From machine learning and artificial intelligence (AI) to robotics, more and more companies are embracing automation to drive process improvements across their business. Unfortunately, they’re falling short of achieving that result. The leading factor holding them back is data accuracy.
Three escalating trends: Acceleration of AI, data fragmentation and complex ownership structures
When AI is used to drive business process automation within an environment that’s lacking in data quality, the cost of error increases by a 10x factor. As the path from concept to implementation moves at lightning speed and each process amplifies far more quickly, any error at any step along the way also spreads rapidly, when it comes to process automation via AI.
The potential risks and its negative impact are further heightened with the increasing fragmentation of data, as businesses of all sizes are creating data at an exponential rate from just a year ago. Today, they also need to figure out how best to access that data from countless sources – in the cloud, on premises, from IoT devices and more apps – or via hybrid environments.
Remember the simpler days when IT departments had far more control of company data? That’s ancient history today, as data ownership structures are becoming ever more complex, given increasingly distributed enterprise systems and the Cloud. Consider this: Who owns the data in Salesforce? And data ownership is even more complex when it comes to IoT: there is no standardization, as companies are using different approaches to regulating the transfer of data control and title.
Beyond these three trends, there’s an emerging fourth to watch out for. Poor data quality ultimately leads to decreasing engagement among users of the systems: when they don’t trust the data, they are likelier to abandon the system impacting their KPIs and success criteria.
The imperative to combine data governance with productivity improvements
Incorporating data governance into process improvement initiatives are table stakes for an effective outcome. This is not to say that a “boiling the ocean” enterprise-wide effort is required to maximize the yield of the initiative, but rather it takes a pragmatic blend of data quality, ownership and workflow within it. It also requires working with solution providers who address data quality at the core of productivity improvements.
Consider the following to solve for data accuracy:
- For automation to succeed, it’s critical to have transparent analysis and controls through operational reporting. This is needed for gaining real-time visibility into potential issues so they can be addressed quickly before turning into significant challenges.
- Solving for data fragmentation requires having a common way to access disparate data sources in real time. This can be achieved with standards-based connectors and tools for managing access to live data in business applications. In addition, utilizing governance catalog tools can aid in understanding which applications contain which data.
- The complexities of data ownership demand rigorous data governance policies and the ability to map those catalogs and clearly identify ownership of those catalogs across the enterprise.
Data accuracy is the absolute keystone for any data and analytics program, whether it’s being used for business reporting or for more advanced AI capabilities to achieve business process automation. Businesses need to act quickly to build and deploy a high-quality data foundation as the complexities of AI, data fragmentation and ownership continue to accelerate, making separating bad data from good data more difficult – and costly – than ever.