|Data Management Practices|
If you ask what the most precious thing for an enterprise is, everyone will provide the same answer, Data. Why data is so important now because we can generate so much of it through various means and we are using it to make critical decisions that will make or break an enterprise or its efforts to become successful.
Since data is this much critical, having an accurate version in the format we want is the most important action that would decide the quality of decisions an enterprise makes.
In the remainder of the article, let's see a few best practices for Data Management.
In the remainder of the article, let's see a few best practices for Data Management, starting with what data management?
Data Management spawns right from the planning of what needs to be collected as data to retiring of the collected and used data. Every organization should be very clear on what they need as data so that it will help them in what decision making and other usages. This clarity is very important so that they can devise an implementation strategy to collect data, develop those tools for data collection and make sure it is cleaned, transformed and ready to use state.
There should also be a data retiring plan which is part of the data management. The retiring plan is when an organization has used that data and that data is not relevant anymore, then what should they do with that data? With the concept of what is data management out of the way, let's see a few effective data management practices in the remainder of this article.
With the concept of what is data management out of the way, let's see a few effective data management practices in the remainder of this article.
The Best Practices:
As explained in the concept section, data management starts with planning.
Planning means, what to collect, from where to collect, what processing needs to be done after collection, how to use the transformed data and when to retire that data?
All these questions can be answered only if an organization has clear goals on where they want to go? the purpose, that purpose will drive the efforts on their decision making which in turn will drive their data management practices.
By doing this will limit the data collected which means an organization will collect only data that is needed to them. This will also greatly improve data quality, which is the next best practice we are going to see.
Need to have quality data to make accurate decisions, right?
The first step is to collect only what is required. The second step is to make your team members aware that data quality is a very important process that they need to adhere to.
They should be well trained on those steps that are used to clean data in your organization.
Most of the time data come into an organization in an automated way, hence, it is very important for the team to go through them, find imperfections and correct the same before sending that data upstream to analyze and generate reports for example.
The team should also be aware of figuring out stale data, data that has lost its value and of no use to the organization and remove them periodically.
Few other important steps are to keep checking for data duplication, missing data, wrong data, wrong data format, misleading data types, etc.,
Along with these, data security, which is storing data in a secure way and make that data readily accessible by the teams that need these data should also be a key priority in your data management process.