Why You Must Perfect Your Data Management Strategy: 5 Reasons to Start Now

Key Points to Ponder Over for Data Management

Data management and its proper utilization can play a critical role in achieving goals set for organizations or your employees. From sourcing reliable data for a wide range of tactical customer engagements to ongoing core business processes, an efficient data management strategy provides its value to businesses through its ability to:

1. Create, maintain and deploy the best versions of data across the organization and external ecosystem.

2. Create transparent, holistic business views by consolidating, categorizing, and synchronizing data from different domains.

3. Provide access to key data in a secure and governed manner.

4. Spread and syndicate the right data in the right format to the right channel

5. Analyze the patterns and trends with a data-driven approach for improved performance

Enterprises need better and deeper data management capabilities to unlock the real value of data and drive improved business outcomes. They need to manage data from every aspect in terms of data quality, governance, workflow management, versioning and rule management, and data syndication.

Here is how a robust data management strategy can control cost, minimize risk, and improve businesses outcomes:

1. Improvement in Data Quality Standards

Maintaining high data quality is a process in itself. An enterprise’s operational efficiency is wasted on non-value tasks because of poor quality data and its refinement. Businesses need solid data management capabilities with a deep understanding of how data in each silo has been consolidated, maintained, and shared. Your data management strategy should focus on automatic processes especially updating records and cleaning redundant data to ensure accuracy and compliance. With frequent data quality cycle checks, businesses can analyze, cleanse and monitor data from disparate ERP, CRM, or SCM systems for up-to-date, consistent, and governed data sets. By the end of 2021, augmented data management could reduce manual data management tasks by 45%, according to Gartner.

2. Better Match Needs with Customer Personas

Businesses that have accurate customer data pertaining to buying patterns, credentials, and native preferences can better cross-sell or up-sell products to their customers. By creating unique customer data repositories, they can predict and pitch products that most likely match customer needs. In addition to supporting unique data sets for each customer’s persona, a centralized data management strategy supports unique attributes for each line of business as well. For example, a B2B customer data set is defined and authorized to a separate repository from a B2C customer data set ensuring a seamless cutover window for businesses to change data priority levels while managing and editing from their master data source.

3. Identify and Assess Data-Related Risks in Supply Chain

Businesses can draw insights on vendor performance, financial health, policies, and processes from the data of vendors and other third-party channels involved across the supply chain to better identify critical threats to operations. By closely analyzing and categorizing partner data into relevant categories, a well-placed data management strategy illustrates ways to implement compliance measures in data and identify value-add components of a supplier more effectively. This further helps organizations strategize their purchase-related decision by identifying risks on time and preventing supply downtime.

4. Minimize Frictions in Business Operations

Data duplication not only increases inconsistencies in data but also hampers productivity in set tasks across the value chain. By unifying business process systems, organizations can easily identify and streamline processes or sub-processes that tend to reuse or duplicate information that has been entered. With this, standardized measures can be implemented across the business and its processes for enhanced transparency, consistency, and reduced variables in operations. Acting as an abstraction layer, a comprehensive data management solution helps businesses resolve redundancy issues across decentralized process systems. 

5. Accelerate Business with an Integrated Approach

As businesses grow, manually managing all data points from several systems and applications is next to impossible. It not only waivers organizations from making crucial decisions in sales but also affects the internal workings of an organization. There’s a constant need for businesses to collaborate on improvements, monitor processes, make corrections, and create reliable data sets for actionable data analysis. This creates an alarming need for an integrated approach. By creating a single master source with a well-defined management strategy, organizations can store, retrieve, and manage business-critical information as and when needed. A well-defined master data strategy in place not only governs multi-domain or customer, product, or channel data but also logically interrelates domains to create a practical view for businesses.

The Final Part

Businesses can break free from cumbersome manual entries and complex data structures with a single source for crucial data i.e. generally used by different departments across the organization. With a consolidated and centralized management, protecting data from possible misuse is easier and enhanced data security protocols and consistency parameters can smoothly be implemented by the business. From adapting to changing business needs or implementing corrective measures, with a data management solution, any changes made to the business’s master data will reflect across all the relevant data destinations. 

Without any doubt, storage, backup, and operation of data are simple and manageable. When it comes to managing critical and complex data, a comprehensive data management strategy holds the key to providing an integrated, consistent, coherent, and accurate database. You must choose the right direction in order to make the most of your data management initiatives. Your willingness to adapt fast, data literacy rate within the organization, and robust data related policies, procedures, and practices will be crucial for moving in the right direction.

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