Click to learn more about author Jeff Kinard.
2020 saw a rapid acceleration in digital transformation, and this trend shows no sign of slowing down in 2021. The smart factory and plant now incorporate an array of connected technologies, all generating a vast volume of data. As a result, data will continue its exponential growth, with IDC anticipating that by 2025, data worldwide will hit a staggering 175 zettabytes.
This has presented many industrial organizations with the problem of managing the sheer volume of data that connected devices and services are generating. Many are drowning in information and need help to garner actionable business insights to track, analyze, and improve processes and productivity. This is putting effective Data Management firmly in the spotlight as otherwise, enterprises will have invested in automation and other technology innovations but will not be able to realize the operational benefits.
Quality master data is vital to fuel predictive maintenance strategies. Without it, effective and efficient maintenance and operations will remain out of reach for organizations. This problem is magnified for heavy asset operators as poor quality data can significantly impact the bottom line and have safety implications. Data cleansing, governance, and management are critical if organizations want to streamline maintenance work and eliminate costly disruptions. An organization that makes decisions based on poor quality data opens the door up to financial and, in the case of many predictive maintenance situations, safety implications.
How Can Organizations Improve Data Quality?
organizations strive to manage data effectively and efficiently, they will
increasingly need to integrate intelligent technologies, including artificial
intelligence (AI) and machine learning (ML). They will use the power of these
technologies to help aggregate and interpret the vast array of available data.
With AI and maintenance software, enterprises can garner meaningful insights
and data points to optimize maintenance efforts. Intelligent technologies are
critical as organizations strive to have the right insights to keep their
critical assets productive with minimal downtime. For example, ML can detect
failure patterns in data, helping businesses maintain equipment health more
As enterprises look to predictive maintenance to help optimize operations, they must focus on Data Quality to ensure that they have an accurate, single view of data to drive maintenance decisions. Armed with quality data, organizations can schedule and distribute necessary downtime to cause minimum disruption and prevent unplanned outages. Quality data drives better decision-making, improved business processes, and greater competitiveness.
The rollout of 5G will only add to the explosive growth of data, putting even more pressure on organizations to transform how they cleanse and manage their data. Effective Data Management is the key to creating a more competitive organization, but traditional approaches will fail in our connected world. By prioritizing Data Quality and integrating intelligent technologies, organizations will improve Data Governance and Master Data Management. For industrial enterprises looking for a roadmap to realize the benefits of digital transformation, focusing on quality master data is the key to success in 2021.
Credit: Source link