Data Quality 101: Are You Using Effective Master Data Management?
Nowadays there is an abundance of data available to businesses that can be useful for a wide variety of purposes. However, in order to be of any real use, it's essential that the data is of good quality, and this is something that many organisations are struggling with. In this article we'll explore why having access to quality data is so important, and how you can ensure that your strategy is set up to take this into consideration.
The Importance of Data Quality
Having data that is of a high quality means that it is valid and you're able to use it for the intended purpose, e.g. informing your business decisions. There are a number of factors to take into consideration when assessing data quality; how complete it is, if there are duplicates, is it consistent and uniform, and are there any errors.
Ensuring that your data is high quality is incredibly important for a number of reasons. Bad data can easily lead you to generate false insights and therefore make the wrong choices for any operation in your business. For example, if you're using sales data to forecast future performance it's vital that the data is accurate in order to give you a realistic prediction, or if you were looking up a Singapore business listing you'd want to ensure that the company contact information was all error free and up to date.
It is also very costly and time-consuming to fix, so it's far better to take preventative measures to make sure you're always working with the best data possible, whether that's your own data generated from within your business or that taken from a third party source.
Master Data Management
With businesses drawing on huge amounts of data from a wide number of sources, and much of it structured in different ways, it is an even bigger task to ensure data is stored and managed effectively. This is why ensuring that your business' master data is uniform and of a high quality.
This includes things like customer information or product data. Managing master data effectively will involve ensuring that it is consistent across all systems, and will ultimately mean that you are able to offer a better customer experience and carry out your activities in a more efficient way.
Ensuring Data Quality
When it comes to the process of making sure that the data you’re using is of a sufficient quality, there are several factors and processes to take into consideration. Ultimately, it will be a long-term activity that requires effort throughout all aspects of your business.
Whose Responsibility is Improving Data Quality?
In order to implement an effective data quality and master data management strategy, you should aim to roll out a culture of consistency and thoroughness across your entire organisation. This means taking stock of your current systems and processes and ensuring that they're up to date and efficient, and the technology you're using is supporting your team effectively.
One of the biggest factors in any successful data strategy, however, is ensuring you have the right people with the right skills to take ownership of any problems that arise. Although it's important that every member of your team has an understanding of the best practices involved with ensuring data quality, assigning specific roles for the process will make it easier to keep track of who is doing what and make everyone aware who is responsible for each part.
The roles might include:
- Data owner - Shapes the data strategy, sets out goals and requirements and hands out access rights to other members of the team.
- Data manager - Carries out the needs as set out by the data owner and ensures that the technology in use is efficient and secure. The person behind this role will usually be someone from the IT department.
- Data steward - Works out the logistics of carrying out the data strategy, and has the responsibility of ensuring a good data standard across operations, e.g. spotting errors or stale information.
For each of these roles it's important to establish specific tasks to be carried out, and to make it clear how these are in line with business goals. It's also useful to keep in mind that technology can still only do so much when it comes to data quality; ultimately you need human eyes to evaluate and verify your data.
The Data Quality Process
When it's time to implement your data quality strategy, there is a three-step process you should implement. This involves analysing, cleansing and tracking. However, the very first stage is the establishing of your data quality aims in relation to your business' requirements. You're then able to carry out the next three stages:
- Analysing - Once you have a clear idea of your goals, you'll know which data needs to be analysed and the standards and rules each dataset must adhere to.
- Cleansing - This can either be done manually or automatically through software with specific rules defined to allow the technology to spot errors. Another option is to use a B2B database; for example if you need to cleanse your Germany customer data, you could employ the use of a German business directory list to help look for out of data or incomplete information.
- Tracking - In order to make sure that the level of data quality needed is continuously reached, it's vital that there is consistent verifications and monitoring of the data, for example through the use of software developed for this purpose.
With such large amounts of data being generated and used by businesses everyday, and much of it complex and varied in nature, it is essential to adopt a culture of data quality across your organisation. This way you can take preventative measures to keep things clean and current, and avoid potentially costly mistakes caused by false insights. You should also keep in mind that the process is a continuous one; ensuring data quality is not a one-time task but a consistent one that requires everyone to be fully aware of the role they play in it.06 Feb 2019
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