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When discussing data quality and accessibility, it is crucial for global retail brands to shift from siloed data management to an enterprise-wide approach. Integrating data across departments and regions not only improves transparency but also enhances the accuracy of analytics and predictive models, enabling better decision-making and more effective global operations. Here are 5 key factors every global retail brand should consider to maximize the value of organizational data.

Data quality and accessibility are foundational to successful Data Driven Decision Making (DDDM). Poor data quality or lack of accessibility can lead to incorrect insights, misguided decisions, a number of missed opportunities, and ultimately, business failures.  Simply put, as a business organization, we must ensure that we collect all the necessary data to support informed decision-making, and we clearly declare situations where accurate data is not available. The data should be gathered frequently, efficiently, and continuously, with a strong emphasis on its reliability, thus minimizing manual errors through automation and intelligent data validation. Additionally, it is essential that those within the organization who need the data for their work have easy access to these valuable corporate assets.

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Let’s delve into the intricacies of the challenges

A good way to achieve your goal is to eliminate all major issues that could prevent you from reaching it. Let’s go through them to identify what they are, their impact, and a recommended solution.

1. Data Fragmentation

 In many organizations, data is stored across multiple systems, departments, or even geographic locations. This fragmentation often occurs because different departments use different software or platforms, each tailored to their specific needs. For instance, marketing may use a customer relationship management (CRM) system, while finance relies on an enterprise resource planning (ERP) system, and supply chain management uses yet another platform. Each of these systems may store valuable data, but the lack of integration creates silos, making it difficult to obtain a comprehensive view of the organization’s data landscape.

Impact: Fragmented data leads to inefficiencies in data retrieval and analysis, as analysts may need to manually gather data from multiple sources, increasing the risk of errors. Additionally, it hinders the ability to conduct holistic analyses, as the data may not be easily combined or integrated.

Solution: Bringing departments with distinct needs together on a single platform is not the right approach. Each domain requires tailored tools, and pushing business units to use solutions that don’t align with their specific needs ultimately leads to a dead end. To address fragmentation, companies should implement data integration solutions, such as data warehouses or data lakes, that consolidate data from disparate sources into a unified repository. This enables easier access and analysis, though it requires careful planning to ensure that the integrated data remains consistent and up-to-date.

Recommended reading:

The Power of Data-driven Decisions in Retail  

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The Power of Data-driven Decisions in Retail  

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2. Data Completeness and Accuracy

Even when data is accessible, its usefulness depends on its completeness and accuracy. Incomplete data can arise from various sources, such as missing fields in data entry, truncated data due to system limitations, or loss of data during migration between systems or during aggregation. Inaccurate data, on the other hand, can result from human error, outdated information, or issues with data collection methods. The compulsion for completeness can create situations where we attempt to fill in missing data with estimates or from memory, which can cause significant disruptions in the system.

It is important to emphasize that, when it comes to data, we should never sacrifice accuracy for completeness.

Impact: Decisions based on incomplete or inaccurate data can lead to poor outcomes.It can also cause unexpected errors during system integrations. For example, if sales data is missing critical customer demographics, marketing campaigns may be poorly targeted, reducing their effectiveness. Similarly, inaccurate financial data can lead to erroneous forecasts, impacting budgeting and resource allocation.

Solution: Ensuring data completeness and accuracy involves implementing data validation rules and checks at the point of entry and throughout the data lifecycle. Regular audits and data cleansing processes can help identify and correct errors, but a more effective approach is to enhance system integration, which naturally reveals these issues. The key to successful integration is involving an expert who understands both retail operations and IT solutions.

3. Timeliness and Relevance

Data must be not only accurate and complete but also timely. In fast-paced industries, outdated data can be as harmful as inaccurate data. For example, relying on last quarter’s sales data to make real-time inventory decisions can lead to overstocking or stockouts. Timeliness is closely related to data accessibility; if data is not readily accessible when needed, it quickly loses its relevance.

