Successful Data Integration: It's All in Your Approach
Your initial approach to data integration can either make or break the success of the entire project. There are several crucial steps to be taken in the beginning stages of a data integration effort, before actual “integration” occurs.
At the heart of it, data integration is about bringing data together to create a comprehensive, accurate, information source. For a data integration project to produce such a source, careful measures must be taken. Poorly planned and executed data projects can yield disastrous results. A single misstep can taint a data pool or remove vital information, rendering once accurate data irreparably useless.
To avoid this, a look-before-you-leap approach is recommended, but at a more granular level: Look, analyze, consult, document, communicate, plan, prep…and then leap. Below are a few early-stage data integration best practices that can protect your valuable data and guide your project to successful completion.
Best Practices for Data Integration Success
Address Data Heterogeneity
When performing data integrations, the data sources you are combining hold data in different formats. This can include structured data, such as the information stored in fielded databases, and unstructured data, such as documents, videos. and images. Whatever the medley of data, you need to address the heterogeneity, and the manner in which this is done is very important. Detailed planning and analysis is imperative. This should include careful data mapping and choosing a standard format. The metadata within unstructured files (including geotags, transcripts and descriptions) can be used to help standardize unstructured sources like images and videos. You then need to determine whether various applications used within the organization can work with your chosen standard data format. If not, measures need to be put into place to address that before any integration begins. Otherwise your efforts to synchronize and streamline might be undermined by collateral processing issues.
"Unstructured data is perhaps the most challenging to integrate, but also the most prevalent type of data, comprising about 80% of business data."
Carefully Evaluate Data Quality
When combining data from different sources, the quality will undoubtedly vary. It is essential to evaluate data quality in source systems before combining them. Poor data quality is contagious and can taint databases that are otherwise in good shape. Tainted or corrupted data causes a number of harmful ripple effects, including irreparable damage to data accuracy. Once data quality is evaluated, it is recommended that developers and users work together to determine ongoing quality assurance procedures and controls that will remain in place after the integration.
Safeguard Data Compliance
During the process of homogenizing and combining data, it is crucial to ensure that even seemingly minute data points are not lost in the shuffle. Data managers, modelers, and developers should consult a compliance analyst before choosing to remove any data. Some metadata, such as log information that proves data origin or manipulation, may not seem mission-critical when working with giant data sets. But in reality that information may be required for compliance reports and audits and therefore invaluable in its own right.
Include On-going Automation
Data integration is never “done”. Data decay begins the minute a data point is created and data exchanges and transfers are permanent fixtures in day-to-day operations. It’s important to plan for your data’s future, putting measures in place to maintain acceptable levels of accuracy and quality. During the early stages of data integration, is best to arrange the set-up of data integration automations and quality checks. Automating regular data exchanges reduces the likelihood of human error, which is often introduced during manual processes. Additionally, some automated integration tools offer code and process management as well as documentation. Therefore, processes can be created and reused rather than redeveloped over and over again.
Ensure Buy-in and Communication
Effective communication and internal buy-in are vital to the success of a data integration project. Most of these projects affect a variety of parties outside of IT, including various departments and lines of business. Your organization’s senior leaders must act as champions for your data integration project, thereby sanctioning the time and resources needed to support thorough efforts. In addition, the project team needs to include a well-rounded collection of skillsets, including those of data managers and modelers, compliance analysts, representatives from various user groups, and project communicators. Having the right team on board, combined with proper scoping and thorough requirements, creates a recipe for success.
Be Open to Using More Than One Type of Tool
Some data integration tools were designed to work with structured or semi-structured data and excel at it. Others were built to integrate unstructured data. And some have evolved to work with both. These days, most companies manage both types of data, ranging from on-premises legacy systems (structured) to social media and web data (unstructured). Depending on the tools available and the individual needs of the organization (current and future), it might be necessary to use a combination of tools for data integration, such as an ESB combined with an integration platform as a service (iPaaS).
If thoroughly planned and prepared, your data integration project can maintain the integrity of existing data sets, while adding an invaluable level of insight by combining them. It’s all in how methodically and thoughtfully you approach it.