Next-gen technologies and strategies for seamless data integration

The opportunity to leverage data to make smarter business decisions is stronger today as new sources of data emerge, including real-time data from the Internet of Things (IoT) and social media. Yet, large volumes of data remain untapped, questioning the efficacy of an organization’s current data integration practices and strategies.

Over half the respondents (55%) of business leaders in a study by Splunk, The State of Dark Data reported the prevalence of ‘dark data’ or data they had not been able to access, though they believed data was invaluable to success.

Many businesses are missing out on opportunities due to their inability to use data effectively. According to a report by Salesforce, Untapped Data Research, which covered 10,000 business leaders, showed that businesses were losing out by not quickly putting data at their disposal into practice. As many as 67% of business leaders were not using their data to decide on pricing in line with economic conditions, such as inflation. Additionally, only 29% were backing their strategy to enter new markets with data-driven insights.

Effective data integration enables organizations to consolidate disparate data sources and create single sources of truth, improving decision-making and timely responses to market changes.

The global data integration market is estimated to grow at a compound annual growth rate (CAGR) of 12.3% from 2023 to 2030, due to the increasing need to derive intelligence from data from various sources and formats.

Data management practices

Organizations must embrace a multifaceted approach to achieve effective data management and integration.

1. Extract, transform, load (ETL)

ETL, a three-step data integration process, involves extracting data from diverse sources, transforming it for usefulness, and loading it into a target database such as a data warehouse and data lake for business intelligence.

Think of an organization's data as a bustling marketplace that is scattered and unorganized. The ETL tools act as market organizers that gather, categorize, and arrange the offerings into neat stalls. This transformation helps to establish order and enable businesses to navigate the data marketplace and extract valuable insights.

However, many businesses are switching from ETL tools to newer data integration options such as API integration and data virtualization.

2. Application programming interface (API) integration

API integration is a way to ensure smooth interaction between different systems and applications. This facilitates the sharing and synchronization of data, which enables seamless data integration. API tools offer businesses a lot of benefits like enhanced scalability, improved data accuracy, time and cost savings, security compliance, and cross-platform compatibility. Prominent API integration tools include MuleSoft and Dell Boomi.

3. Data virtualization

Data virtualization is a technique that creates a virtual representation of data gathered from diverse sources, such as databases, cloud platforms, and APIs. Instead of consolidating data into a single repository, data virtualization tools offer a flexible approach that simplifies data management enabling efficient analysis and decision-making. Leading data virtualization tools include Denodo, JBoss, and AB Inito.

4. Steps toward successful data integration

In today’s digital economy, data integration has become increasingly important. However, several challenges such as poor data quality, stringent regulatory standards, a lack of tech expertise, and inadequate stakeholder buy-in are impeding adoption. A thorough understanding of the organization’s data landscape and in-house capabilities must inform data integration strategies.

• To design an effective data architecture, it is essential to determine which data sources contribute value to your business, whether they originate from applications within your data center, SaaS platforms, IoT events, transaction streams, external sources, or emerging data types.
• Aligning data availability with usage requirements is also critical to identify current and future data needs, usage patterns, and ensure stakeholder alignment. Efficiently transferring and connecting data while considering factors such as data quality, quantity, and the costs associated with moving data across SaaS and cloud platforms is essential. Streamlining data movement ensures timely access to relevant information without unnecessary overheads.
• Integrating various data infrastructure components, integration tools, compliance regulations, analytics capabilities (including AI-ML, semantic analysis, and geospatial analytics), architectural principles, and delivery mechanisms into a unified strategy is necessary for creating a high-value, data-driven business environment that fosters innovation and competitiveness.

As a trusted advisor with over 25 years of industry experience, Torry Harris has been helping organizations globally to achieve their business objectives through their effective data integration strategies. Our partnership with Schneider Electric resulted in the design of a robust integration framework for the company’s IoT-enabled platform, EcoStruxure.. As of 2022, there were over 7.4 million assets connected to EcoStruxure globally. Our outside-in approach, engaging Schneider Electric's technology partners and lines of business, contributed to a deeper understanding of challenges, fostering effective collaboration.