Efficient data management

The data management industry is changing at lightning speed. The rise of AI and machine learning, the Internet of Things and new cloud services are just some of the factors driving this rapid change. Still, many organizations are finding it difficult to keep pace with these changes and have efficient data management.

In fact, a recent survey found that data management professionals see their role as the most stressful job in technology today. In this article, I attempt to translate 5 key data management challenges that will affect how your organization stores and analyzes data in the future.

The massive deluge of data being generated by the IoT

The Internet of Things is the network of physical devices that are connected via the Internet. By 2025, there will be an estimated 27 billion connected devices. This hyperconnectivity means that there will be huge amounts of data flowing between these devices and organizations.

One area that will be transformed by IoT is manufacturing. Many manufacturers already use sensors to track their supply chains. This allows them to monitor inventory and ensure that products meet customer expectations. But sensors will not only be used for tracking.

They will also be used for intelligence. For example, sensors will be able to monitor machinery and provide feedback on its performance. This will help reduce breakdowns and enable remote diagnostics. All this will significantly increase the amount and type of data generated.

It will also change the way data is distributed. Data will no longer be centralized. It will be distributed across a wide range of devices, systems and locations. This will make managing and analyzing it extremely difficult.

Data is getting bigger, and faster

Big Data has become a key part of many organizations’ data strategy. But as organizations collect more data, they have to store it for longer. That’s because data retention policies are getting stricter.

Regulations such as GDPR or HIPAA require organizations to retain data for much longer. And as organizations store more data, they are also having to create more copies of it. Why? Because they need to protect against data loss from disasters, cyberattacks or human error. This means companies need to invest in more storage devices. They need to increase the number of data centers where this data is stored. They also need to staff their IT teams with more experts. This is creating significant pressure on budgets and resources.

Data automation will be a necessity

Organizations are collecting more and more data from existing sensors, devices, systems and applications. This is creating a huge challenge for IT teams. They have to collect and manage all this data. It’s not an easy task. But as organizations collect more data, they are finding it much harder to analyze it. That’s because most of the data is unstructured, which means it can’t be easily used in IT systems.

This is why data automation is becoming increasingly popular. It allows organizations to significantly reduce the time and effort involved in digesting unstructured data. It can also be used to help interpret the meaning of unstructured data.

Data quality remains an issue

Just because organizations have more data to analyze does not mean they have better data. In fact, the opposite is true. As organizations collect more data from more devices, it is harder to ensure data quality. And the problem is growing.

According to a recent IBM survey, nearly three-quarters of companies expect data quality issues to increase over the next two years. Increased reliance on AI and machine learning is creating more problems. The technologies are highly accurate and designed to always provide the right result. Therefore, if the data is inaccurate, the AI will make mistakes. Organizations can minimize data quality issues by increasing investment in data quality initiatives. This could involve hiring more data quality experts, investing in more software, and improving staff training.

Cloud services are growing, and so is their impact on data management.

Cloud services are becoming increasingly popular. They provide access to more computing power. They make it easier to share data between different organizations. And they provide greater flexibility. But as more organizations move to the cloud, they need to be aware of the impact this will have on data management.

In fact, many organizations are struggling to cope with the scale of data they have at the moment. This makes managing data across different cloud services a big challenge and makes it difficult to ensure that data complies with regulations such as GDPR and HIPAA. But there are ways to overcome these challenges. One solution involves using a cloud data management tool, which can help you identify, govern and secure data wherever it is stored, as well as help you move data between different cloud services.

In conclusion, efficient data management is no longer a one-person job. It is a team sport. Organizations will need to collaborate more than ever to stay ahead of the curve. To do so, they need to integrate data from different sources, systems and applications. And they need to do it in a way that enables the continuous use of data to drive business results.