To achieve a unified view of data that is sourced from different locations and formats, it is necessary to have an established data integration solution. This can also include occasions when two companies are merging or in the consolidation of internal applications. Data integration can also be beneficial in the creation of a better and more comprehensive data warehouse; ultimately leading to a more accurate and effective analysis.
Data integration is the process of taking data from many disparate sources and making it usable. As the number of sources continues to grow, the need for effective data integration also continues to grow in importance. There are a number of benefits associated with an effective data integration solution. Some of these include:
- A single and reliable version of truth that is synced and accessible across locations
- An enhanced capacity for analysis, forecasting, and decision making based on accurate data
- A fully comprehensive view of an organization and its customers
- Data availability throughout a business and its stakeholders
Ultimately, data integration lays the foundation for effective Business Intelligence (BI) and the effective decision making it enables.
Understanding 4 Components of Data Integration
Data integration is a term that covers a range of subtopics. A few of the most important categories include:
- Data migration: Moving data between locations, formats, or applications
- Enterprise Application Integration (EAI): Enabling interoperability between systems
- Master Data Management: An effort to create one single master reference.
- Data aggregation: Combining disparate data sources
- Data Federation: Data is combined into a virtual database.
- Data Warehousing: Data is combined into a physical database.
Image source: Astera
Data migration is the process of moving data between locations, formats, or applications. It is often caused by the introduction of a new system or location for the data. One common cause today is the shift from on-premises to cloud-based storage and applications.
There are a few types of data migration to keep in mind:
- Storage migration: Moving data from existing arrays into their more modern counterparts to achieve faster performance, scaling, and data management tasks such as cloning, backup, and snapshots.
- Cloud migration: Moving data, applications, and other business elements to the cloud or between clouds. Often also requires a storage migration.
- Application migration: Moving an application from one environment or another.
It is important to consider the difference between integration and migration. Integration is the combination of processes that enable data from different sources to be turned into business insights. Data migration is a different process that involves the transfer of data between storage types, formats, architectures, and systems. Another distinction is that integration generally requires collection of data from outside sources, whereas migration often refers to internal movements of data.
Enterprise Application Integration
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Enterprise application integration (EAI) is a category of approaches to achieving interoperability between different systems that businesses utilize. Specifically, it requires approaching problems related to the modular architecture of the organization. Some key factors that it includes are:
- Interoperability: Managing the different languages, operating systems, data formats of components so that they can be connected.
- Integration: Creation of a standard process for managing the flow of data between applications and systems to ensure consistency.
- Robustness, stability, scalability: Whatever solution is implemented, it needs to be able to adapt quickly and smoothly to changes within a business.
Prior to EAI approaches, integration was generally managed via point-to-point integration; where a unique connector is built for each pair of differing systems or applications that need to communicate. Today, EAI solutions include models of middleware to help with the centralization and standardization of practices throughout an entire infrastructure.
To meet the needs of modern businesses, a bus-based EAI, known as Enterprise Service Bus ESB software was developed. This software creates an architecture that enables differing applications to interact. Additionally, they set processes, protocols, and rules to enable secure data transfers, route messages between services, and other key tasks.
Master Data Management
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Master data management (MDM) is a discipline that focuses on the cooperation of business and IT to achieve uniformity, accuracy, stewardship, accountability, and semantic consistency of shared master data assets. Master data includes the identifiers and attributes that make up the core of the business – such as customers, suppliers, sites, and more.
Continuous data improvement and a well-executed data quality strategy are key for effective ongoing MDM. To create a single version of truth, it is necessary to harmonize and synchronize multiple data items. In order to support these efforts and more, change management is essential to ensure the adoption of MDM practices and processes throughout an organization.
There are a few reasons why MDM is gaining momentum among businesses:
- The huge impact it can have. Master data is some of the most important data that an organization has and any errors within it will be felt throughout
- The complexity of today’s environment in terms of data volume, availability, and other similar factors
- Compliance and regulatory requirements that have created a need for a deeper visibility and transparency
There are a few challenges associated with implementing a MDM strategy. They include:
- Complexity: Data quality can be varied, especially between legacy systems
- Overlap: The same data may be duplicated across many systems
- Governance: Difficulty in achieving stewardship, ownership, and policies
- Standards: Finding agreement on domain values
There are also a few practical challenges related to lack of qualified talent in the discipline, difficulty in executive buy in, and others; as are common in many IT initiatives.
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Data warehousing is a technology that aggregates structured data from one or multiple sources in order to compare and analyze it to achieve greater business intelligence. It is effective for getting a better understanding of the overall performance of a business because it makes a wide range of data available for analysis. It varies from a traditional operational warehouse because it is designed to give a long-term view of data over time.
Owing to the focus on data aggregation instead of transaction volume, a data warehouse is essential when there are analytical needs that require actions ‘against’ the ongoing performance of an operational database. For example, if there is a need for a complex query on a database, it must enter a fixed state temporarily. With databases that work off transactions, such a state can be difficult to reach – creating a need for another entity to do the analytical work; like a data warehouse.
