Any conversation of data management implementation automatically includes a discussion regarding data governance, as both go hand in hand with one another. Successful major data management implementations require an in-depth understanding of data ownership and security, in addition to determining the business rules, you’ll apply to data science. The business rules include SOPs for matching as well as consolidating data and quality checks.
In a data management solution for any organization, there are different owners for every type and source of data, different rules that require implementation for enhancing data, different people for correcting data. In addition to all of that, there is a need for managing communication and changes. Through NetApp training and certification, it is possible for your team to get ahead of any issues the business might face in the future.
All the aforementioned items have to be decided during the implementation process – they also need to be maintained, monitored and adjusted once the data solution is in effect. The process of managing policies and data surrounding the data solution is known as data governance. Thus, a successful data management implementation needs NetApp training for a well-defined data governance strategy.
Data Ownership
A critical aspect of data management and data governance is properly defining the data and who owns it. This can be a challenging task to undertake, most organizations think that the IT department should take ownership of the data since the IT departments manage the systems where all the data is stored. However, in most cases, IT is rarely the actual owner of the data. When assigning the owner of the data it is important to consider who is the person that actually answer questions regarding the data, its definitions, its attributes, and its validity. Those who can answer these questions are the people who can be considered true owners of the data. An organization needs to involve these people from the beginning to define the rules for cleansing, corrections, matching, and consolidation of the data.
Data Stewardship
Data stewardship is another important step which involves giving a team or an individual the ability to correct any issues with the data from within, it is equivalent to giving someone the master access. This is normally carried out by routing the errors to secure directory where they are corrected and later merged with the master data. Various functions of this process fall under the data governance process where rules are defined and steward reviews and corrects the data.
It is the rules that have been defined which will trigger errors to its repository. It is critical that these rules are carefully created because if an incorrect rule is allowed to remain it will trigger large volumes of data to fail. First of all, such an event should not occur, however, if it does another rule will have to be added that overrules such data during the cleansing process. Obviously, there should be enough data in the archives for the steward to determine correct data from the incorrect ones, allowing them to potentially eliminate the data that is useless to the organization and it is removed from the systems altogether. As the levels of data quality required and the data rules are expected to change over time it is critical to revisit these rules again and again on a regular basis.
Data Security
The master data and the access to it are considered to be enterprise-level data, meaning most people in the organization won’t have any access to it or most parts of it. Data security is applicable to most areas with security rules similar to those discussed in data stewardship. Defining and managing these rules is key to data security, and communicating them to your team is an essential aspect of data management implementation and well thought out data governance.
In most organizations departments are responsible for the data they own as well as setting the rules that govern them. If someone outside the department wishes to view that data, that department will have to grant the necessary permissions to those that wish to view the data.
Survivorship Rules
Data entries an organization wishes to master will be made up of a variety of attributes. These attributes will clearly be represented in the golden record, the most accurate version of the type of data an organization is trying to gather, built from a wide variety of sources, the best data is taken and combined.
Survivorship rules define the attributes that win when the system makes a match and two records have to be merged into one. These rules are set by prioritizing a single source, in terms of how recent it is and how accurate. Determining these rules and communicating them is something the team governing the data will be responsible for.
Conclusion
Data Governance is key and plays an important role when it comes to data management implementation. Without which, the implementation won’t go smoothly making the process quite difficult for your team. A governing strategy for different companies may not work out the same for them both. Whatever the focus of a company they must at least focus on:
Define Data Elements – Figure out who truly owns the data, sets attributes and rules. As these are the groups of people who will need to work together to achieve efficiency. Different departments may own sources of data that makes up your entire data management implementation strategy.
Business Rules – These business rules are key and rather important as they will properly define how data is matched, consolidated and improved.
Communication – Without an effective communication strategy between different areas of your business, any initiative will fail. Since master data is used throughout the organization, the importance of communicating updates and statuses has never been higher.
Upskill Team – When it comes to storing, managing and using data a team that has had NetApp training will only increase the chances of this venture being a success.
Data governance strategy might change as time passes, as your organization and the data management solution matures. Focusing on the points mentioned above will ensure an effective data management implementation solution.