Despite the global digital acceleration of data use cases, many companies still struggle to be data-driven. Abandoning gut decision-making and the siloed data analytic processes of the past are proving to be not a technological challenge but a challenge of strategy, planning and culture.
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The tools for organizations to make an effective switch are highly developed and available today. From cloud computing to machine learning and business apps, companies have many options to boost performance by better leveraging their data.
However, creating a data-driven company requires a clear data integration strategy and a solid data culture. But how is data integration built? How can top-level executives visualize all the data they need in one platform while other teams, such as sales, inventory, production and IT, utilize the same? In this report, we dive into the essence of data integration strategies and how to create one for your organization.
What is data integration?
Data integration is a framework that combines data from siloed sources to provide users with a unified vision. Data integration benefits include better data governance and quality, increased visualization, better decision-making, and better performance. Standardizing data is essential for data integration to be successful, as multiple teams — some of which may not have advanced IT technical knowledge and skills — need to access and use the data system.
When combined with tools like machine learning and predictive analysis, unified data insights can significantly impact a company’s operations, allowing it to detect risks in advance, meet compliance across the board, boost sales and detect new growth opportunities. Data integration aims to create a single access point of data storage that is available and has good quality standards. But to move data from one system to another and meet big data challenges with excellence requires a data integration strategy.
Tips for creating your data integration strategy
Data integration involves fully transforming an organization’s data systems and how they operate. The migration and transformation of data are, in itself, challenging and technical. However, other issues, which are often neglected, are vital for a data integration strategy to work. Here are some tips for your journey toward creating a seamless data integration strategy.
Where do you start with a data integration strategy?
The first step in data integration is not to acquire the tools and tech from vendors but to plan the company’s strategy. Data integration is not about data and technology — these are just tools that serve a purpose. Data integration is about collaboration between people, teams and your entire workforce.
Every company has its objectives and goals and must understand which data will help them achieve them. Aligned with a company’s mission, values and data governance strategy, data leaders must lead the data integration strategy.
Once organizations have answered what business goals their data integration needs to support, they can turn to other questions. Access and availability need to be clear and transparent. While executives and critical stakeholders might need full access and visibility on all unified data, other departments require restricted access. Additionally, roles and responsibilities should also be set.
Ideally, organizations should aim to integrate independent systems into one master data warehouse. In order to accomplish this task, leaders need to ask what data needs to be integrated, who will make up the data integration team and where will the data integration take place: on the cloud, on-premises or hybrid.
What data should be integrated?
The data that you choose to integrate in your business will depend on your business goals. It will also depend on the industry you work in and any regulatory standards or competitive benchmarks that you need to meet. But regardless of your specific data and what you hope to achieve with it, most companies need to integrate data across these categories:
- Financial: Financial planning and budgeting are essential to empower data-driven business decisions. Financial data and systems that should be integrated include accounts payable, accounts receivable, general ledgers and consolidation systems.
- Sales and marketing: Having siloed sales and marketing systems impacts customers and your operations. Data related to customers should be integrated into a unified view. Data related to inventory, manufacturing and distribution may also need to be integrated, depending on your business operations.
- Human resources: HR data systems help companies to more effectively attract and retain talent while providing them with tools and resources. HR data that should be integrated includes payroll, leave management, workforce planning, basic administration modules, skills and training, and growth path opportunities.
What type of data integration should I use?
Data integration can be done using different technologies and architectures. It is essential that you define with your IT team which type of data integration system is best for your operations.
The most common enterprise data integration strategy is known as extract, transform and load (ETL). Other common data integration strategies include data replication — where data is copied from one database to another — and data virtualization — where a virtual database connects to existing separate datasets.
Organizations must also decide where the data warehouse will be created. Cloud or hybrid environments will give teams more flexibility to adopt new technologies as they hit the market. Operating strictly on-premises makes it more difficult for companies to be on the cutting edge of innovation due to the costs that deploying new technologies may have.
Choosing the right data integration technology
Once you have laid down a clear strategy, it’s time to nurture a data integration culture, define the framework’s processes and choose the technology that best suits your goals.
There are several excellent data integration vendors and solutions, each with diverse offerings. While determining which data integration solution works best for your business, consider the following factors:
While data integration will be led by IT and data specialists, the data must be accessible to different departments. Code-free solutions that support drag-and-drop functionality are recommended for everyone in your company to be able to use the platform.
Visibility, access and performance
It’s important to ensure that the tool you select provides 360 visibility with customizable access features. Administrators should be able to open up or restrict access on a per-user basis. The tool should also integrate new data instantly and double-check existing data for quality.
New technologies that power automation will save time and costs while providing better insights and faster decision-making. Data migration and integration of big data resources can take time, therefore tools must be agile, fast and require little to no human intervention.
What capabilities should data integration technology have?
When searching for data integration solutions, organizations should look carefully into the features and capabilities offered. Some tools are essential for data integration; without them, efforts to transform can be hindered.
Microsoft explains that data integration tools should include:
- Data catalogs: Allow organizations to find and inventory data assets throughout multiple silos.
- Data governance and compliance: Automatically scan assets to prevent risks by meeting regulations, assuring data quality and aligning with your governance policies.
- Data cleansing: Detect data errors such as inaccuracy, duplicates, incorrect format and inconsistency. These tools will replace, modify or delete data errors automatically.
- Data migration: Data must constantly migrate in data integration systems, from computers to storage systems or applications.
- Data ingestion: Secure the proper data gathering processes for immediate and later uses.
- Master data management: Help businesses stick to standard data definitions, classifications and categories through taxonomy to help establish a single source of truth.
Data integration: The ultimate checklist
Organizations use data integration checklists to ensure their frameworks and policies progress and reach milestones.
The following is a comprehensive data integration checklist:
- Develop a business case and clear strategy: Have a clear strategy to guide the entire data integration process and revise it periodically as goals and datasets change.
- Build a robust data integration culture: Build a solid data integration culture in which all staff is familiar with data best practices. Offer training and resources specific to data integration and data management to upskill your workers.
- Determine teams and systems: Define your data integration teams and leaders and what data needs to be integrated.
- Select appropriate architecture: Analyze and define where your data integration system will be hosted: cloud, hybrid, or on-premises.
- Work on data quality and backups: Before starting your transformation, clean up your data and ensure it meets good data quality standards. Creating reliable backups is essential before data manipulation begins.
- Choose your technology: Find the right technology and tools to support your data integration strategy.
- Benchmark progress, adjust and set new goals: Data integration should be a circular process that restarts once the final step is reached. Check your progress, adjust and set new goals, and constantly encourage data integration, data quality and innovation.
Benefits of developing a data integration strategy
The benefits of data integration are endless. Better data leads to better collaboration, increased return on investment, enhanced sales and customer experiences, and business maturity.
As data modernizes operations and potential use cases, products and services, data strategy is changing the way the world does business. Remember, keeping up with data-driven companies is not just about the technology but about a shift in mentality and culture leading the way. To stay ahead of the curve, start with a simple data integration strategy and continue to build on it, regardless of your line of business or the size of your organization. For the maximum chance of success and a stronger overall data strategy, align your data integration plans with equally robust data governance and data quality strategies.