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If your organization is settling for marketing automation, there's no way you should go wrong with data management. In fact, it is the key to marketing automation, and an organization must be prepared to handle the massive amount of data after the shift. Without data management best practices, it isn't possible to make the most out of your marketing automation and other business operations. Ideal data management practices do not only help with the perfect implementation of reliable data storage but also with appropriate maintenance to keep up with the new technological features that come with it. This is what NetApp training can aid with.
NetApp training and certification makes data management effective and increases the pace of the research process. The trained team contributes and makes the process more efficient and result-oriented to meet the requirement of the organization by storing, managing, and sharing research data more effectively. NetApp training does not focus on using or creating any particular analysis tool or specific data collection. It helps the team understand data management at the core by discussing it at a general reveal.
The fact that we have a massive amount of data all around us, it is important that we pick only what is important. Therefore, it requires careful analysis to make sense to the business. Collecting the right type of data for business analysis or reporting is a major task on its own. This is particularly applicable to technology giants dealing with data mining, raw data, data management, and data extraction. For such people, understanding the best practices of data management and ensuring their team is well trained and skilled on the subject can make the task much easier.
Data management is not only crucial for the company's own processes but also to get a leap over competitors in the industry. Having the required know-how on the data management can save an organization physical labor and time, and deliver data analysis without compromising on the quality of data.
Data management practices enable perfect implementation for data and help with its maintenance. But that's not all. Using these practices, an organization can also boost its profits and experience a reduction in operating costs. Here are the proven data management best practices that could offer you effective results right away.
An organization that's after collecting large amounts of raw data may actually get nothing purposeful out of it. Unless the data fulfills the need of the project, its usefulness is zero. Collecting specific or project-related data is the best practice that could save money, labor and time as the very first advantages. Putting your hands-on information instead of raw data can be more beneficial. It also saves the storage space and time required to process gigabytes of useless data. As a result, it saves significant cost of IT infrastructure.
There's a thin line between 'essential' and 'desirable,' and that's what a team of trained employees can distinguish. This is a basic step, which if ignored can lead to long-term consequences. It is important to divide the collected data into two different categories:
Desirable: Data which you may want but is not necessary (can be an advantage but does not have any adverse effect)
Essential: The ultimate data you need for your analysis (cannot proceed without it)
Once the data is categorized, it is easier to prioritize and give preference to essential data, which should be obtained and processed first.
Regardless of the project, the reliability and quality measure the strength of the data, and its analysis is considered the backbone of the project. High-quality data is accurate and often collected from the reliable source. These factors weigh the reliability of the data collected. For any organization, it is more sensible to keep less yet high-quality data than to have tons of useless information. Always check data for reliability and source. If you fail to keep up with the quality standards of the data, the analysis based on that information will also be compromised.
Secondary data can be authentic too. In fact, that's where you can get more targeted data easier. Instead of asking the people to fill out fields for you - which in most cases isn't reliable - you can use information as e-mail to get more data. You don't even have to worry about storage for such data. It keeps your database clean and well-maintained.
Every data comes with an expiry date. This is also applicable to crucial data such as email addresses. The key is to refresh and make sure your organization maintains up-to-date data only. Let your team of experts work on the average tenure of the key data demographics. For instance, if your key demographic is marketers, you must know how frequently they change their job - 2.5-3 years on average - and change your data accordingly.
While it is important to know who your customers are, it is more important to know what they are doing. Thus, focusing on behavioral data is more reliable as compared to demographic data. For marketing automation, this data offers better and more promising results.
We can't stress more on the importance of data organization. This includes creating directories, folders, and file naming. There are many things you can take into consideration when it comes to data organization. But as far as the best practices are concerned, be consistent with your approach. For instance, always use the same information for the directories or folders. Also, retain the order of information to make data more accessible.
Data management covers the development and execution of projects, policies, processes, architecture, practices, and procedures. With the information mentioned above, you can keep crucial data more organized and use the information to generate more reliable analysis, which is used for decision making by the organizations. Additionally, providing certification training in NetApp-related areas such as Data ONTAP SAN Implementation, and NAS and Performance on Clustered Data ONTAP can assist tremendously with data management overall.
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