The real cost of poor data

Want to harness the promise of analytics, AI, and automation? Sure you do. We all do. But these ambitions are often undermined by a hidden adversary: poor quality data.  

Inconsistent, incomplete, or improperly formatted data can undermine trust in business intelligence platforms and misinform business decisions. Poor quality data leads to costly errors at every level. When your data is not clean and consistent, even basic AI and analytics projects struggle to deliver reliable results, creating setbacks in your operations and missteps in your strategy.

When being a market leader relies on a data-driven strategy, making sure data has a positive impact on your business (as opposed to a negative impact) relies on its quality. In this blog, we will discuss the real cost of poor data, how it hinders data and AI/ML projects, and how you can remedy that.  

Do you know where your data is stored?

If you lack up-to-date information about the data within your organisation, including its storage locations and methods, your organisation may not be ready for AI initiatives. Incomplete data models, or having only a partial view of where and how your data is stored mean your AI project will be built on shaky foundations.

Some data sources in your IT environment may exist in silos, making it difficult for those overseeing data governance and management to be fully aware of all available data.

To remedy this, conduct a comprehensive, organisation-wide data inventory to ensure data models are thorough and complete.

The risks of poor data hygiene  

If your data is inconsistent or low-quality, platforms and AI models that use it can produce inaccurate results. Data gaps and poor quality data harm daily operations and strategic decisions, causing wasted time, errors, and frustration with AI tools and platforms. With generative AI and machine learning, these issues are amplified, leading to unreliable outputs that you may not even realise are occurring.  

Internally, inefficiencies, inaccuracies, and employee frustration may arise, gradually diminishing confidence in your data efforts and reducing engagement with data platforms. Externally, these same challenges can lead to reduced customer satisfaction and/or revenue due to diminished performance, or poor business decisions made from inaccurate data.

Ultimately, users will not trust your AI if it is not effective, accurate and reliable.

The practicalities of cleansing and standardising your data

Disparate data structures and inconsistencies between data sources can significantly impair the performance and reliability of the new platform.

For example, when building a data platform using information from two different databases such as Salesforce and ServiceNow, each system may have its own schema or model for structuring data. Customers might be identified differently: Salesforce may use “account.id” while ServiceNow uses “contact.sys_id”. Additionally, there may be discrepancies in customer information, such as one system using “ACME” and another using “ACME Ltd.”. These differences present challenges when consolidating and managing data across systems.

Such challenges commonly arise due to the absence of established best practices and insufficient training in accurate data entry, storage, and management across the organisation.

There are two possible approaches to addressing this issue:

1. Use data governance controls across the organisation, so that data owners can agree on standards to resolve inconsistency issues with data.
2. Extract all relevant data into a central warehouse and standardise the input fields. Through data cleansing, your input fields can be unified under a consistent label, ensuring data uniformity across platforms. Subsequently, use data contracts to help establish a common understanding between teams and systems thereby improving data quality and reliability.

Establishing a data strategy and promoting best practice

Achieving high data quality and consistency can be difficult if your organisation hasn't had a comprehensive data strategy or standardised data entry practices. Disconnected systems, siloed teams, and poor standards for data consistency all mean a significant effort is needed to make sure data is suitable for its intended use.

Some inconsistencies result from human error, but many originate from a lack of organisation-wide best practices regarding data consistency and a lack of training on why this is important.  

To address this, you need a data strategy that incorporates company-wide standards for data input and maintenance. Employees should be informed of how poor data practices may impact business outcomes. Training and incentives for employees to accurately capture, input, and validate their data can help maintain consistency throughout the organisation’s systems. As data influences decision making, readiness and reliability are closely tied to organisational progress and effectiveness.

Establishing a culture that prioritises data integrity is crucial, as it encourages adherence to best practices and minimises inconsistencies in data. While we’d all like widespread data literacy, the reality is that most businesses contend with large, inconsistent datasets, even in critical operational areas.  

It is important to note that large-scale data cleansing initiatives are inherently complex and resource-intensive, especially without specialised expertise and tools. Partnering with a managed service provider is often necessary to achieve thorough data cleansing, thereby preparing an organisation for successful data-driven initiatives.

Make use of data platform and database management capabilities  

In addition to data cleansing, managed service providers offer essential support for the ongoing management and optimisation of your data platform or database. For instance, database-as-a-service (DBaaS) enables organisations to delegate configuration, administration, and management responsibilities to reliable experts.

Using DBaaS alleviates the burden of overseeing complex systems internally and mitigates the risk of potential data-related issues, including diminished ROI resulting from limited adoption, revenue loss attributable to inaccurate data analysis, and possible breaches of data security or privacy.

Another method that companies can use to alleviate the problems caused by poor data is to invest in data engineering services. A third-party provider may offer management and monitoring as part of their data engineering services, so you can leverage expert data engineers for the smooth running of your data platform.

The bottom line

Data is rarely entirely clean or consistent. In many organisations, the volume of data, the diversity of systems, and the length of time over which data has been collected by various individuals make comprehensive cleansing and consolidation unrealistic.

However, the quality and accuracy required for data depend on its intended use and the platform involved. Efforts to improve data quality should be guided by specific project requirements, ensuring that data meets its purpose on a case-by-case basis.

Find out more how we can help you get more from your data, or contact us below.