In my view, the efforts detailed in the prior articles will have a material impact on the business in three ways: (i) The application of scale economies to the management of data; (ii) the attribution of control mechanisms (such as thesauri) and (iii) a greater ability to merge and mingle metadata to improve revenues. Below, I suggest how some of these benefits might be actualized:
Scale:Those are some of the general benefits of better corporate data management and developing a corporate data strategy. The effort required to implement a data strategy program isn’t inconsequential and planning should be rational and realistic; however, as data management across a business becomes more and more ‘strategic’ the faster you adopt an approach, the faster your business will benefit. If you believe the tasks involved are difficult (or near impossible) now, they are only likely to get more so; therefore, it would be best to get started now.
Centralizing metadata management allows the organization to take advantage of scale economies in factor costs, technology and expertise. Not every business unit can afford to acquire state of the art technology or the market’s best metadata expert but these types of decisions are almost encouraged if their benefits can be spread across the enterprise. The financial benefits of better data management can also be most appreciated and captured at the corporate level, thereby providing greater financial justification for the adoption of technology and staffing to support the data strategy.
Collective Dictionaries “You say tomato, I say tomato” - Thesauri, ontologies and the attribution of consistent cataloging rules:
Business units don’t speak to one another nearly enough and this is absolutely the case in the way they manage information about the products they sell. The manner and method one business unit may use to describe a product could be vastly different than that which a sister unit may apply to a similar or likely compatible product. Take, for example, a large legal publisher publishing journals and educational materials: It would make logical and strategic sense that the metadata used to describe these complimentary products would be produced using the same metadata language and dictionary, yet that is rarely the case. (Think of this as a ‘chart of accounts’ for data).
Additionally, the manner and method by which authors and contributors are required to compile (write) their authored materials are unlikely to take account of the potential for compatibility and consistency across content type. As is readily apparent, traditional content silos are breaking down as users are accorded more power in finding specific content and an organization will be significantly hampered if it cannot provide relevant material to customers irrespective of its format.
Inter-relationships and cross selling:
Companies frequently leave it up to channel partners to aggregate compatible or complimentary products. Naturally, at the retail level this activity happens across publishers; however, to not provide the supply chain with an integrated metadata file that represents the best complete presentation of all the company’s products suggests a contradiction with the wider corporate business strategy of acquiring or developing products that ‘fit’ with corporate objectives. In other words, why doesn’t the company’s data strategy support the corporate business strategy in managing a collection of related and complimentary products and services? To do this, data strategy should be a component of business strategy planning.
The opportunity inherent in managing data in this manner will be a real ability to sell more products and services to customers. Providing relevant “packages” of content that add related and complimentary products to the item originally sought will generate cross- and up-sell opportunities. Why rely on a hit-or-miss approach provided by your channel partners? (Or worse, an association with a competing product). This activity is only possible with good data yet; if done effectively, can become a significant competitive advantage with incremental sales. Return on investment would be seen in metrics such as average revenue per customer and/or average shopping cart revenues. Importantly, selling more products to a customer who is already interested in buying from you is always easier and more profitable than finding new customers.
Market, promotions and branding:
Combining a company’s products in a logical manner reinforces branding and messaging. If product information is disaggregated and disorganized, it is likely that the branding and messaging in the mind of consumers similarly lacks effectiveness.
Channel Partner Relationships:
A company may be able to exert more leverage with a channel partner if it is in a position to represent all of its products in a coordinated and managed manner than if this interaction is dispersed across the organization
Matching and marrying data with partners will be less problematic and more effective if data models can be allied – time to market will be significantly reduced and planned benefits of the relationships should accrue in shorter time frames.
Additionally, providing a well-managed metadata file that supplies the type of product descriptive cohesion described above is going to benefit your channel partners as well, not only making their lives easier but also make them money by selling more of your products.
Acquisitions – integration of new data:
Historically, the integration of companies and new products with respect to product metadata might have been a haphazard affair. At a consolidated level this task becomes much easier with the added benefit that connections between products, adoption of standard dictionaries and standards may have an immediate financial impact. As noted before, it is likely the justification for the acquisition of the company in the first place was its compatibility or consistency with a strategy and it is logical that this be reflected in the manner in which the products are managed.
It is likely we will see that companies with ‘best in class’ approaches to data asset management will be valued more than those without. Increasingly, companies will be asked about their data policies and management practices and those which ‘under-manage’ their data will be seen as less attractive – for acquisitions, partnerships and other relationships.