Addressing Data Consolidation Imperative With a Unified Data Platform

What is unified data platform? Since organizations implementing BI solutions registered a 127% ROI in the last three years (Tableau), understanding how it can streamline your organization’s data management is essential. In this article, we will discover the importance and techniques used for data consolidation and how data storage optimization becomes an essential facet of modern data management in the corporate sphere.

What Is Data Consolidation?

Data consolidation is the process of combining data from different sources (websites, apps, databases, social networks, etc) or parts of an organization to create a unified and consistent database. This process can occur in various contexts, whether in the corporate world, information technology, financial accounting, or other business-critical areas. The most significant benefits of data consolidation include:

• Data monetization, as the consolidated information can be monetized by selling insights or data products to other organizations, creating additional revenue streams
• Cost savings by reducing the need for multiple data storage systems and minimizing data duplication
• Centralized data source, which helps maintain data accuracy and consistency, reducing errors and improving overall data quality

Data sets can be systematically merged from all existing sources using data consolidation software, eliminating fragmentation and consolidating them using specific techniques to centralize the data.

Data consolidation techniques

1. ETL (Extract, Transform, Load)

ETL is one of the most widely used data management techniques to consolidate data. It is a process of extracting data from a source system and loading it into a target system after transformation (including data cleansing, aggregation, sorting, format standardization etc.).

Automation integration tools can perform ETL in two ways:

• Batch processing: suitable for executing repetitive data jobs with high data volume at predefined intervals.
• Real-time ETL: uses CDC (Change Data Capture technology) to push updated data to the target system in real time.

Batch processing is suitable for executing repetitive data jobs with high data volume at predefined intervals. At the same time, real-time ETL uses CDC to push updated data to the target system immediately, allowing for near-instant data synchronization.

How it works: in the retail industry, a large multinational chain faced a significant inventory management challenge. The company’s inventory data was spread across multiple regional databases and store locations, resulting in data silos, stockouts, overstocking, and inefficient supply chain management. The data underwent transformation processes to standardize product codes, categorize items, and calculate demand forecasts. As a result, the retail chain achieved improved inventory accuracy, reduced overstocking, and optimized its supply chain operations.

2. Data virtualization

Data virtualization blends data from diverse sources without migrating or duplicating it. It provides data operators with a consolidated, virtual view of the information.

Unlike the ETL process, the data remains in place but can be accessed virtually by front-end solutions such as applications, dashboards, and portals without knowing the specific location.

How it works: a major hospital network faced a challenge in aggregating patient data from diverse sources, including electronic health records (EHRs), lab systems, and remote monitoring devices. They aimed to provide medical professionals with a unified view of patient information for more efficient and informed care. Instead of replicating and moving data, they opted for data unification. The hospital network efficiently integrated data from diverse sources without complex data replication or migration by leveraging data virtualization.

3. Data warehousing

Data warehousing solutions integrate data from different sources and place it in a central repository. This technique facilitates reporting, business intelligence, and other ad hoc queries. It provides a comprehensive, integrated view of all data assets, clustering relevant data.

Data warehousing is the broader concept of creating a centralized data repository, while ETL is a specific process within data warehousing that deals with the extraction, transformation, and loading of data into the data warehouse. Data warehousing provides the environment for data storage and analysis, while ETL ensures that the data is prepared and structured appropriately for that environment.

How it works: a global advertising agency faced challenges in assessing the effectiveness of its omnichannel marketing campaigns. By clustering engagement metrics from social media platforms (such as Facebook, Instagram, and Twitter), website traffic data, email marketing performance statistics, and identifying key trends, the agency could better allocate resources and budget, improving its ROI marketing initiatives.

Choosing the correct data consolidation technique is crucial for organizations as it directly impacts their ability to streamline data management, gain valuable insights, and make informed decisions. Whether through ETL for data transformation, data virtualization for real-time accessibility, or data warehousing for comprehensive reporting, the choice of technique should align with specific business needs. Additionally, adopting efficient data consolidation software can contribute to the success of these techniques by providing the necessary tools and capabilities for avoiding frequent challenges.

• Time. In general, internal IT teams already have limited time with their tasks of configuring, assessing, maintaining, and testing on-site equipment and hardware, as well as their daily tasks. For example, a team of a multinational manufacturing company may not have additional time to spend hours managing data consolidation, as its internal IT team is usually already stretched thin with daily tasks.
• Resources. Data aggregation requires many resources, from specialized knowledge from expert data scientists to the right type of software. Some companies may not have the budget to source experts for data-driven innovation. This is a frequent problem for startups who lack the budget to hire expert data scientists and acquire specialized software for data compliance framework development.
• Locations. For many companies that operate in multiple locations with remote offices, warehouses, and branches, data is not stored in a single location. Instead, it is managed across multiple locations. E-commerce businesses are more likely than others to experience this challenge as retail chains with stores and warehouses in various locations face data fragmentation due to data being stored across multiple sites.
• Security. There is always a risk of breaches and hacks when storing data; moving this information elsewhere can increase these risks. Since companies may also need to comply with their industry regulations, it can be difficult to maintain data security policies when there are scattered data sets.

