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We as humans have become dependent on luxuries such as cars, houses, and even our cell phones. But what does our love for manufactured metallic and plastic goods do to the environment? Human activity can be directly attributed to the cause of hundreds of extinctions in the last two centuries, versus the millions of years that extinctions naturally occur. As we progress through the 21st century, humans have changed the world in unprecedented ways. One way humans have manipulated our globe is data mismanagement.
Using AI doesn’t even solve the problem. Workloads might end up creating as much problems than it should solve. These data silos can add up complexity that will drain your Data management department, increased cost of management, lower productivity, loss of time etc., are just few stress data mismanagements can cost you. These data silos can cause haphazard distribution of access to key data which is gross insecurity.
Data is the lifeblood of state development. It is necessary to manage projects, avoid fraud, assess program performance, keep the books in balance, predict future occurrences and deliver services efficiently. But even as the trend toward greater reliance on data has accelerated over the past decades, data mismanagement has lead dangerously off the track. Sometimes it doesn’t exist at all. But worse than that, all too often it’s just wrong.
There are examples everywhere. Last year, the California auditor’s office issued a report that looked at accounting records at the State Controller’s Office to see whether it was accurately recording sick leave and vacation credits. ‘We found circumstances where instead of eight hours, it was 80 and in one case, 800,’ says Elaine Howle, the California state auditor. ‘And the system didn’t have controls to say that’s impossible.’ The audit found 200,000 questionable hours of leave due to data entry errors, with a value of $6 million.
Mistakes like that are morale crushing, and can cause unequal treatment of valued employees. Sometimes, however, decisions made with bad data can have deeper consequences. In 2012, the secretary of environmental protection in Pennsylvania told Congress that there was no evidence the state’s water quality had been affected by fracking. ‘Tens of thousands of wells have been hydraulically fractured in Pennsylvania,’ he said, ‘without any indication that groundwater quality has been impacted.’ But by August 2014, the same department published a list of 248 incidents of damage to well water due to gas development. Why didn’t the department pick up on the water problems sooner? A key reason was that the data collected by its six regional offices had not been forwarded to the central office. At the same time, the regions differed greatly in how they collected, stored, transmitted and dealt with the information. An audit concluded that Pennsylvania’s complaint tracking system for water quality was ineffective and failed to provide ‘reliable information to effectively manage the program.’
When data is mismanaged, consequences are fatal enough to render government enterprise useless. Evaluation of successful program is difficult; taxes go uncollected; services are rendered inconsequential. The list can go on and on and won’t get exhausted.
Third-party problem is particularly significant in data mismanagement. Third party interference makes data lose its credibility. This leads to the question: Can data be ever properly managed?