The topic of data integration has been around forever. Before we used technology to manage data, we integrated data in manual ways. At the time, we needed to integrate simple data structures such as customer data with purchase data. As the industry progressed, we have gone from managing flat data files and integrations to using applications to creating databases and data warehouses that automates the integration of data. The early data sources were few compared to today, with information technology supporting almost everything that we do. Data is everywhere and captured in many formats. Managing data today is not a small task but a much bigger job and grows exponentially every year.
What is Big Data Integration?
Data integration is now a practice in all organizations. Data needs to be protected, governed, transformed, usable, and agile. Data supports everything that we do personally and supports organizations’ ability to deliver products and services to us.
Big data integration is the practice of using people, processes, suppliers, and technologies collaboratively to retrieve, reconcile, and make better use of data from disparate sources for decision support. Big data has the following characteristics: volume, velocity, veracity, variability, value, and visualization.
- Volume – Differentiates big data from traditional structured data managed by relational database systems. The number of data sources is much higher than the conventional approach to managing data inputs.
- Velocity – Data source increases the rate of data generation. Data generation comes from so many sources in various formats and unformatted structures.
- Veracity – Reliability of data, not all data has value, data quality challenges.
- Variability – Data is inconsistent and has to be managed from various sources.
- Value – Data has to have value for processing; all data does not have value
- Visualization – Data has to be meaningful and understood by a consumer
Integration of big data needs to support any service in your organization. Your organization should run as a high-performing team sharing data, information, and knowledge to support your customers’ service and product decisions.
Big Data Integration Process
Big data integration and processing are crucial for all the data that is collected. Data has to have value to support the end result for the usage of the data. With so much data being collected from so many sources, many companies rely on big data scientists, analysts, and engineers to use algorithms and other methods to help derive value from the data received and processed.
The processing of big data has to be compliant relative to organizational governance standards. Ensure the reduction of risk related to decisions with the data. Help enable organizational growth and enablement. Reduce or contain cost. Improve operational efficiency and decision support.
The basic process is;
- Extract data from various sources
- Store data in an appropriate fashion
- Transform and integrate data with analytics
- Orchestrate and Use/Load data
Orchestrating and loading data into applications in an automated manner is critical for success. Technology that does not allow ease of use will be cumbersome and hamper the organization’s ability to be effective using big data.
Challenges of Big Data Integration
Data is constantly changing. Trends have to be managed and assessed for integrity to make sure the data being received is timely and valuable for decision making within the organization. This is not easy. In fact, integrating big data can often be the biggest challenge. Other Big data integration challenges are:
- Using appropriate data sources to create a single source.
- Consistent use and improvement of analytics to deliver valuable data. Data sources increase, change.
- Creating and maintaining valuable data warehouses and data lakes from the collected data. Improving business intelligence.
One of the biggest challenges besides those listed is the enablement of people to use technology. Organizations should look for technology that provides ease of use for users across the organization, but they also need to make sure they choose data management platforms that are robust enough to meet complex use cases. Products and technologies that are not easy to use will not be used effectively and efficiently to support business outcomes.
Strategies of Big Data Integration
Big data integration strategy has to include the following;
- Data governance – data has to be controlled and needs to follow enterprise standards
- Management of data and risk reduction when storing data
- Ensuring appropriate controls for data compliance
- Management of data quality
- Management of data security
- Understanding of integration needs between tools, consumers and data sources
- Understanding of how, why, where, when and what decisions need to be made and how they are made with data
Big data architecture and platform capabilities must support the broader data strategy. With a good data strategy, you can then consider the tactics and technologies that will be utilized and determine the capabilities needed to support and improve the data driven decision making.
Actian and Big Data Integration
Actian Dataconnect allows organizations to integrate without limits. Organizations can integrate anything, anywhere, anytime, and create a dynamic data cloud for easy access. Automated workflows can be built quickly to support changing business needs and reduce the time and risk associated with manual processes. Integration is seamless, enabling dynamic data usage to support all organizational needs. Business users, integration specialists, SaaS administrators, and others can be empowered to take full advantage of Actian’s big data management and integration capabilities.