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Data streams into an organization from all corners of the business: from finance to marketing, sales to HR. But this provides only limited value if viewed as discrete silos. To unlock the full potential in business data — and to create a catalyst for digital transformation — it needs to be integrated. This increases the insight that can be achieved, moving from piecemeal views of specific applications to a detailed overview of interrelated processes across business functions.
When a business has properly integrated data, the ’cause and effect’ — of the impact one area of a business makes on another — can be seen. Achieving this level of understanding provides a platform from which businesses can make continuous improvements to processes, products and services. Integrating data in to a central hub for analysis — via Business Intelligence (BI) and analytics tools — also significantly helps data security and governance efforts. But it brings with it several inherent challenges.
No two businesses’ data is the same. As companies grow, so does their IT infrastructure. For example, instead of every office of a company using the latest version of a single vendor’s ERP software, they might have some offices using an online platform such as NetSuite with others using separate instances of an old version of Microsoft Dynamics.
There might also be another, parallel consideration, that of bespoke systems that have been customized to meet specific business needs. If an ERP system was customized to suite a particular company’s processes ten years ago — and if that company’s business model has remained relatively static — then that ERP software is likely to still be in place.
Dealing with disparate types of data — be it multiple ERP, CRM or HR systems, perhaps — as well as where and how they are located are key considerations of data integration. There might also be another, parallel consideration, that of bespoke systems that have been customized to meet specific business needs. If an ERP system was customized to suite a particular company’s processes ten years ago — and if that company’s business model has remained relatively static — then that ERP software is likely to still be in place.
Given all these considerations, it’s clear that the task of data integration should not be taken lightly. And, given its complexities, it’s certainly not a task that should be undertaken with ad-hoc manual labor. Manual data integration is often inefficient and leads to inaccurate results. It is also extremely difficult to enforce data security and governance.
Regardless of the IT landscape, and no matter what the data source — from a legacy, customized on-premise ERP to a cloud-based CRM system — automated data integration needs to be in place to provide true business-wide visibility.
ZAP Data Hub is an automated, software-based data integration system that simplifies the entire process from the get-go with an array of pre-packaged connectors to all common data sources. It then automates data integration, removing the risk and cost of manual labor.
Automation of data integration — by way of a pre-built set of extract, transform and load (ETL) routines — avoids the pitfall of manual labor. And, as a result, removes time constraints, financial burdens and the potential for mistakes and human error. Avoiding manual labor and manual intervention brings with it two additional, important benefits: heightened data security and more robust data governance.
Going one stage further, if automated data integration also handles data profiling and transformations across all data sources, business data itself can be streamlined and rationalized across an organization. Data transformations are then carried out in the same way every single time, delivering a constant stream of comparative, useful information and marking an end to the perils of legacy, disparate and user-customized systems.
Computer Weekly concurs with this approach. “BI data integration is a must for leveraging enterprise-wide data,” they report. “At the same time, BI data integration on that scale is very challenging.” Setting out the Top five BI data integration challenges, the second point on their list is a clear warning: neglecting automation. “Automated BI data integration is less challenging,” they explain, reasoning that “the less the manual intervention, the fewer the errors and data integration challenges.”
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