Data integration involves bringing together technical and business processes that combine data from disparate sources both traditional and non-traditional into meaningful datasets for business intelligence and analytics. Enterprises gain insights from this big data facilitating better and more timely decision-making that help ensure business competitiveness. However, one major challenge that may arise with the integration of traditional and non-traditional data sources is the vast amount of data generated in the governance ecosystem.
A complete data integration solution integrates data collected from multiple on-premises and cloud sources to support an efficient, business-ready data pipeline for DataOps. Data architecture defines the flow of data from such source systems and the workflow to capture, cleanse, normalize, and store the data for processing.
Techniques of data integration
A typical data integration architecture addresses data governance policies, security, data privacy, and data quality requirements along with helping enterprises consolidate data into a single, trusted view for analysis. The architecture establishes the working environment for the application of various techniques including
ETL (extract, transform, load)
Extracting, transforming, and loading data from multiple data sources to a single data store which is then loaded into a data warehouse. This technique of cleansing and preparing raw data in a staging area instead of a source system like a data warehouse or any other target system improves performance and mitigates the risk of data corruption.
ELT (extract, load, transform)
Extracting and loading raw data from multiple data source locations into a target data lake or cloud data warehouse, where the data can be transformed when required. This technique is ideal for supporting artificial intelligence, machine learning, and predictive analytics, among other applications where real-time data is used to capture intelligence and gain insights.
Data replication is another data integration technique. It delivers near-real-time data synchronization and distribution with low-impact, moving log-based data from one database to another database in order to synchronize and consolidate the information (for operational use and/or for backup). Data is replicated at regular intervals, in real-time or sporadically, depending on the requirements of the enterprise.
Roadblocks for successful data integration
As enterprises continue to leverage big data, integrating data originating from relational databases, streaming data services, and many more real-time sources has become more complex. Well-designed data integration processes ensure that the data is managed, governed, and trusted enabling hidden business intelligence extraction. Integration efforts can be hampered by
– The difficulties in governing data originating from numerous sources.
– The challenges inherit with managing multiple data integration tools.
– A business landscape that allows data to be accessed, edited, copied, and duplicated by many data handlers.
– The slow movement of data from the Cloud and data lakes.
In order to avoid some of these roadblocks, enterprises must begin to consider data as a corporate asset versus a by-product of the business. Beyond design considerations, the implementation of modern technologies like AI has proven effective in mitigating many big data integration challenges.
How can AI help?
Harvard business review predicts that AI will bring $13 trillion into the global economy over the next decade and that “…companies that excel at implementing AI throughout the organization will find themselves at a great advantage”. Not only does AI greatly diminish the heavy lifting often associated with data integration efforts, AI and machine learning technology has shown to improve overall data integration project results such as
- Data mapping powered by AI can automate data transformation mapping by providing advanced features and agile data mapping predictions with machine learning algorithms. AI also enables users with less technical knowledge to embark on the data mapping exercise with simple drag and drop features, thus, reducing the time required to create data mappings.
- The autonomous learning capability of AI and machine learning enable enterprises to learn more about hidden patterns and trends from large datasets, so that accurate business intelligence and insights are derived from them through applying statistical models.
- Data processing with machine learning-powered data integration tools can parse big data generating precise data models that require less human intervention whereas traditional data integration tools require much longer setup and processing times to handle volumes of semi-structured or unstructured data formats.
Data integrations infused with AI and machine learning solve complex data processing problems and improve integration flow propelling business forward, and providing business advantage across the enterprise. These new-age integration tools help enterprises gain insights from big data facilitating better and more timely decision-making that help ensure business competitiveness.
If you would like to learn more about the value that AI and machine learning would bring to your business, send us your query to email@example.com. Intellect Data, Inc. is a software solutions company incorporating data science and artificial intelligence into modern digital products with Intellect2TM. IntellectDataTM develops and implements software, software components, and software as a service (SaaS) for enterprise, desktop, web, mobile, cloud, IoT, wearables, and AR/VR environments. Locate us on the web at www.intellectdata.com.
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