An additional benefit associated with a data warehouse is that it can be part of the final stage of an ETL (Extract, Transform, Load) process; which ultimately means that with the help of an ETL tool, it can analyze data from multiple sources. To learn more about ETL, make sure to see our blog post on the topic.
Some benefits associated with data warehouses include:
- Improved Business Intelligence
- Rapid access to data
- Increased system and query performance
- Historical intelligence
Some disadvantages associated with data warehousing include:
- Cost of scaling
- Challenges with raw, unstructured, or complex data
- Maintenance costs
Another approach to creating an integrated view of data from diverse source systems is data federation. It is an approach to create a virtual database that does not store the data but contains information about where the actual data is. Regardless of how and where data is stored, it should be presented as one integrated data set. This implies that data federation involves transformation, cleansing, and, if necessary, enrichment of data.
Some of the benefits that come with data federation are:
- replacing extract, transform and load (ETL) processes so that data scientist can shift their focus on data query and analysis.
- reduces data latency since the user knows where the data is and it eliminates the need to construct the data warehouse and the ETL technology to move data into the warehouse.
- simplifying BI for organizations by querying data as a whole.
Data Integration Case Study
Behavior Analyst Certification Board (BACB) is a nonprofit corporation to meet the professional credentialing needs of the behavior analysis industry. BACB’s CRM systems were outdated, highly customized and tightly integrated with their website. Therefore it was not flexible for data transfer. If they decide a change in a business process or in their supporting systems, something else would inevitably break. BACB implemented Adeptia’s application integration tool to synchronize data between a Microsoft CRM and NetSuite, ensure reliable transfers of communication and data with third-party providers, and guarantee connectivity with the company website. As a result, BACB was able to increase ROI and IT department was being able to shift from a reactive to a proactive stance.
Data Integration & Implementation
When it comes to implementing data integration practices, there are a few things that can be kept in mind in order to ease the process. There are three broad categories of data integration that all carry their own sets of best practices:
- Analytic data integration (AnDI): Where actions are in the context of business intelligence or data warehousing
- Operational data integration (OpDI): Making data available throughout applications and databases
- Hybrid data integration (HyDI): Includes endeavors such as master data management and similar customer and product information management
However, generally speaking, there are a few general tips and reminders to keep in mind regardless of which category the task may fall into; particularly when it comes to getting executive approval of any new efforts:
- It should be thought of as a process that adds value – similar to the manufacturing of a product where you begin with raw materials (data) and make them into something ultimately more valuable
- There is an aspect of sustainability; data integration helps to lower the carbon footprint of data centers by eliminating redundant and erroneous data and virtualizing hardware servers
- Effective data integration requires collaboration across both technical and business operations – resulting in a more congruent and aware team across departments
- Consider data governance from the beginning – and also consider how proper data integration can be supportive of effective data governance
Impact of Machine Learning
Just like every other data management strategy, data integration tools can benefit from machine learning algorithms. Machine learning can automate repetitive tasks such as coding SQL scripts to migrate data or to aggregate data for reporting and analysis.
Machine learning algorithms help organizations find useful data from different sources and combines that data with enterprise datasets. AI technology is better than human workers when it comes to data processing. With machine learning techniques, organizations can faster and accurately analyze the unified view of data and generate actionable insights. Feel free to check our article about AI capabilities in analytics.
Machine learning algorithms can also help businesses during data protection processes. AI-enhanced data integration tools can automatically detect sensitive or personally identifiable information.
For more on data integration
Here is a recommended article list for you on data integration:
- Data Integration Tools: In-depth Guide
- Business Best Practices for Web Data Integration
- Integration Platform as a Service (IPaaS)
And if you believe your business would benefit from a data integration solution, we have a data-driven list of vendors prepared.
We will help you choose the best one that is suited to your needs and specifications:
Featured image source: Excella
Cem has been the principal analyst at AIMultiple since 2017. AIMultiple informs hundreds of thousands of businesses (as per similarWeb) including 60% of Fortune 500 every month.
Cem's work has been cited by leading global publications including Business Insider, Forbes, Washington Post, global firms like Deloitte, HPE, NGOs like World Economic Forum and supranational organizations like European Commission. You can see more reputable companies and media that referenced AIMultiple.
Throughout his career, Cem served as a tech consultant, tech buyer and tech entrepreneur. He advised businesses on their enterprise software, automation, cloud, AI / ML and other technology related decisions at McKinsey & Company and Altman Solon for more than a decade. He also published a McKinsey report on digitalization.
He led technology strategy and procurement of a telco while reporting to the CEO. He has also led commercial growth of deep tech company Hypatos that reached a 7 digit annual recurring revenue and a 9 digit valuation from 0 within 2 years. Cem's work in Hypatos was covered by leading technology publications like TechCrunch and Business Insider.
Cem regularly speaks at international technology conferences. He graduated from Bogazici University as a computer engineer and holds an MBA from Columbia Business School.
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