Unlike some highly technical concepts, data integration is somewhat easier to understand. It has a smaller learning curve which allows for greater levels of enterprise adaptability. It’s a more intuitive way for brands to leverage large amounts of data. Especially in businesses that frequently touch upon big data, it’s important to have a smarter view of your analytics and metrics.
If you’re looking for ways to better implement data tracking and performance growth, it’s often important to invest in a data integration system. To weight the costs and values of data integration, start by mastering some of the essentials of this modern technique.
Data Integration Essentials
At its core, data integration is a way of consolidating analytics, metrics, and data into one single, usable view. This is paramount for enterprises that have larger spreads of data and information to manage. When you’re juggling disparate strains of information, it complicates the analytics process. It’s that much harder to gain actionable insights and to determine which data points should and shouldn’t be prioritized. Before you fully incorporate integration, you need to begin with the ingestion process. Data ingestion comprises a fair number of steps that lead to higher-quality data. Ingestion includes data cleanings, ETL mapping to build links between streams of data, and even data transformation to ensure streams are usable.
While it may seem fairly procedural, there is no one-size-fits-all approach to data integration. Frequently, it will vary based on a brand’s unique use cases and needs. However, data integration does rely on a handful of common elements. Most regularly, the client or business user will send a request to the endpoint, a master data server. That master server then compiles data from both internal and external sources. After the data is extracted from these internal and external sources, the server combines datasets into a cohesive set of usable data. This dataset proceeds onwards to the business intelligence user or client who can then apply the data packets as needed.
Benefits of Data Integration
A variety of enterprises can leverage and incorporate data integration for smarter business insights. In particular, the Gartner Magic Quadrant Report notes the impact of data integration. On top of a cleaner view of large swaths of data, there are additional applications of this methodology. One of the primary benefits is cost and time-savings. Previously, brands would have to manually compile disparate streams of data. Depending on the approach, this could be a particularly resource-heavy process. Alongside the old adage that time is money, it’s apparent why many brands invest in data integration platforms. With its boosts to the bottom line, data integration is essential for companies across the world.
Data integration also reduces the notable impact of human error. If data streams are improperly compiled or a particular set of data points goes unchecked due to a computing mishap, it can gravely impact your usable data. With data integration, you’re much less likely to encounter these common pitfalls of manual entry and data management. Since a vast number of brands rely on hundreds, if not thousands, of data locations and accounts, it’s much more sensible to avoid a manual process and instead incorporate the streamlined applications of integration. If you don’t want to consistently maintain data updates and account parsing, it’s best to invest in a system that can do so on your behalf.
A Necessary Tool
While some enterprises may consider data integration to be a luxury, it’s a necessity more often than not. Without the streamlined capabilities of a data integration platform, brands run the risk of falling behind the competition. If you want to achieve your organization’s full data potential, it’s a prime idea to find ways to consolidate your data streams.