An alarming 66% of organizations report that at least half of their enterprise data remains unused or "dark," according to Google Cloud’s 2024 Data and AI Trends Report.
It’s a prevalent issue that not only poses significant risks in terms of your security and compliance but also represents a massive loss in potential insights that could drive innovation and efficiency in your organization.
To make sure your business doesn’t become part of this statistic, start by identifying the signs of poor data quality. Whether it's inconsistency, difficulty in accessing data, or clear inaccuracies, each issue can lead directly to poor decisions and inefficiencies in your business operations. This guide will break down these signs and provide tips to improve data management.
High-quality data is critical because it directly impacts decision-making processes across all levels of an organization. Accurate data ensures that business strategies are based on factual, reliable information, leading to better outcomes and minimizing risks associated with poor data, such as faulty forecasts and misguided business strategies.
Additionally, data quality affects the operational efficiency of your business. Systems optimized with high-quality data perform better, as they can more effectively automate processes and reduce the need for manual interventions, which are often prone to errors.
There are several signs of bad enterprise data that can help you uncover issues before they become major roadblocks for your business.
Inconsistency in data across various systems is a prevalent issue in enterprises that can significantly undermine the reliability and usability of business information.
Enterprises typically use multiple systems for different business functions — CRM systems for sales and customer data, ERP systems for operations and finance, and various others for different purposes.
Each system might use different formats, standards, or even definitions for the same data entity. One system might record customer names in a 'First Name, Last Name' format, while another might use a single 'Full Name' field.
Lack of standardization is another issue. Without standardized data entry guidelines across systems, discrepancies easily creep in. This can be as simple as using "Rocal Ltd." in one system and "Rocal Limited" in another, leading to duplications and mismatches in reporting and analytics.
If you ever implement any kind of migration or upgrade, you could face issues too. When transitioning from legacy systems to more modern solutions or migrating data to the cloud, inconsistencies can occur if data is not properly cleaned, mapped, and tested.
High levels of manual data entry can stem from several sources and have far-reaching implications on your overall business performance. A lack of automation tools when it comes to data management can keep your business in the past. Many organizations still rely on manual input for data gathering and entry into databases, spreadsheets, or other systems.
Not putting enough attention to making sure your systems are integrated properly can also encourage you to resort to manual data entry. When enterprise systems are not fully integrated, data must often be re-entered into multiple systems manually — not only increasing time and effort but also increasing the likelihood of errors.
Frequent data-related errors are a common challenge in many organizations, significantly impacting data quality and reliability. Data errors can stem from various sources and manifest in multiple forms, each affecting data usability and decision-making processes.
One common source of such errors is human error in data entry. This usually occurs when data is manually entered into systems, leading to potential typographical mistakes, data omissions, or misinterpretations of the intended data formats.
Then there is data decay — where data becomes outdated or irrelevant — particularly contact details in marketing databases, like email addresses or phone numbers. Duplication of data entries and corruption during data migrations, especially from older legacy systems to newer platforms, can cause these types of issues.
Despite the significant advancements in data collection and analytics technologies, many organizations still struggle to leverage data effectively for decision-making. Decision-makers often doubt the accuracy, completeness, or reliability of the data available to them.
This skepticism can often be justified, especially due to previous experiences with data errors, inconsistencies, or outcomes that did not meet expectations. When leaders don't trust the data, they are more likely to rely on intuition or past experiences rather than data-driven insights.
Not every decision-maker has the necessary skills to interpret complex data sets or to understand the statistical methods used in data analysis. Without a high level of data literacy across the organization, the data collected can be misinterpreted or underutilized, leading to decisions that are not backed by solid evidence.
Compliance issues and security gaps present significant challenges for organizations. These challenges stem primarily from the need to adhere to various regulatory requirements while securing sensitive data against unauthorized access and breaches.
One of the major compliance challenges involves ensuring that data handling practices conform to legal standards such as GDPR in Europe or HIPAA in the United States. These regulations mandate stringent controls over how personal data is collected, stored, and used.
Security gaps often occur when organizations fail to implement sufficient data protection measures. Potential problems can include inadequate encryption, poor access controls, or the use of legacy systems that are vulnerable to cyber-attacks.
For businesses struggling with poor quality enterprise data, the first step towards fixing it involves acknowledging the problem and understanding its scope and impact on current operations.
It is essential to conduct thorough audits of your existing data, identify the sources of inaccuracies, and understand the underlying issues contributing to these inaccuracies, whether they be human errors, outdated processes, or inadequate data management systems.
Once the issues are identified, you can begin cleansing your data. This process involves removing or correcting data that is inaccurate, incomplete, or irrelevant, which can help in enhancing your data quality. Implementing robust data governance practices can also go a lone way to ensure ongoing data quality and security, and clearly define who is accountable for how data is used and maintained.
If all of this looks like a daunting effort, you can turn to experts to help take some of the pressure off. Promevo’s Cloud Health Check services play a pivotal role in improving cloud environments. These services are designed to give your business a comprehensive analysis of its current cloud infrastructure and figure out how to use your data to its full potential.
Promevo's approach includes evaluating critical areas such as budget alerts, project structure, identity and access management, and the efficiency of data pipelines. Not to mention, by granting Promevo temporary read-only access to your Google Cloud Platform (GCP) environment, you can benefit from an expert assessment conducted by certified Google Cloud Architects.
Contact us today to learn more about getting your Cloud Health Check and getting on the fast track to making more effective data-driven decisions with your enterprise data.
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