Efficient data consolidation is vital for organizations seeking to avoid the abovementioned challenges. The following step-by-step strategy outlines the essential preparatory work required for successful data consolidation, ensuring data integrity and compliance with company regulations.

Step-by-step strategy for optimal data consolidation

1. Identify data silos. Begin by identifying existing data silos within the organization. Evaluate the size and significance of each silo, as the extent of preparatory work may vary.
2. Assess data compliance. Determine whether centralizing the data complies with company regulations and policies. Ensure access control is in place to maintain data security, especially if access should be restricted to specific groups.
3. Analyze data format. Examine the format of the data to be consolidated. Assess whether the existing data format suits the consolidated platform or if changes are needed to ensure data interoperability.
4. Regular data backups. Implement a backup strategy to safeguard data integrity during the consolidation process. Regularly back up data to prevent any loss or corruption during migration.

By following this step-by-step strategy, organizations can efficiently consolidate data, eliminate silos, and ensure a compliant and secure transition to a centralized data repository.

Data consolidation focuses on gathering data from various sources, eliminating data silos, and ensuring data is in one place. However, the data is still distributed across various systems, possibly with inconsistent formats and structures.

A unified data analytics platform builds upon data consolidation by bringing these consolidated datasets together into a single, well-organized repository. This platform not only centralizes data but also offers integrated tools for data processing, analytics, and security. Let’s explore how it impacts business processes.

A unified data platform is the central control center for organization’s data storage solutions. It combines data from various sources and provides efficient management, analysis, and decision-making tools. 

Data warehouse core components include:

• Seamless data integration from multiple sources, eliminating data silos.
• A centralized storage location for structured and unstructured data accessibility.
• Data processing tools for data cleaning, transformation, and preparation.
• Advanced data analysis, reporting, and business intelligence.
• Robust data security measures to protect sensitive information.
• Data scalability options to handle large volumes of data and business growth accommodation.
• Real-time data processing for informed decision-making.

Many commercial and open-source unified data platform developments are available in the market with different user experiences and functionality. Usually, they offer a selection of the features listed below:

• Customizable dashboards to enhance decision-making through real-time data visibility
• Data visualizations for facilitating data interpretation and trend identification.
• Scheduled reports with security able to streamline report automation while ensuring data security.
• Data quality control to maintain high data accuracy for reliable decision-making
• NLP for extracting valuable information from unstructured data sources
• Faster data migration for timely analysis and insights
• KPI performance tracking to monitor and improve business performance

Unified Data Platform In Practice

How does a unified data platform streamline data consolidation and analytics? It is better to look at examples from different industries to see in particular which business processes are being improved.

Industry	Data types	Without data consolidation	With data consolidation
Healthtech	Patient records, diagnostic reports, treatment history	Patient data is scattered across different healthcare providers, leading to fragmented medical histories and difficulties in coordinating care	Patient EHRs are centralized, providing a comprehensive and up-to-date medical history. Healthcare providers can deliver more efficient patient care and refine preventive diagnostics.
Fintech	Transaction records, customer profiles, investment data	Transaction records and customer profiles are stored in disparate systems, making fraud detection and financial analysis challenging.	A unified data platform simplifies regulatory compliance by providing a single source for auditing and reporting. It allows for more effective fraud detection by analyzing transaction patterns and anomalies.
Insurtech	Policy data, claims information, customer communication	Policy data and claims information are scattered, making claims processing and customer service less efficient	Insurers get a single source of truth for customer profiles, policies, and claims. They can fine-tune pricing models and risk assessments, simultaneously improving fraud detection algorithms.
Martech	Customer data, campaign performance metrics, customer feedback	Сustomer data and campaign performance metrics are spread across multiple platforms, making campaign analysis and customer targeting complex.	Marketers can coordinate efforts across email, social media, and other channels for a seamless customer experience. They can better measure and analyze return on investment (ROI), enabling more effective campaign adjustments.
Publishing	Articles, headlines, metadata, content repositories	In a publishing company, pieces of content are stored across various content management systems, making content retrieval and analysis cumbersome.	A platform makes organizing, searching, and retrieving content easier. Publishing businesses can analyze content performance, identify trends, and understand reader preferences better.

In fact, data lakes in many companies are nothing but a hidden treasure trove, and data analytics solution companies empower businesses with the ability to turn raw data into valuable insights by implementing advanced data governance policies.

Conclusion: The Advantage of Data-Driven Decisions 

Unified data platforms are an important building block of a data-driven company. When data is stored in one location, a smaller setup is required to manage it. A customized platform’s data analytics tools and processes allow companies to:

• Reduce hardware, maintenance, and operational costs
• Enjoy greater control as fewer data retrieval processes are required
• Access the data directly from one place
• Achieve significant time savings by reducing data search and retrieval efforts
• Implement straightforward disaster recovery solutions in cases of data loss, cyberattacks, or system failures

Need help figuring out where to start? A Lightpoint expert will happily share the company’s experience and offer a solution that meets your immediate business goals during an in-person consultation.