Despite the global digital acceleration of data use cases, many companies are still struggling to be data-driven. Abandoning decision-making and the isolated data analysis processes of the past are proving not to be a technological challenge, but a strategy, planning and culture challenge.
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The tools for organizations to transition effectively are highly developed and available today. From cloud computing to machine learning and business apps, companies have many options for improving performance by making better use of their data.
However, creating a data-driven business 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 on one platform, while other teams, such as sales, inventory, manufacturing, and IT, use the same? In this report, we dive into the essentials 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 silo sources to provide users with a unified view. Benefits of data integration include better data management and quality, improved visualization, better decision-making, and better performance. Standardizing data is essential for successful data integration, as multiple teams – some of which may not have advanced IT technical knowledge and skills – must access and use the data system.
Combined with tools such as machine learning and predictive analytics, unified data insights can have a significant impact on a company’s operations, enabling it to detect risk in advance, meet compliance across the board, drive sales and detect new growth opportunities . Data integration aims to create a single point of access for data storage that is available and has good quality standards. But moving data from one system to another and meeting the challenges of big data with excellence requires a data integration strategy.
Tips for creating your data integration strategy
Data integration involves completely transforming an organization’s data systems and how they operate. The migration and transformation of data is challenging and technical in itself. However, other things, often neglected, are vital for a data integration strategy to work. Here are some tips for your journey to 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 technology from suppliers, 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 business has its goals and objectives and needs to understand what data will help them achieve them. In line with a company’s mission, values and data governance strategy, data leaders must lead the data integration strategy.
Once organizations have answered which business goals their data integration should support, they can ask other questions. Access and availability must be clear and transparent. While executives and critical stakeholders may need full access and visibility to all unified data, other departments require limited access. In addition, roles and responsibilities must also be established.
Ideally, organizations should strive to integrate independent systems into one master data warehouse. To accomplish this task, leaders must consider what data to integrate, who will form the data integration team, and where the data integration will take place: in the cloud, on-premises, or hybrid.
What data should be integrated?
The data you want to integrate into your business depends on your business goals. It also depends on the industry you work in and any regulatory standards or competitive benchmarks you have to meet. But regardless of your specific data and what you hope to achieve with it, most companies should integrate data into these categories:
- Financial: Financial planning and budgeting are essential to enable data-driven business decisions. Financial data and systems to be integrated include accounts payable, accounts receivable, ledger, and consolidation systems.
- Sales and marketing: Having silos for sales and marketing systems affects customers and your operations. Data related to customers should be integrated into a unified view. Inventory, manufacturing, and distribution data may also need to be integrated, depending on your business.
- Human resource department: HR data systems help companies attract and retain talent more effectively and provide them with tools and resources. HR data to integrate includes payroll, leave management, workforce scheduling, basic accounting 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 what type of data integration system is best for your operations.
The most common business 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 discrete data sets.
Organizations also need to decide where the data warehouse will be created. Cloud or hybrid environments give teams more flexibility to adopt new technologies as they come to market. Operating strictly on-premises makes it more difficult for companies to be at the forefront of innovation because of the costs associated with adopting new technologies.
Choosing the right data integration technology
Once you’ve established a clear strategy, it’s time to nurture a data integration culture, define the framework’s processes, and choose the technology that best fits your goals.
There are several excellent data integration vendors and solutions, each offering diverse offerings. Consider the following factors when determining which data integration solution will work best for your business:
While the 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 anyone in your company to use the platform.
Visibility, access and performance
It is important to ensure that the tool you select offers 360 degree visibility with customizable access features. Administrators must be able to open or restrict access on a per-user basis. The tool should also integrate new data immediately and double check existing data for quality.
New technologies that enable automation will save time and costs while providing better insights and faster decision-making. Data migration and integration of big data sources can take time, so tools must be flexible, fast and require little to no human intervention.
What capabilities should data integration technology have?
When looking for data integration solutions, organizations need to look closely at the features and capabilities on offer. Some tools are essential for data integration; without them, efforts to transform can be hindered.
Microsoft explains that data integration tools should include:
- Data catalogues: Enable organizations to find and inventory data assets in multiple silos.
- Data management and compliance: Automatically scan assets to avoid risk by complying with regulations, ensuring data quality, and aligning with your governance policies.
- Data cleaning: Detect data errors such as inaccuracy, duplicates, incorrect format and inconsistency. These tools will automatically replace, modify or remove data errors.
- Data migration: Data must constantly migrate in data integration systems, from computers to storage systems or applications.
- Data recording: Secure the correct data collection processes for immediate and later use.
- Manage master data: Help companies 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 that their frameworks and policies are progressing and reaching 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 review it periodically as goals and data sets change.
- Build a robust data integration culture: Build a solid data integration culture where all employees are familiar with data best practices. Provide training and resources specific to data integration and data management to upskill your employees.
- Determine teams and systems: Define your data integration teams and leaders and what data to integrate.
- Select the right architecture: Analyze and define where your data integration system is hosted: cloud, hybrid or on-premises.
- Working on data quality and backups: Before you begin your transformation, clean up your data and make sure 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 starts again once the last step is reached. Monitor your progress, adjust and set new goals, and continuously drive 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, greater return on investment, better 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 businesses isn’t just about the technology, it’s about a shift in mindset and culture leading the way. To stay ahead, start with a simple data integration strategy and build on it, regardless of your industry or the size of your organization. For maximum chance of success and a stronger overall data strategy, align your data integration plans with equally robust data management and data quality strategies.