What can happen when business colleagues struggle to find the information they need for effective collaboration, planning, and decisions?
For manufacturers and their contractors, data sharing issues could put assembly processes at risk for errors. Commercial lenders might lack data that helps them assess creditworthiness, resulting in unattractive terms and low acceptance of offers. Without accurate inventory data, retailers may underestimate customer demand for crucial products and inadvertently trigger a costly shortage.
Too often, traditional data management tools and processes can isolate data. They may also place data and data quality exclusively in the care of detached data management teams. These conditions make it harder for people to find and leverage the information they need, share crucial analysis and insights, and ultimately help achieve organizational goals.
How Data Silos Can Cause Damage
Siloed data systems and functions can prevent teams from accessing or sharing information they need to do their jobs successfully. This situation can increase the potential for errors due to missing, incorrect, or obsolete data. It may also create extra work for data managers, as requests to retrieve and file information could pull them away from their core duties.
Data silos can add unnecessary complexity to workflows, which may require more resources, slow down progress, inflate project costs, and increase the likelihood of errors. This can lead to disruptive attitudes and assumptions. Teams may come to distrust the information they receive, which can impact day-to-day operations as well as mission-critical enterprise processes. To help overcome the challenges of data silos, companies need to prioritize data democratization.
Data democratization refers to the process of making data available to all people within an organization, regardless of their technical expertise. The goal is to empower employees to make data-driven decisions by providing them with the systems and resources necessary to access, analyze, and interpret data.
The Connection Between Data Democratization and MDM
Because it expands enterprise-wide familiarity with and appreciation for data quality and integrity, data democratization helps establish the right environment for master data management (MDM). MDM refers to the processes governing how information is gathered, evaluated, and integrated into a single source of truth. And just like with data democratization, everyone has a role to play in MDM’s success.
The combination of data democratization and master data management can pay serious dividends for employees and teams across the enterprise:
Enhanced trust, accountability, and cooperation among teams: If all teams have a stake and active role in data quality, they are more likely to feel confident about the information and to support decisions that rely on that information.
Increased agility, creativity, and innovation: Teams view and use data differently; as more teams make use of the same data, the more likely they are to help find new insights and identify new opportunities that can benefit the entire business.
Improved speed, quality, and impact of decisions: With a highly accessible and trusted data foundation that provides a 360-degree view of prospects and customers, teams can more confidently and quickly design, test, and adjust strategies for optimal outcomes.
Greater value placed on data and data literacy: Data literacy is the ability to understand, analyze, and apply data to make decisions and take action. It is crucial, says George C. L’Heureux, Jr., Senior Director of Product Management at Dun & Bradstreet, because “Data is the language in which we do business. It’s how we make decisions. It’s how we talk to one another. And it’s how we’re going to be able to measure our success or our failure.”
While the benefits seem attractive in the abstract, what impact could they have on day-to-day business decisions and results?
The compliance team could gain access to more comprehensive data on customers, vendors, and suppliers for better informed decisions that help avoid regulatory penalties and prevent brand damage. Procurement managers could improve their visibility into their company’s balance of trade with customers and suppliers, leading to smarter spending and supply chain management decisions. Marketing and sales teams could use more accurate data to personalize customer experiences that help increase conversions, customer satisfaction, and retention.
Other positive results could include improved data analytics and business intelligence from data managers (on customers, suppliers, and partners) that help build more accurate sales forecasts and financial plans. Finance teams could better assess credit or loan applicants, offer more competitive terms, spot potential fraudsters quickly, and more easily gauge portfolio risk. Impacts could extend also to the executive team, which could improve its visibility into budgets, operating plans, and performance metrics and lead to smarter decisions about where future investments should be focused.
How Can AI Impact Data Democratization?
Data is more likely to be trusted, accessed, and widely used within an organization when it is clean, consistent, and fresh. Analytics tools powered by artificial intelligence (AI) can support data democratization and can help teams more easily unlock the value of their data.
By reducing or eliminating manual processes, AI can facilitate more objective, efficient analysis, better scalability, and avoid human error. For instance, AI can automate data cleaning processes, making data more consistent and accurate without the need for manual intervention.
AI also can help make data analytics more broadly accessible to teams and employees. Many modern AI systems include features like natural language processing, sophisticated interfaces, and zero-code designs that help make them efficient and easy to use.
A well-designed AI model can uncover what users might not have even known to look for, deliver automated notifications and updates to stakeholders, and provide clear explanations to help make the information meaningful and actionable.
Implementing Data Democratization
The more resources, time, and effort that teams can commit to planning and implementing a data democratization strategy, the faster they can consolidate and strengthen their data. Though companies can have very different resources and goals, data experts and senior leaders generally agree on these four key steps for establishing a data democracy.
