The challenges and benefits of data quality management in our advancing digital world

In today’s rapidly evolving business landscape, data has emerged as the lifeblood that fuels informed decision-making, innovation and growth. However, the sheer volume and complexity of data comes with its own set of challenges, so taking action is imperative.

In a report entitled 3 Steps to Improve IT Service View CMDB Data Quality, Gartner stated that “99% of organizations using configuration management database (CMDB) tooling that do not confront configuration item data quality gaps will experience visible business disruptions by the end of 2024.

Let’s examine the prevailing trends and challenges businesses face when seeking data quality management solutions, highlighting the critical role they play in ensuring accurate, reliable and actionable data.

The benefits of data quality management

Data quality management (sometimes called DQM) is the process of ensuring that data is fit for its intended purpose. It involves defining, measuring, monitoring and improving the quality of data throughout its lifecycle. Data quality management evolves with the changing needs and expectations of businesses and their customers. Here are some of the key trends on how it is being used — and the benefits that businesses can realize:

  • Data-driven decision making: Accurate data quality is no longer a luxury —but a necessity to ensure your business decisions are grounded in reality.
  • Advanced analytics and AI: As analytics and artificial intelligence become integral to business operations, the quality of input data is paramount. High-quality data fuels accurate insights and predictive modeling.
  • Data privacy and compliance: Data protection regulations are tightening globally. Businesses need data quality solutions to ensure compliance and safeguard customer trust.

Today, data is the lifeblood that fuels innovation and growth, but its sheer volume and complexity brings its own set of challenges — making decisive action critical.

  • Data integration from multiple sources: Data is coming from a diverse range of internal and external sources. Ensuring uniform quality as it is integrated becomes a vital challenge.
  • Cloud migration and data mobility: The shift to cloud platforms demands seamless data migration without compromising its quality.
  • Personalization and customer experience: Personalized experiences are now a competitive differentiator. Accurate customer data is pivotal in delivering tailored services that build lasting relationships.
  • Supply chain optimization: In the supply chain realm, accurate data ensures efficient operations, timely deliveries and cost savings.
  • Real-time data processing: The demand for real-time insights necessitates accurate, up-to-the-minute data — because any inaccuracies can lead to misguided decisions.

Clearly, data quality management is incredibly important in today’s business environment. However, DQM also poses significant challenges that must be addressed.

Challenges in data quality management

Businesses face various obstacles that hinder their ability to achieve and maintain high-quality data. Data can be inaccurate, incomplete or redundant, wasting resources and leading to misguided decisions and financial losses. There’s also an element of human error during manual data entry, which can lead to inaccuracies and affect the customer experience. Security concerns also come into play, as incomplete or inaccurate data can introduce vulnerabilities and compromise data security. These challenges underscore the need for effective data quality solutions.

How to tell if you have a data quality problem

There are signs and indicators that your company may have poor IT operational tools data.

  • Inaccurate decision making: When decisions based on the data consistently lead to unexpected outcomes or inaccuracies, it suggests that the data might be unreliable.
  • Discrepancies and divergence: If data from different tools or sources contradicts each other, it’s a clear sign of inconsistency and poor data quality.
  • Lack of trust: When IT teams, managers, or executives express doubts about the data’s accuracy, it indicates a lack of trust in the information.
  • Reactive measures: If teams are often caught off-guard by incidents management, change management, request management issues that weren’t foreseen by the data, it implies that the data is not adequately predictive.
  • Frequent IT disruptions: If IT disruptions are occurring more frequently than anticipated, it could be due to unreliable data impacting the quality of your Change Control practices.
  • Customer complaints: If customers experience service disruptions or inconsistencies, it could result from poor IT data affecting service delivery.
  • Audit failures: If audits reveal inconsistencies or inaccuracies in the IT environment, it’s a clear indication of poor data quality.

What to look for in a data quality management tool

Data quality solutions are tools or services that help enterprises improve the quality of the data that their businesses run on. There are many products in the marketplace, but none are designed to run in isolation. To meet the evolving challenges of data quality management, you need a strong product implemented by a partner with digital transformation experience and knowledge in your industry or domain. When properly implemented, the right product can address the complexities of data accuracy, compliance and integration — delivering data-driven decision making, real-time insights and personalized experiences.

The power of TFORM

TFORM is a specialized software for managing data quality in IT Operations (IT Ops). It integrates IT data — whether it comes from agentless scans or existing tools — into a single, reliable IT asset database known as the Single Source of Truth (SSOT). This database acts as a central hub for automatically identifying data quality problems across your entire IT infrastructure, including critical components like CMDB, ITSM tools like ServiceNow, and others such as SolarWinds, Commvault, McAfee and many more. TFORM also employs an AI-powered correlation engine to keep all your current tools up-to-date, and ensures they remain current over time.

If TFORM sounds like the right solution for you, let’s talk about how we can implement a TFORM-based DQM solution that ensures you are ready to navigate the intricacies of data management with precision — unlocking unparalleled opportunities for growth and excellence.

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