My wife and I have data management conversations with our teens all the time. Of course, not in data terms because that would be too over-the-top geeky, but it usually has to do with the location of homework, car keys, or the missing outfit that must be worn today. The parental response is generally along the lines of, “If you just put your stuff where it belongs and hang your car keys on the hook in the kitchen, they will be there when you need them.” The same principle applies to data. Particularly in the media industry, there is a massive amount of data that is continuously generated and updated. That data is very similar to my kids’ rooms – it needs organization so that when you need something, it can be found and used quickly.
Utilizing an MDM Process
Running a streamlined master data management (MDM) process is a challenge for media sales organizations. With multiple parallel back-end systems (such as various traffic systems and vendors), new business models, numerous sales teams and selling regions, different standards, and the constant churn of mergers, acquisitions, and divestitures, there are many facets that come into play. Add in the current challenges of today’s world, channel synchronization and early visibility are critical.
Not having a master data strategy, particularly around sellers, advertisers, and agencies, can lead to inaccurate reporting and forecasting, a lack of clarity around sales team customer ownership, and challenges in making your information actionable. It also can lead to a reliance on spreadsheets – which are useful, but many times also require assigning a single person to compile data, fix it, and then hopefully, get around to analyzing it.
MDM and Matrix
Before we dig in further, we should define MDM in the context of Matrix. Our MDM definition consists of both the tools and the processes used to establish master records for certain kinds of data – such as advertisers and agencies – to provide accurate, consistent, and unified data for use by sales teams and management. It involves both tools and processes because to do it well requires more than a bit of code that matches names together. Some of the most challenging data management issues we all deal with involve nuances such as differentiating between a national account and the account of a local franchisee advertiser, or classification issues such as knowing which database provides master information. We also use as much intelligent automation as possible to learn from previous matches, but data management requires constant maintenance to keep everything clean and consistent over time, or things get even harder to clean up.
Our teams at Matrix spend a lot of time thinking about and developing requirements and code that enable us to better clean-up data. This allows our solutions to natively provide a level of data management functionality where most of the processing happens behind the scenes, as automatically as possible. This process is able to be distilled into a simple statement that represents how Matrix handles the complexity of data management: “Monarch aggregates, normalizes, and cleanses data from disparate systems in the workflow to provide users with one unified view, eliminating the need to access data in multiple systems and reducing errors, manual entry, and data redundancy.”
The Matcher Service
Our primary process in handling the complexity of data management, Matcher, is a proprietary MDM tool that was recently redesigned from scratch, not only to continue our current matching capabilities but also to give us the flexibility to grow in multiple directions. For starters, it is entirely cloud-based. This frees us from previous server-related restrictions and provides us with opportunities to leverage Matcher in more places. The modern architecture also provides a REST API, which allows for future opportunities for our customers to share merged records with other solutions.
Functionally, the Matcher service uses intelligent, proprietary processes to automatically identify and merge duplicate records as they are received by (or created in) the system in order to avoid discrepancies that lead to incorrect data analysis. The continual process looks at the actual text, common articles (the, a), suffixes (Inc, LLC), and additional business rules to correctly merge records. Typical examples would be combining The Home Depot and Home Depot, and detecting and consolidating variations such as McDonalds, Mcdonald’s and MacDonalds into McDonald’s. These normalized records are then used across the system so that everyone is looking at the same information, in the same way. The system also allows for the unmerging of items for business reasons at any time. For the few items that Matcher finds inconclusive, Matrix virtual sales assistants then review and can manually unify those records.
Matcher and Beyond
The Matcher service is an excellent update to Monarch’s sales workflow, but our data management doesn’t end there. With our ad-sales first focus, we also incorporate soft normalization into our solutions. This allows, for example, the ability to group advertisers, easily letting you view them in any format needed (reviewing North vs. South for analyzing weather trends). It also allows you to dig into “All Subway” which is a group that combines national, regional, and local franchisee business for a complete view without impacting invoicing.
Put those pieces together – Matcher, Virtual Sales Assistants, and soft normalization – and you get a flexible sales tool with accurate forecasting and reporting that gives you a dynamic and holistic view into your business. We are also building toward the future as our teams are actively reviewing utilizing matches for a variety of workflow integrations, as well as for routing and distributing of RFPs and other types of messages in the Matrix Sales Gateway.
We would love to hear some of your challenges with matching and data management. Please send us a line!