Business decision-makers, and especially CIOs, are confronting an inconvenient truth - to drive successful digital transformation initiates, there can be only one single source of truth when it comes to their data. Among large-scale companies, in particular, the data deluge has reached almost unmanageable levels. Big data and associated 4Vs as a concept is not a new challenge. It’s the repeat data that’s new for them. This is the data that resides in numerous places across an enterprise ecosystem and fails to be consistent.
Let’s take an example.
You may have a retail data set - such as prices, a current stock gauge, a future demand prediction or seasonal stats — in a retail local store. You may have that same data on a different system, in a different place or being merged and transferred following an acquisition. Multiple databases, computers, and data warehouses are quite obvious consequences of the big data surge. Only one anomaly can threaten the validity of the datasets completely.
This is where the role of data integration solutions come into play. Organizations are investing in these technologies as part of their digital transformation efforts. Simply, data integration offers a way to bring those disparate networks together and create a single “source of truth” that not only support insights delivery but also decision-making.
But reaching this pristine, consistent, easily viewable, and unique central storage system is not simple. It calls for the heavyweight digital players of this world, and with that also comes a huge investment, significant time spent, failures before successes and a delay in achieving the goal behind the investment. To achieve business success, companies must not only focus on the presumed need to bring data into one location but also on the yielding capability of this single source of truth.
This is where Artificial Intelligence (AI) and Machine Learning (ML) shine. Organizations can leverage these transformative technologies to deliver suggestions that enables data mapping - at speed and scale.
Business users with minimal technical expertise can analyze existing data mappings and provide suggestions on building data maps. By leveraging these solutions, users can access recommendations backed by confidence levels. And so, it supports each mapping suggestion with the rationale along with possible cautions.
AI and ML help users map complex, bidirectional data while keeping the data integrity and accuracy intact. Thus the issue created by repeated data sets can be removed. By automating many steps, they free companies’ IT resources to focus on more strategic tasks. It promotes the rapid development of data maps that can be used across trading partner ecosystems. Non-technical business users can use self-service capabilities such as shared templates to integrate data to create a single source of truth and add new customers to the existing ecosystem, improving the ease of doing business and ultimately driving revenue.
When AI and ML are used in combination with data integration, it becomes easier for organizations to consolidate different types of data, residing in different locations or places. By doing so, they can speed up insights delivery, decision making, and ultimately revenue.