Let’s take another example of the importance of projecting and anticipating problems early, rather than simply reacting to them afterward. Imagine a supplier who used to deliver merchandising products reliably for a long time but begins experiencing minor delays at first, followed by major delays in fulfillment. If we notice the pattern of increasingly frequent or prolonged delays in time, we can recognize that the supplier is encountering difficulties and take the necessary steps before we face significant shortages in the supply chain. This information is no longer relevant when the supplier has already gone bankrupt, and we are left trying to find another reliable supplier for our empty warehouse shelves.

Impact: Outdated data hampers the ability to make proactive decisions, forcing companies into reactive modes that may miss emerging opportunities or threats. The relevance of data also diminishes over time, as market conditions, customer preferences, and competitive landscapes evolve.

Solution: Companies must establish real-time or near-real-time data collecting and processing capabilities, such as streaming data pipelines, to ensure that decision-makers have access to the most current data. Implementing automated data refresh processes and ensuring that data is continuously updated can greatly enhance the relevance and timeliness of data-driven insights.

4. Data Consistency Across Systems

When data is stored in multiple systems, inconsistencies can arise due to differences in data formats, standards, or update schedules. For instance, a customer’s contact information might be updated in the CRM but not in the ERP system, leading to discrepancies. These inconsistencies can cause confusion and lead to decisions based on conflicting data.

Impact: Data inconsistency undermines trust in data, making stakeholders hesitant to rely on data-driven insights. It can also lead to duplicated efforts, as different departments may independently work to resolve inconsistencies, often repeatedly correcting discrepancies made in previous processes without communicating with each other and addressing the root causes.

Solution: Establishing Master Data Management (MDM) practices can help maintain consistency across systems. MDM involves creating a single, authoritative source of data (a ‘single point of truth’) that is regularly synchronized with other systems. It ensures that each data element is managed and modified by only one designated system, referred to as the ‘owner.’ By standardizing data formats and definitions across the organization, companies can reduce inconsistencies and ensure that all stakeholders are working with the same information.

5. Data Accessibility and User Empowerment

Even when high-quality data exists, it must be accessible to those who need it. Accessibility involves not just the technical ability to retrieve data but also ensuring that users have the tools and skills to read and interpret it. In many organizations, data is accessible only to a small group of data professionals, while the broader workforce relies on outdated reports or limited data sets. There must be a common understanding within the organization of what high-quality data means, and we must openly identify where our shortcomings and gaps lie in this regard.

Impact: Limited data accessibility can create bottlenecks, where decision-makers have to wait for reports or analyses from data teams. This delays decision-making and can prevent the organization from responding swiftly to changes in the market or internal conditions.It also diminishes transparency between teams, leading to misalignment and suboptimal performance.

Solution: Democratizing data access is key to overcoming this challenge. This can be achieved by deploying user-friendly business intelligence (BI) tools that allow non-technical users to explore data and generate insights independently. Training and upskilling employees to become more data-literate is also critical, ensuring that they can confidently access and use data in their decision-making processes.

Recommended reading:

How Can You Trust Your Data?  

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How Can You Trust Your Data?  

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In conclusion

As global retail brands become more dependent on data to guide their operations, the importance of strong data quality management continues to grow. To overcome the challenges of poor data, it is essential to implement a comprehensive, enterprise-wide Data Quality Program. Such a program can provide a solid foundation for the successful implementation of data-driven decision-making and helps support the brand’s overall goals and strategic objectives.

Wherever you are in your journey to building a robust data foundation, we are here to support you every step of the way, empowering your workforce to make better, faster, and more informed decisions. We specialize in addressing data fragmentation and improving data quality by enhancing completeness, accuracy, timeliness, consistency, and accessibility through a combination of technology investments and process improvements. We’ve got your back(end) covered, so you can focus on making the business decisions that drive your success.

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