. This challenge can be addressed by:

• Identifying the specific data transformation requirements, such as changes in data formats, units, or encoding.
• Safeguard compliance with reporting, regulatory, and process obligations, such as historical reporting, GDPR, TPRM (Third Party Risk Management), and executive reporting, using our data migration software. Optimize efficiency by developing and implementing data transformation scripts or leveraging cutting-edge data conversion tools, automating the process for swift and accurate migration. Prioritize data integrity by conducting rigorous testing and validation procedures, guaranteeing the accuracy and integrity of the transformed data.

PRO TIP: Transforming data has advanced significantly with data science toolkits and Python. End-to-end data conversion platforms will help automate this process with pre-configured business rules and algorithms.

Tip 4. Testing the Accuracy of Data Conversion

Ensuring your data conversion is accurate is vital for successful system migration. Inaccurate data can cause all sorts of headaches, from operational problems to unhappy users and even potential penalties from regulators. To ensure your reporting and operations meet expectations, follow these essential steps:

• Create a thorough test plan covering a range of scenarios, spanning data integrity checks, functional testing, and end-to-end testing.
• Optimize your testing process for data migration software by harnessing the power of automated testing tools and cutting-edge techniques.
• Establish an effective feedback loop with critical stakeholders to identify and resolve data conversion issues promptly.
• Select relevant stakeholders to validate jobs, processes, reports, and API feeds/links to enable end-to-end operations.

PRO TIP: Don't cheat on double-checking your data conversion; it's vital to avoid headaches down the line. Create a solid test plan covering all bases using automated testing tools for efficiency and engaging your stakeholders for quicker feedback. By getting the right folks involved in validating everything, you'll have all bases covered, ensuring a smooth end-to-end conversion for your operations.

Tip 5. Managing Change and Ensuring User Adoption

The success of a system migration hinges on users' enthusiastic adoption and seamless integration of the new system. Achieving success in system migration relies on effective change management strategies and user adoption. To fulfill these crucial requirements, it is imperative to:

• Develop a change management plan that includes communication, training, and support strategies to help users transition to the new system.
• Facilitate learning by providing comprehensive training and resources like user guides and video tutorials.
• Establish a robust support structure featuring responsive help desks and user forums, providing prompt assistance and addressing user concerns throughout the migration process.
• Ensure executives and key stakeholders can monitor progress and your proactive communications.

PRO TIP: Foster a positive attitude towards change and user adoption by highlighting the benefits and addressing potential concerns through frequent engagement and positive interactions in your communication and training materials, helping users feel more comfortable and excited about the transition.

Conclusion

System migration is a complex and challenging process that requires careful planning, execution, communications, and management. By understanding the requirements of the new system, effectively mapping fields, seamlessly transforming data, quickly testing the accuracy of data conversion, and adeptly managing change, organizations can successfully migrate to a new system. By doing so, they can unlock the value of their digital transformation programs and drive their organizations to higher levels of growth and effectiveness.

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Data conversions are far too manual

Companies have moved their data from old to new since the first computer system was retired. Enterprise software has advanced a lot since then, but the process for converting data hasn’t. IT teams and consultants still rely on essential tools like spreadsheets, scripts, and SQL queries. These are labor-intensive and require high-level expertise, extending the timeline for onboarding new applications. This fragmented approach to data conversion is time-consuming and inefficient, resulting in too many projects delivered late and over budget.

Zengines has a better way of converting data with a platform that automates the entire process from end to end. With Zengines, data conversion efforts are faster and less labor-intensive resulting in reduced costs and risk.

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AI-powered data conversion rapidly accelerates data analysis, mapping, and testing

Companies migrating to new systems often need more expertise in the new target system. In contrast, software vendors for the new system tend to shift the responsibility for data conversion onto the data owners. This creates a data conversion gap that is challenging to navigate. Questions that customers of new software often ask include:

·       How long will it take to convert the data?

·       Will there be a gap in the data compatibility and completeness between the two systems?

·       Is my source data sufficient to populate the target system

·       Will I be able to achieve the business goals we are seeking?

·       Do I need to enrich my data from other sources?

·       Can my target store all of my current data?

With Zengines, a data conversion project never starts from scratch. Our AI algorithms understand the requirements of the target system. They will automatically analyze the source system's table structures, relationships, and data to automate the data conversion process. The result is a tremendous head start with any data conversion projects and a better understanding of what is required to achieve your business objectives.

How Zengines uses artificial intelligence to accelerate data conversions

1. Analyzer: Connect to 45 different databases or ingest multi-format data sets from CSV, Excel, XML, PDF, or APIs
2. Analyzer: Classify tables and fields. Automatically build relationships based on primary-key/foreign-key relationships.
3. Analyzer: Identify quality issues within source data.
4. Mapper: Identify source-to-target field matching.
5. Mapper: Detect and resolve data type differences.
6. Mapper: Produce field-level logic where data needs to be transformed to meet the requirements of the new target system.
7. Loader: Integrated Testing and Validation

Zengines ML models continually improve with each new conversion, offering better results with each data conversion project.

