Formulating a Strategic Data Management Plan to meet Storage Management Metrics through NetApp Training

Strategic data management plan has the potential to change the calculus by why the data management groups purchase, manage, and structure information storage. It is not only a force that drives technological transformation, but it can also be critical for economic transformation as well. As far as effective data management in the definition is concerned, it is all about handling, documenting, organizing, and enhancing available research data. Most organizations are not even aware of the connection between storage management metrics and data management plans, and how providing NetApp training to the employees can achieve these goals.

To that end, let’s discuss some of the important metrics now.

What You Need to Know About Storage Management Metrics

Storage is a part of the foundational technologies that are still considered a mystery in an IT environment. Most people consider it a bunch of high-potential hard drives that holds an organization's data assets. But the moment you start peeling off that definition, you realize how storage is so much more. Also, when an organization face trouble with storage, going down to the root cause can be quite a challenge in itself. It can become highly frustrating to resolve it, especially when it's intermittent.

This is where data management plan comes in. The plan is directly associated with the data storage metrics that are important. These include:

Latency

It is the single most important metric that every data management plan must incorporate. In storage, latency is the time required for an operation to complete on storage. High latency has an immediate and direct impact on workloads operating on that specific storage. If the figure of latency falls between 20 and 30 milliseconds, it is a matter of concern.

Capacity

This one's the most common and essential part of the storage management metrics. Since NetApp training also includes storage management service and tech, it is easier for the trained employees to figure out any existing storage problem with this single metric. The capacity factor highlights when the disk space is running out. People with NetApp training and certification can find out much more than the remaining capacity when this happens. This includes provisioning and de-duplication.

While these technologies complicate the monitoring of capacity for a layman, they are easily readable and understandable by a trained individual and often help organizations get a lot from this storage metric.

IOPS

IOPS refer to Input/Output Operations Per Second, which is another common yet important metric that highlights the amount of reading and writes operations the storage can handle. IOPS play an important role in storage management capacity, particularly because of virtualization in desktop projects. It is important to take these metrics into account and understand them to ensure mission-critical business applications as well as for formulating a strategic data management plan.

Establishing a Strategic Data Management Plan

A data management plan comprises of the basic concepts, procedures, and different elements involved in data management to make sure it works. The goal is to ensure you have an effective, organized, and well-thought-out system to manage your data throughout the process. Indeed, data storage plays an important role. Once those metrics are used for data measurement, the plans would address those measurements to sought data after the research is complete. Moreover, it is also important to understand that the primary focus of any data management plan is to preserve and share data and not the management plan. Here's a step by step plan on how you can succeed with this.

Data Identification

The first and easiest step of a data management plan is to craft a data inventory. This should include:

• Data description and its type such as experimental, observational, survey responses, temporal, etc. 
• The resources from where the data is generated or collected such as models, camera traps, word counts, image analysis, sensors, interviews, etc.
• Data storage and file formats used; for example, databases, digital video, algorithms, sound files, digital images, spreadsheets, computer code, etc. 
• The volume of data
• The software incorporated in the process

An employee with NetApp training can easily put down a complete inventory and use the summary to get started with step one.

Data Organizing

An organization may use different methods to organize data. Depending on the type of data you are dealing with, some may require planning and commitment to implement. While quality control and data organization methods are very important, it should not be treated as a significant portion of the fundamental data management plan.

Data Documentation

The only way data is considered useful is if it has context, worth, or meaning. Metadata is the best way to give data its usefulness. Using supplementary files, such as data dictionary, is also a way to achieve that goal. Limiting or eliminating uncertainty is the ultimate target of data documentation.

Data Storage

And we are back to storage management. Where the data is kept, and the security measures taken to keep it protected are two very important concerns of data management plan. In any data plan, data sharing and preservation should take precedence. Certifications such as ONTAP Storage concepts and NetApp Data Protection Administration help teams come to grips with data storage and management setbacks.

Regardless of the volume of data, you will be dealing with, you should be very careful with your storage system and ensure that it meets the requirements of the research project. While hard drives, USB drives, and disks are easy and affordable solutions, they may not be the most secure options. It is easy to destroy or lose such storage mediums. There should always be multiple and secure ways to backup and store research data.

In addition to offering the right training for better decision making, it is also important for effective data management to begin when research is in its design stage. Always consider the storage management metrics to meet its requirements and think critically about preserving and sharing research data to determine if there's a need to prohibit or limit certain procedures, or if any further steps need to be taken to make the procedure smoother.