Step 1: Inventory and identify data sources.
Organizations need to thoroughly investigate where data resides, taking into consideration on-premises servers, third-party data centers, cloud applications, team-specific applications, AI and other technology, internal databases/hubs/spreadsheets/archives, etc.
With this knowledge, companies can start consolidating and building a single source of truth for data, beginning with “golden records,” which are authoritative and trusted records for prospects and customers. A golden record provides a complete view of an entity with no inconsistencies across multiple sources or systems. Ultimately, it’s the great enabler of better customer relationship management, effective marketing campaigns, accurate financial reporting, and streamlined business operations.
When there’s a rigid belief that there’s only one “perfect” name, address, phone number, and so on, the golden record can become unmanageable. Organizational teams that are ready to embrace data democratization and MDM should embrace the concept of the “multifaceted golden record.” Essentially, this means allowing the inclusion of fields that can accommodate seemingly duplicated values or linking to tables that capture the diverse versions of the truth. By adopting this approach, companies can capture the full richness of teams’ data and ensure that all valuable versions — and use cases — are appropriately represented.
Step 2: Create clear, consistent data governance policies.
Companies can struggle with data governance — the guidelines and requirements that ensure data is being used appropriately and responsibly throughout its lifecycle. To preserve data integrity, privacy, and security, organizations need documented protocols for data collection, management, storage, access, and use among teams. Those protocols should be specific about data roles and responsibilities and include clear data-quality standards and standardized KPIs.
Start by defining enterprise data definitions and ensure all business units and teams are aligned to those definitions. To have consistent, trustworthy data that can help the organization grow, there should not be multiple competing definitions for the most important business entities (customers, partners, suppliers, products, etc.).
Teams need to collaborate and agree on the dimensions of data quality, the process to measure data quality, and the means to regulate and distribute quality metrics throughout their company. They also need to agree on hierarchy definitions that help the entire company understand the full breadth and depth of any relationship (or potential relationship) with other companies. Because the business landscape is dynamic, they also need to agree on how they will make changes to their organization’s data.
Step 3: Provide tools, education, and ongoing support to teams.
Companies can use various employee surveys and interactions to gauge how data literacy may vary by role, function, etc., and use that information to determine which tools and instruction can help employees effectively access, use, and share data. Depending on budget and other resources, companies may need to invest in consulting services, AI-powered analytics platforms, data portals, and/or software training to help remove barriers and build up the knowledge and confidence their staff needs to effectively democratize, interpret, and leverage data.
Data experts recommend that organizations remember to view data literacy as an ongoing learning journey. Implementing progress check-ins, retraining, and upskilling opportunities can help organizations protect their investment and help employees continue to grow their data expertise.
Step 4: Strengthen collaboration and cooperation.
Data democratization is more likely to succeed within companies that actively and consistently reinforce sharing, transparency, and ongoing learning. Cross-functional teams, company forums, and recognition and incentives from leadership can all encourage staff members to share data analysis and insights that can help them tackle responsibilities and contribute to business goals.
Companies can also stimulate internal interest in data management and data sharing through their internal channels. For instance, employee newsletters and rallies can be ideal for showcasing success stories in which data democratization contributed to product breakthroughs, customer retention or acquisition, or accolades from external organizations. Remember, the easier and more rewarding it is for teams to share what they’ve learned, the more frequently idea exchanges and knowledge transfers are likely to occur.
Seek Expert Support from a Trusted Data Solutions Provider
Evaluating and selecting a data provider can be crucial to building a reliable data foundation for MDM and data democratization. Working with the right provider can help ensure data is consistent, current, and comprehensive. The right provider can also help make data widely available via AI-driven applications and easy-to-navigate user interfaces. These powerful tools can give casual users, advanced users, and power users the access to information they need, when they need it.
Before making a final decision, companies should evaluate a data company’s expertise and experience. Assess the provider’s data quality, coverage, and freshness, as well as the legitimacy, consistency, and reliability of its data collection practices. In addition to the provider’s commitment to research and innovation, be sure to investigate its approach to data security, integration, and compliance. Finally, take a hard look at the provider’s stability and market reputation before signing on the dotted line.
Get Better Results with Data Democratization
Data is a powerful and vital asset for organizations that can become disconnected, incomplete, and obsolete.
Through data democratization and master data management, companies can remove data silos and improve data quality. These processes help ensure trusted data is confidently accessed, used, and shared within and across teams, and they can bring beneficial changes to almost every function – changes that can drive better decisions and outcomes and more efficient use of budget, time, and other crucial resources. By taking key steps, companies can begin to adopt more democratic data management processes and develop a corporate culture that encourages ongoing learning to help improve data quality, increase data use, and drive innovation.
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