What is centralized data management and what are the benefits?

Centralized data management is a process of consolidating data from multiple sources so it can be stored, organized, and managed in a single, unified system. This approach makes it easier for users to retrieve the data they need while improving its consistency, completeness, and accuracy.

It’s often hard to make a business case for centralized records management because the nature of the benefits can be difficult to quantify.

Let’s first look at why we perform records management at all. At its core, we do it because data and content are strategic assets that make a direct contribution to organizational objectives, efficiency, and effectiveness.

The need for data collection and record-keeping is indisputable. In most cases, laws and regulatory requirements specify that certain records should be created and kept for specific periods of time. In fact, records are critical to helping organizations:

• Undertake and continue their business functions
• Maintain data quality, data integrity, data consistency, and data security
• Make good business decisions
• Service their customers and clients
• Significantly enhance customer experiences
• Empower data analysts to create actionable insights
• Improve their reputation
• Respond to legal issues, investigations, inquiries, and audits

Centralized vs decentralized data management  

A centralized data management system consolidates data from across multiple sources into a single database. If you have worked with enterprise data or a data lake, then you are likely to be familiar with centralized repositories. Everything is stored in one place with data consistent, accessible, and secure.

In contrast, decentralized data management is sprawled across multiple systems or nodes. There isn’t a single, unified architecture here. Instead, data resides at different points in a network or infrastructure. 

There are pros and cons to both approaches. However, a centralized data model is typically more efficient, consistent, and secure as there is a “single source of truth” and no competing and isolated systems in play.

What are the benefits of centralized data management? 

Now, let’s look at some more of the benefits of centralized data management.

1. Reduced cost

For many organizations, cost reduction remains the primary driver for a data management platform. Effective records management enables profitability by lowering the cost of doing business.

Effective data management addresses the challenge of storage growth within an organization. For example, if an organization stores 400 TB of electronic information and can eliminate 30% of this volume by managing the content lifecycle more effectively, it saves 120 TB of storage.

In this scenario, a company saves $198,120.00 1 based on infrastructure costs alone, even before applying the 28% 2 of a single IT FTE’s time that would otherwise be spent managing this additional data. This is how effective management reduces costs.

A systematic and automated records management process can reduce the cost of these obsolete records and, at the same time, reduce the inherent risk present in keeping information for longer than required.

One of the core elements of Records365 is our connector framework, which makes it easy to connect cloud-based and on-premise content sources in a matter of minutes.

2. Reduced risk

The recent introduction of broad-based compliance and information privacy laws, such as the EU GDPR and the California Consumer Privacy Act of 2018, makes the need for effective records management more quantifiable and critical for decision-makers charged with managing organizational risks.  

Records exist regardless of whether they are recognized as such by the organization. For instance, consider vital records such as lease agreements, signed contracts, articles of incorporation, patents, and intellectual property. 

These one-of-a-kind records are critical to an organization’s operational efficiency in the event of a disaster and require specialized tracking and preservation measures to effectively manage risk.

An effective overall corporate risk management strategy includes a systematic retention program to minimize risk related to legal matters and regulatory compliance. Risks to records fall into two categories:

Risks related to the security of the record or the information that it contains

Some examples include the following:

• Unauthorized disclosure (for example, a staff member downloads and emails a confidential document to the press)
• Unauthorized modification, such as someone editing final versions of records
• Unauthorized destruction, such as someone deleting documents without approval

Risks to the record continuing to be available, readable, and usable for the duration of its lifetime (or retention period)

Examples can include:

• Malicious damage and theft
• Environmental damage and acts of God
• Improper storage
• Failure to back up information

A well-designed records management system provides process and application safeguards against these types of risks across both electronic and physical content.

By ensuring appropriate information retention, systematic records management also reduces the organization’s exposure during civil litigation and investigation. Records provide a critical role as evidence in public liability, personal injury, breach of contract, and wrongful dismissal suits.

3. Increased revenue

When managed effectively, recorded information has a quantifiable value that is marketable and sellable. Customer profiles and mailing lists are obvious examples, but this benefit also applies to broader business intelligence information, such as intellectual property or data about customer or client purchasing habits, preferences, and behaviors.

Consider the organizational value of intellectual property, such as proprietary technologies or processes, trade secrets, patents, product formulations and blueprints, trademarks, and copyrights. When correctly managed, these information repositories can create new business opportunities for the organization. One example is extracting intellectual property to market a new product, production method, or service.

Because the efficient management of these types of information also enables easy discoverability and retrieval, it delivers a distinct competitive advantage to the organization.

How does centralized data management work? 

Centralized data management is all about creating single sources of truth. Data is consolidated into one system for storage, processing, and governance. But how exactly does it work?

1. Data integration

Data from multiple sources needs to be combined and stored in a centralized database. This process is known as master data management (MDM). MDM tools are used to automate data cleansing and integration for use by each department.

2. Centralized repository

Both structured and unstructured data are then stored in a data warehouse or data lake. Organized data is easier to manage and retrieve.

3. Centralized control

These centralized data systems are overseen by centralized data teams, who are responsible for ensuring the integrity, security, and accessibility of data. The central oversight makes it easier to implement data governance frameworks and meet compliance.

