Maximize Your Return On Technology Investments Focus On Data Quality First Inner

According to a report by Flexera, IT asset managers estimate that at least one-third of their IT budget is being squandered, on average. No doubt, maximizing return on technology investments is critical for organizations to remain competitive and achieve their business goals. By effectively leveraging technology, organizations can improve operational efficiency, reduce costs, enhance customer experience, and drive innovation. 

But at the same time, the failure to maximize ROI can result in wasted resources, lost opportunities, and increased vulnerability to disruption from more tech-savvy competitors. The underlying cause of the failure of these initiatives is the quality of data. But before delving into the technical details, let’s first comprehend what data quality entails.

Understanding Data Quality

Data quality refers to the degree to which data meets the requirements and expectations of its users. This brings factors like accuracy, completeness, consistency, timeliness, relevance, and usability to the mix. High-quality data is essential for making informed decisions, driving business performance, and achieving organizational goals.

Most importantly, data quality is critical for reliable and accurate decision-making. Poor data quality can accrue incorrect conclusions, wasted resources, and a damaged reputation. Ensuring data is complete, accurate, consistent, and timely is essential to minimize errors and ensure confidence in analysis and decision-making.

To that end, here are the key components that define data quality:

  • Completeness: Ensuring that all necessary data is present
  • Accuracy: Ensuring that the data is correct and free from errors
  • Consistency: Ensuring that the data is uniform and follows the same format
  • Validity: Ensuring that the data is relevant to the purpose for which it is being used
  • Timeliness: Ensuring that the data is up-to-date and reflects the current state of affairs
  • Integrity: Ensuring that the data is secure and protected against unauthorized access, modification, or deletion
  • Relevance: Ensuring that the data is useful and appropriate for the intended audience and purpose

How Good Data Quality Is Critical For Maximizing Return On Technology Investments

Often, businesses invest in technology (i.e., applications and platforms) to ensure seamless operations across different business workflows. However, these tools don’t run by themselves. They need high-quality data to yield outcomes that are in line with what the business needs to succeed amid soaring competition.

In fact, high-quality data is also crucial to make further investments in technology. This is because the accuracy and completeness of the data you use to inform your investment decisions directly affect the success of those decisions. Poor data quality can lead to erroneous or incomplete analyses, which can result in incorrect decisions and lost opportunities. 

For tools already at disposal, incorrect data could make the analytics, visualization, AI and ML, and more initiatives unsuccessful. Let’s understand this by categorizing the data across different themes.

1. Inaccurate Financial Data

If you use inaccurate financial data, you may overestimate or underestimate the profitability of an investment, an opportunity, etc. 

2. Incomplete Customer Data

If you have incomplete customer data, you may miss opportunities to target specific customer segments with relevant products or services. For instance, if you don’t have complete data on the demographics or buying behavior of your customers, you may miss out on opportunities to offer personalized promotions or deals, leading to reduced customer engagement and lower ROI.

3. Outdated Inventory Data 

If you have outdated inventory data, you may fail to replenish products that are in high demand, leading to lost sales and reduced ROI. 

4. Inaccurate Operational Data 

If you have inaccurate operational data, you may make suboptimal decisions that can negatively impact the performance of your business. Consider this; if you use substandard quality data to schedule employees or allocate resources, you may end up with inefficiencies or delays that can lead to reduced productivity and lower ROI.

Why Focus On Data Quality First?

Having gained an understanding of the obstacles, let’s now explore some of the advantages of emphasizing data quality earlier for maximizing the return on technology investments.

1. Improved Business Insights And Analytics

Improved data quality leads to more accurate and consistent data, which can provide deeper insights into business performance and opportunities. Accurate and reliable data can also enable more precise forecasting and trend analysis.

2. Enhanced Customer Experience And Satisfaction

Better data quality can help organizations provide more personalized and relevant experiences to their customers. Accurate and complete customer data can equip businesses to better comprehend customer needs and preferences, leading to more targeted marketing and better customer service.

3. Increased Operational Efficiency And Cost Savings

Gartner previously reported that poor data quality could cost businesses around $15 million annually. This would be even higher today. As it stands, data quality is central to streamlining operations and reducing infrastructure costs. Accurate and complete data can help organizations identify inefficiencies and opportunities for improvement, leading to more efficient operations and cost savings.

4. Mitigated Risk And Compliance Issues

Improved data quality can help organizations mitigate risk and comply with regulatory requirements. They can be better equipped to identify potential compliance issues and proactively address them, reducing the risk of regulatory fines or reputational damage.

So, How Can You Improve Data Quality? 

1. Data Profiling And Analysis

Data profiling involves analyzing data to gain insights into its structure, content, and quality. This can help organizations identify issues with data, such as inconsistencies or errors, and take corrective action. Data analysis tools can be used to automate the profiling process and provide insights into data quality issues.

2. Data Quality Tools And Platforms

Data quality tools and platforms can help organizations automate data quality processes, such as data validation and cleansing. These tools can also help enforce data standards and policies and provide visibility into data quality issues across the organization.

3. Establishing Data Governance Frameworks

Data governance frameworks provide a structure for managing data quality and ensuring that data is managed consistently across the organization. This can include defining policies, procedures, and roles and responsibilities for data management.

4. Enforcing Data Standards And Policies

Enforcing data standards and policies can help ensure that data is managed consistently and accurately. This can include:

  • Establishing guidelines for data entry
  • Ensuring data validation
  • Implementing processes for data quality control
5. Regular Data Audits And Monitoring

Regular data audits and monitoring can help organizations identify issues with data quality and take corrective action. This can involve regular reviews of data quality metrics, such as completeness and accuracy, as well as monitoring data usage and identifying potential issues with data quality.

The EnFuse Advantage 

As we’ve seen, the focus on data quality is critical to enhancing the value delivered out of the tools and applications at disposal. At EnFuse, we help businesses improve data quality by:

  • Identifying Data Quality Issues: Through our AI training data services, we empower businesses to identify data quality issues, such as poor data entry, lack of data standards, or inadequate data security. This helps them develop targeted strategies to address these issues.

  • Establishing Robust Data Management: From data profiling to enrichment and standardization to compliance, we ensure that businesses are equipped with high-quality data and robust data management procedures.

Interested in learning more? Connect with us here.