Data is the new oil. Having lots of data available is a good thing. But just like crude oil, data needs its own refinery – to filter and ensure that data transforms into meaningful insights.
With information being collected through a wide range of sources like systems, devices (IoT), enterprise servers, documents, logs, and publicly available data like news and weather, just to name a few, it is difficult to manage all of that data and ensure its integrity.
Ensuring data accuracy and relevancy and making sure it is accessible to everyone who needs it, including technologies that use data to inform operations, is critical to business outcomes. However, at the same time, quality over quantity may be a concept that comes into play when dealing with data and taking a more curated approach may help bring better end results.
When data is being used by technologies like AI and ML, it’s important to start by thinking about what types of datasets are needed for optimal performance.
- Do existing datasets provide meaningful, accurate and actionable insights?
- Are there any additional types of data that you need?
- If you don’t have enough data, do you have a strategy to collect the data you need?
As for making sure that datasets are accessible, that’s where integration comes in. Data integration is the process of collecting data from multiple sources, both internal and external, and bringing it all together to create a unified view of all available information. Integration offers a way to address data silos by connecting systems and standardizing data formats.
Integration plays a vital role in the digital transformation of the supply chain.
In addition to improving connectivity and visibility throughout the supply chain, data integration can help improve data quality. The existence or availability of data is meaningful only if that data is accurate, complete and consistent. It’s extremely common for supply chain professionals to have concerns about the reliability of the data available to them.
To get the most out of data integration, it’s important to start with developing a strategy to evaluate existing data and address potential gaps. An integration solution also feeds into process automation and optimization, which drives process efficiency.
Developing a Data Strategy
There is no one-size-fits-all approach to data integration and it’s possible that multiple approaches may be needed to deliver the most significant results. It’s important to partner with a company that understands your processes and the needs of your company and how technology can best help you. Collaboration and one-organization with a singular view to common goals in key to success.
Through Bristlecone’s digital consulting and integration and automation services, we can work with you to find a solution that will meet the needs of your company. Not just the needs your company has today, but also your company’s future needs. Our solutions are designed to help you thrive on change by overcoming obstacles like inadequate supply chain visibility, poor data quality and slow data processing times. Contact us to learn more.
