What is Data Quality in unstructured information?

We are often asked, “What is an intelligent migration?” “What makes it intelligent?”

For us, it’s about taking control of the data first, and this includes cleansing of unstructured data to improve the quality of the data estate.

Before we get into it, let’s looks at Data Quality as a general term. What do you think when you hear the word quality?

• The data is useful
• The data is up to date
• The data is accurate
• The data is reliable
• The data is consistent

Data Quality is normally associated with structured data, that is, information which is in a highly organised, standardised, easy-to-analyse format such as a database.

So, for example, in this context, Data Quality means that the database is complete (i.e. both the customer/constituent’s first and second names are included), or that it is reliable (i.e. there are no unwanted characters in the values).

But Data Quality is just as important and is not commonly recognised in its relevance for unstructured information (that’s things like Word documents, emails, audio files, open-ended survey results.)

With unstructured data being so vast, so siloed, from so many sources, and difficult to analyse, it can therefore seem near impossible to ensure Data Quality, no?

Data Quality in unstructured data can have profound impacts, not least being able to make critical business decisions on data you know is rich with the most useful and accurate information.

So, how can you aid Data Quality as part of an intelligent migration?

• Remediation of Duplicates: Removing duplicated data means there is a single source of truth. A potential problem with migrating duplicated content is that new and important information may be added to only one document, leaving others incomplete and inconsistent.
• Remediation of old data: Removing redundant and obsolete data. This means decisions are only based on the most timely and accurate information, and not data which is now out of date.
• Classifications and Access-based controls: Applying automated Classifications mean that once information has been categorised, you can restrict system access to unauthorised users. This ensures that information is modified by trustworthy and reliable sources only.
• Automated Retention Policies: Applying automation to retention and disposition policies ensures data is deleted or archived when required. On top of the regulatory aspect, this means that information which is no longer relevant or correct is not accessed down the line.
• Ensuring Data Searchability: Once you have migrated both your data and people to the Cloud, you must ensure there is easy access to the information for those who need it. This is to avoid the chance of inaccurate information being created because the knowledge required is not easy to find – or similar/duplicated documents being generated as the originals cannot be located.

It’s estimated that a huge 80% of organisations’ data in the next few years will be unstructured.

Without understanding, analysing and managing this enormous amount of information, how can you truly ensure your teams are working with quality?

For more information on data quality as part of intelligent migration, join our webinar Rock or Sand?  What’s your SharePoint Foundation? with our partner LG Improve on 12^th January. Sign up below: