This post is not based on Gartner or any other research, but my own observations on projects I have been carrying out over the last few years.
- All organizations seem to suffer from bad quality of data as well as bad quality of metadata (the data about data or content).
- Most of the organizations are not even aware of the fact that great deal of business problems are due to information management problems.
- …and those who have, yet put their money in “real business challenges = IM technology” and overlook the data/metadata issues.
- Local agile solutions in business units cause severe deadlocks for the data integrations at the global level.
Structured data (schema based metadata) by schema.org has become guideline for on-page mark up, helping search engines to understand the information on webpages and provide richer results. Let’s explore some real life cases where bad (meta) data management caused business failures.
Business problems in real life – caused by poor metadata management
I have witnessed, or my colleagues have told me stories, such like:
- RTD is blind for business metadata.
A large manufacturer had to switch off their real time decision engine from the campaign page, since the RTD-box would never show any content regarding their brand new product. The new product had not yet gained any scores from users to which their RTD-engine could use to give recommendations about the new product. RTD-engines are typically overly self-containing in reacting to users’ behavior, but rigid in combining external business rules. As an example when marketing wants to change rules to boost visibility of the new product, regardless of user’s profile or history. The required change in RTD rules is a manual operation and can difficult or impossible.
- Bad metadata in web pages cripples SEO.
Metadata in HTML-content plays a crucial role in SEO. In most of the web publishing projects I have witnessed low quality of available metadata, which comes with the content items to the assembly and transformation engine. Often no metadata exists, or it is inconsistent and not correctly describing the content and/or the product the page is marketing. One team decided to switch off the metadata based index enrichment because all the metadata for intranet documents were the same, based on the document template and just causing more noise than relevancy.
- Inconsistent use of identifiers kills web analytics.
A web analytics team had to use text mining practices in order to get (guess) the big picture of which campaigns were ran globally, which products were currently marketed and which type of content was actually published. It was also impossible to conclude which content correlates to increased sales of products in web stores. There was no consistent use of unique identifiers and categories, instead each systems relied on ad-hoc hash tags and locally generated page metadata. All this inconsistency just caused unreliable analytic results after the laborious efforts.
- Anarchy in the local systems prevents merging of information.
A company built a centralized metadata hub to harmonize content categorization (tagging etc.). The plan was to merge data from different sources. The mapping exercise became next to impossible as source systems were using their own categories and ad-hoc logics. All kinds of workarounds were implemented in the eve of eCommerce. What was tragicomic was that the master data management system was in place, but obviously not accessible or failed in their internal marketing, since not that many systems complied their reference data.
- Web pages are not providing meaningful information for search engines
So that consumers could find your site and would pay attention on your web shop products, store locations etc. while browsing Google search results. Often store and product data is not even showing up in the search results.
Model for digital marketing 360 metadata
To cut corners, one can say – all business objects have a structure and a lifecycle. By combining those viewpoints, I have modeled a high-level structure for the metadata according to the phases of lifecycle.
Lifecycle of the metadata
If you consider using metadata for digital marketing, say using full potential of metadata over the content life for targeting, the lifecycle phases are:
- Creation of substance, when the author also has the best opportunity to create descriptive metadata.
- Managing and publishing the substance, when business can add the contextual metadata.
- Engagement of the substance, when users can contribute their feedback to be added on.
- Analytics: what has happened in similar situations, when behavioural metadata can be merged in?