Understanding the causes of data debt can help organizations reduce the amount they carry and avoid creating additional data debt. 

What is Data Debt?

Data debt describes the build up of data-related issues over time. This includes – but is not limited to – issues such as data quality, capacity and security. Without a strategy to better govern data and reduce data debt, the build up of data debt is inevitable.

Enterprises collect and generate massive volumes of data as they expand their IT ecosystem by introducing new requirements, solutions, and capabilities.

Naturally, the volume and variety of data organizations collect tends to grow over time, making data more difficult to manage and govern.

This rapid data growth can overburden storage capacities and degrade system performance if left unchecked.

As additional teams, systems, and functions come into play, they start pushing more and more data into the system. Particularly when data and processes are unorganized and undocumented, rapidly piling data inevitably leads to data debt.

Prevention is always better than treatment, but for organizations already burdened with high levels of data debt, there are some tried and tested solutions

The lack of reliable data management and governance processes can make data inaccurate, hard to locate, and difficult to interpret.

This is especially true for data that is replicated or reproduced in a number of different systems requiring different processes and data formats.

So introducing solutions such as automated integrations can ensure consistency in how data is distributed around the enterprise. 

The Common Causes of Data Debt

The leading causes of data debt are:

  • Dark data
  • Duplicate data
  • Outdated data
  • Poor data quality
  • Enterprise solutions and applications that generate lots of data

Below, we address why these issues are common causes of data debt.

1. Dark data

According to Gartner, dark data is “The information assets organizations collect, process, and store during regular business activities, but generally fail to use for other purposes (for example, analytics, business relationships, and direct monetizing).” 

When data is not in active use, organizations often neglect its management. But even when data is not being actively used – is “dark” – within the enterprise, it can still contribute to data debt and should be managed accordingly. 

As well as data created as a byproduct of other enterprise functions, data retained for compliance purposes is also a common source of dark data. 

For Software-as-a-Service solutions where data storage requirements influence pricing, storing such data adds to costs and doesn’t create value.

And while some data may seem irrelevant today, there may be insight to be gained or a requirement to protect/use it in the future.

As such, it’s best practice to apply good management and governance practices to all stored data.

Extracting data from solutions and replicating it within data stores such as databases and data warehouses gives organizations more control over their data.

This can help organizations put data that is currently “dark” into good use, or better manage dark data so that it doesn’t significantly contribute to data debt.

Automated integration solutions can ensure such extraction and replication into an external database is a repeatable process – helping prevent data debt buildup. 

2. Duplicate data

Aside from the obvious storage and system performance issues, duplicate data can affect how data is used.

From misinformed analysis and decision-making to challenges concerning data availability, duplicate data is a burden on the enterprise. Numerous factors increase the amount of duplicate data. 

For instance, manual data entry and/or replication processes can lead to data debt. Incorrectly mapped integrations are also a common source of duplicate data.

In both cases, a well-implemented, automated solution for moving data can limit the potential for duplication. 

3. Outdated data

Business decisions based on obsolete data can be just as bad – or worse – than those made without data. Since data is ever-evolving, keeping company databases updated is essential to prevent wasting time and money.

Redundant, old, and unused data take up storage space. If the data isn’t managed and secured correctly, the organization is at an increased risk of breaches and non-compliance. 

For industries where standard regulations demand companies store data for a defined period, firms can adopt storage options that don’t negatively impact performance or cost.

Migrating data to external databases via integrations gives organizations more flexibility and control over how they manage old data.

4. Poor data quality 

Sound business decisions depend on data quality. When organizations rely on poor-quality data from unreliable sources, it impacts their analysis, decision-making, and customer relations. 

Manual data entry is a common cause of poor data quality stemming from human errors. Using multiple tools/databases without automation to keep them synchronized leads to a reliance on manual and generally poor data entry practices. 

Using poorly maintained integration solutions can also lead to poor data quality. API errors can cause complete integration failure or create consistency issues like irregular, missing, or misplaced values in the database. 

Organizations that want to avoid this can benefit from working with integration-as-a-service providers with expertise in the field, to maintain integrations on the organization’s behalf.

5. Enterprise solutions and applications

Business-critical solutions such as ITSM tools (including ServiceNow) are significant sources of data debt.

They are also adversely affected by data debt. Tools like ServiceNow generate enormous data volumes requiring strict data governance and management guidelines. 

Examples of data debt in enterprise applications include:

  • Poor data availability 
  • Performance degradation when exporting large amounts of data for reporting/analysis, delaying reporting
  • Data quality issues caused by manual data processes or broken API/integrations

Choosing the correct integration solution to automate the extraction and replication of data between systems can help pay down these examples of data debt.

Keep Your ServiceNow Data Debt in Check With Perspectium

The ability to extract and replicate large amounts of ServiceNow data is essential for tackling and managing data debt in the platform. 

Extracting and replicating ServiceNow data to an external database can help organizations: 

  • create integrations between ServiceNow and other technologies
  • make use of data that would otherwise be “dark”
  • reduce the burden on storage capacity and performance issues related to large data volumes
  • ensure consistency in how data is moved and reduce the reliance on manual data processes

Perspectium is an integration-as-a-service solution for ServiceNow that can help organizations manage and reduce their data and technical debt.

The service – delivered via a native ServiceNow application – allows ServiceNow users to extract and replicate massive amounts of ServiceNow data without affecting instance performance.

Organizations can use Perspectium DataSync to extract and replicate data to an external database for archiving, or to create integrations between ServiceNow and third-party applications

Perspectium ServiceBond enables synchronization – or “eBonding” – between ServiceNow and other tools supporting IT Service Management and Service Desks, such as Azure DevOps, AutoTask, Jira, Ivanti, AWS Support and other ServiceNow instances. 

Perspectium’s is a trusted name and service provider for ServiceNow data extraction, replication and synchronization.

Perspectium’s founder – David Loo – was also a founding developer of ServiceNow, and used their intimate understanding of the platform to create Perspectium’s performance-impact free technologies.

Because of this, many organizations – including ServiceNow themselves – use Perspectium’s applications and service to replicate and extract ServiceNow data. 

So if you need help with a ServiceNow integration or data extraction project, speak to us, the experts

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