4. Data accessibility

Centralizing data creates a single point of access. There are no blind spots or data silos. Employees access data catalogs, dashboards, and APIs to get the data they need to perform their jobs. This eliminates the frustration of retrieving data from multiple data sources.

5. Data processing

Centralized platforms continue to ingest raw data and prepare it for use by employees, data teams, and data engineers for advanced analytics using analytics tools.

5. Health checks and optimization

Automation tools and monitoring systems detect anomalies and streamline repetitive tasks to improve data efficiency.

5. Scale and grow

Cloud-based solutions then dynamically scale according to demand and deploy compression techniques and tiered storage to maximize value and reduce waste.

How to get started with centralized data management

Don’t be daunted by the task of consolidating data. By taking the time to understand your data environment and the costs involved, you can start planning your centralization strategy. 

Step 1: Understand your costs (and savings)

Knowing your cost base and the potential savings from investing in a records management solution is key to getting started. This requires a thorough analysis of IT infrastructure and data storage costs, and related costs such as IT personnel and soft costs. Be sure to also add the impact on productivity brought by managing high volumes of obsolete records.

Step 2: Prepare for implementation

Before getting started, assess your records management readiness:

• Have you designated a formal records management role in your organization?
• Do you have a network of designated liaisons for records management in each organizational unit?
• Have you established a formal records management data program within the organization?
• Have you instituted policies and procedures for records management and implemented an automated records management system?
• Have you identified and mapped the applicable laws and regulations that apply to your organization and industry sector? For example, FINRA, HIPAA, SOX, GLBA, US 36 CFR, GDPR, DoD, etc.

To ensure the successful implementation of a solution, you have to be able to answer yes to each of those questions.

Step 3: Identify high-risk records

Next, identify the high-value records within the organization that are at high-risk. Value can normally be determined by two main factors:

• Permanent value:  Generally one-of-a-kind records such as articles of incorporation, trademarks, and IP.
• Business value:  Records that enable organizations to make data-driven decisions, undertake and continue their business functions, service their customers and clients, and keep their legal and regulatory obligations.

Once you’ve made these assessments, you’re ready to implement a records management solution.  

What are some examples of effective centralized data management?

Three-quarters of organizations have already deployed a centralized data management strategy. Let’s look at the approach in action with two real-world examples.

Huayu Sanden increases productivity

Automotive component manufacturer Huayu Sanden started its transition to a centralized data platform after struggling with a scattershot approach with information siloed on employees’ computers. The company’s technical center director, Yao Ti, said: “Our previous PDM solution did not meet our requirements for improving the R&D process’ productivity.”

To solve the problem, Huayu Sanden developed a centralized data infrastructure to integrate previously isolated ERP, PDM, and OA systems. The move has been transformative, driving a 60% reduction in printing errors and costs while providing the insights its team needs to target expansion with new and innovative product designs.

Karnataka Engineering achieves time and cost savings

Karnataka Engineers also reaped the rewards of a centralized data system after running into problems with data duplication and security. This made it difficult for employees to search and access the project data required to complete critical tasks.

The India-based manufacturer embraced data centralization with a secure, integrated, and scalable data architecture, which has since increased productivity by 25% and driven 60-70% time savings and 25-30% cost savings. 

Karnataka Engineering Manager Harsha Chandrashekar adds: “Quick data retrieval and easy revision management has really made our lives easy.”

4 best practices for centralized data management 

Here are four best practices to improve your data:

1. Know your data landscape

Don’t run before you can walk. Take the time to understand the true scale of your data assets. Start by identifying your structured and unstructured data sources so you know what and how you need to bring everything together. Don’t forget about legacy systems, and older spreadsheets and CRMs.

2. Establish clear governance policies

Centralized data management works best when you have clear governance frameworks in place. Make sure to create policies that outline who can access, modify, and manage your data.

3. Implement security protocols

You must also set up role-based data access controls and encryption to meet any relevant regulations and ensure compliance standards. Your centralized system must comply with GLBA, GDPR, and APA (depending on where your business resides).

3. Standardize data formats

Master data is key to true centralization. All data sources must be sanded down and harmonized to create standard data formats and protocols. The aim is for all of your systems to communicate seamlessly.

Summing up

Centralized data management combines data from various sources, bringing order to a chaotic data environment. By consolidating data from multiple sources and creating a unified architecture, organizations can make data more secure, consistent, and accessible. Users will know exactly where data resides when they need it the most.

Are you ready to act? RecordPoint can centralize your data and deliver the personalized experiences customers demand. Schedule a demo today.

FAQs

What does centralized data management mean?

Centralized data management is about consolidating data from multiple sources into one single location. The aim is to take back control of data so it can be easily accessed and managed to drive productivity gains. 

What is the difference between centralized and decentralized data management? 

Centralized data management focuses on a single source of truth with uniformity and integration at its core. This contrasts with decentralized data management, which involves multiple systems and can lead to inconsistencies and inefficiencies. 

What are the challenges of decentralized data management?

Integrating multiple data sources can be complex and might be met by resistance from users who are accustomed to legacy systems. To address these challenges, make sure to plan your migration in detail and communicate clearly with stakeholders about the changes and benefits. 

What is an example of centralized data? 

Examples of centralized data include data warehouses, data lakes, and online database software. Centralized data is always stored in one location and is typically managed by a central authority or server.

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