Are You Struggling to Maintain Data Quality?
Did you know, 52% of organisations confess to relying on educated guesses to inform their data-based decisions?*
In today’s marketplace, where data plays a pivotal role in providing organisations with competitive advantage, it is surprising that so many still rely on an element of assumption to inform business strategy. The use of data can empower organisations in various ways, from the creation of personalised, omnichannel experiences to enabling efficient, complex product trials. As such, those organisations who do not capitalise on all data available may face repercussions business-wide. This could lead to loss of competitive advantage in the marketplace.
What makes your data poor quality?
There are four key elements of poor quality data:
- Missing values: when some profile data is available and some isn’t, these entries can’t be used to inform ongoing analytics. This may limit the accuracy of any insight generated. As such, your teams need to come up with a solution to ensure as much data as possible is available for analytics.
- Irrelevant timescales: if data is too old, it may not be a legitimate source of insight to inform current behaviour. For example, user behaviour from 10 years ago is not appropriate for a telecoms company to use to inform future projections.
- Disparate data stores: when data is stored across a number of locations, it will often lead to conflicting information for an individual, damaging ongoing business processes.
- Poor linking between the same profile: similar but slightly different profiles relating to one individual can cause confusion and mislead analytics as it may lead to one individual being identified as multiple customers.
It is likely that each of these issues plays at least a small role at your organisation. With the significance of each depending on the scale of the problem, it may seem like an impossible challenge to resolve. However, that is not the case: with a little work, your data users can build confidence in the validity of your business-critical decisions.
What are the implications of poor quality data?
As soon as assumptions and guesswork come into play within your data analytics, the significance and validity of findings and decisions fall, generating a lack of confidence in the conclusions it informs. That’s not to say your findings won’t generally be representative, however, given the investment you make in data management and data analytics, don’t you want to know that your conclusions are based on more than just guesswork? Wouldn’t you like to be completely confident in its accuracy and know that it has drawn on all relevant sources of data?
The implications of poor quality data can also present tangible challenges in other areas, such as:
- Lack of compliance with industry regulations
- Badly informed business decisions leading to loss of competitive advantage in the marketplace.
How can you overcome poor quality data?
Amadeus’ experts have many years’ experience supporting customers in data cleansing and taking relevant corrective action to ensure ongoing data quality. These developments include:
- Centralising existing data stores to reduce the likelihood of multiple customer profiles for one individual.
- Developing processes to align disparate data stores so that linked profiles reflect the information contained in the newest listing by overwriting existing information.
- Entity resolution: Amadeus can employ SAS® Data Management Studio to cleanse your data and combine multiple rows into one. For example, it will help to establish if T Smith is the same person as Tommy Smith, T Smyth and Tom Smith.
Overcoming missing values across your data
If you are struggling with existing data quality issues, this could significantly impact the validity of ongoing analytics. Missing values could be to blame. Learn how to handle missing values yourself thanks to our free download.
Benefitting from your high quality data
Once you have overcome your data quality issues, you can seek to access more value from your data through the use of statistical analysis, such as regression analytics, which is invaluable in real-world situations such as calculating credit risk. Amadeus hosts regular Regression for Predictive Analytics courses, taught by our in-house experts.
Amadeus offers Data Science expertise to support you in gaining maximum value from your data. In taking preventative and remedial steps, your teams and decision makers can have confidence that the data analytics is producing insights that are truly representative of the data stored within your environment.
Contact Amadeus today
Contact our team of SAS experts today to discuss your current situation and what benefits you can expect with the support of data quality expertise from Amadeus Software.
* Source: Experian, 2017. The 2017 global data management benchmark report.