Automating the data conversion process with the Zengines platform

Zengines provides an end-to-end platform, digitizing and streamlining each step of the data conversion process. This integrated approach simplifies the user experience, with inputs and outputs for each step in the process, and they seamlessly flow from step to step until all data is converted. Four technologies underpin the flow of data throughout the process:

1.     Zengines Data Catalog^TM – System migration projects need up-to-date, accurate metadata to identify critical patterns between the source and target systems. Zengines’ intelligent data catalog is rapidly constructed from all source and target system schemas and metadata generated by Zengines Analyzer, providing the foundation for data conversion analysis.

2.     Zengines Knowledge Graph^TM – The Zengines Knowledge Graph is the repository for training our powerful data conversion models. Our patent-pending technology uniquely builds domain-specific models for each industry and commercial software platform we have worked with. Customers benefit from prior data conversion efforts, making the algorithms smarter over time.

3.     Data Conversion Pipeline – On the Zengines platform, data flows through an integrated process: ingestion à cleansing à transformation à post-processing à loading of data, à reconciliation. Our Data Conversion Pipeline controls the flow of this data as it is prepared for the target system and enables rapid iterative cycles through the pipeline to refine outputs continuously.

4.     Collaboration and Control – System migration efforts involve many teams, people, and environments, such as Development, Testing, User Acceptance Testing, and Production. Hundreds or even thousands of decisions need to be made and tracked during these projects. Zengines Collaboration and Control module ensures that project users and resources stay organized and can operate from a common source of truth.

Why we load data fast, early and often

Companies migrating to new systems often need more expertise in the target system, while vendors tend to shift the data conversion responsibility onto the data owners. This creates a data gap that is challenging to navigate. To address this issue, it's crucial to adopt a target system-focused approach, starting with the new system's requirements in mind.

Target and downstream system vendors, product owners, and users eagerly wait to see their data within the new system resulting in project delays and waste. Zengines believes in loading data quickly, even if it's not initially perfect, to allow for iterative improvements in data completeness and quality and to drive overall project efficiencies.

Conclusion

Zengines offers an entirely new way of converting data. With an emphasis on speed and repeatability, Zengines combines artificial intelligence and automation to transform manual and time-consuming data conversions into an efficient and predictable process.

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Have you ever been involved in migrating a legacy computer system to a new platform? If so, you’ll agree it’s an amazingly difficult job.  

Whether switching vendors, moving to the cloud or undergoing a complex digital transformation, companies struggle with converting their data from where it lives to where it needs to be. Projects often take multiple years, cost millions of dollars, and typically run far over budget. Yet, despite the cost and urgency, the tools and methods for converting data are basic and not built-for-purpose; most heavy lifting is still performed manually with SQL scripts and spreadsheets. Even after systems migrations are declared finished, a lack of data analysis, testing and conversion tools can lead to low quality or incomplete data – resulting in rework or even failure after the new systems go live.  

With decades of experience and hundreds of projects under our belts, the team at Zengines knew there had to be a better way. In 2020 we founded Zengines to make it easier and faster for companies to move their data to new software platforms; our vision is a future where data conversions are frictionless and companies are no longer chained to their legacy systems and data.

Since our founding we have invested tens of thousands of hours into R&D solving the many challenges our customers face analyzing, mapping, and loading data into new systems. Zengines uses the latest in AI, machine learning models and data science to support the data conversion process end-to-end.

As companies and industries become increasingly dependent on software, their agility and speed of technology adoption will determine the winners and losers. For those companies looking for a competitive advantage, Zengines has developed an entirely new way of converting data.^TM  

Welcome to Zengines, and to the launch of our blog, Accelerator. It is our intent to share all the tips, technologies, and data conversion best practices we can offer. Our goal is to help your IT & business teams unleash their productivity in every way we can. To help, we have recruited our highly experienced data conversion consultants, product experts, engineers, and outside contributors to write articles for Accelerator. We welcome you to subscribe and we will keep you posted of articles and news.

If you have any tips or experience you’d like to share with the Zengines team, please email us at accelerator@zengines.ai or fill out our contact form on the website. We’re looking forward to hearing from you!

- Team Zengines

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Zengines has created an entirely new way of converting data. The Zengines AI platform automates the end-to-end data conversion process, reducing the time, cost and risk associated with data conversions, system migrations and data onboarding for a variety of business solutions. Zengines is used by leading enterprises, software vendors and consultants across multiple industries

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