The enthusiasm for data-driven marketing technologies like predictive, AI, ABM, marketing automation, and personalization tools is driving interest in a new category of technologies – Data Orchestration Solutions. These solutions automate critical marketing processes including things like onboarding data, cleansing and enriching that data, unifying field values across different systems, and delivering to key partners.
Now that sounds bland and theoretical, and marketers tend to like things that are shiny. So why are marketers becoming excited about these? It’s because the success of so many of those shiny MarTech tools is being hindered by a bad process and bad data, and data orchestration solutions address those issues directly.
A few real-life examples:
- You run hundreds of campaigns every year, and the offshore team that you’re paying $50,000 a year to clean up and upload lead files is missing its SLAs, while both Sales and Marketing are complaining about inconsistent results. You’re tired of manually editing their work—that isn’t scaling.
- You’ve got 3,000 industry values, 250 state values, and over half your database is missing job level and job function fields, despite buying data from three different data providers. You want to have 10 industry values, 50 states, and job functions and job levels for all your leads so you easily segment your database and make lead scoring work. But, that’s easier said than done.
- Your sales team is tired of you sending leads with names like “Asdf” and “Bugs Bunny” over to them as MQLs when it’s obvious these aren’t real people. It’s damaging your team’s credibility.
- The cutting-edge AI experiments you’ve been working on haven’t delivered anything usable for the marketing and sales teams. You suspect the data you’re using to train the models aren’t good enough.
- You’ve found that 25% of your best MQLs aren’t getting followed up on because they’re going to wrong people. Your company’s leads should be routed based on named account, product interest, geography, industry vertical, and partner involvement. This looked easy enough when you saw it on a whiteboard and committed to implementing it, but in practice, it hasn’t been successful.
- Your campaign attribution reporting is in awful shape and you can’t financially justify doing an event that anecdotally was the sales team’s favorite. You see that part of the problem is that almost 20% of your leads and contacts are duplicates. You acknowledge that the sales team isn’t compensated for data hygiene, and you see little support in gaining their commitment to do better with this.
At first glance, all of these issues sound completely unrelated–they sound like data quality issues, sales training issues, Salesforce lead routing issues, Marketo lead scoring issues, and immature AI technology issues. The reality, though, is that this isn’t the case. The root cause of all these issues that are sabotaging your marketing efforts is the same–it’s poor marketing processes. Those bad processes affect the data that all of your critical marketing technologies depend on. It’s the proverbial “garbage in/garbage out” problem.
What Do Data Orchestration Solutions Actually Do?
Data Orchestration Platforms automate the critical processes that marketing teams and their MarTech stacks depend on. Data orchestration solutions work in real-time, behind the scenes, to ensure that data in a marketing system of record, such as Salesforce, Marketo, or a data warehouse, always conform to the standards that you’ve set up. Data Orchestration solutions do this without the mind-numbing, manual efforts that nobody wants to do day-in, day-out.
Key Capabilities of These Solutions
Most of the solutions in this space are SaaS-based and plug directly into sales automation and marketing automation solutions, as well as third-party data providers like ZoomInfo and Dun & Bradstreet.
The more mature solutions have pre-built recipes that marketers can easily deploy for tasks such as lead-to-account matching, lead deduplication, data cleansing, lead and account scoring, and lead routing. Some also include reference data sets and allow marketers to plug in and customize their own data sets to perform tasks like normalizing (standardizing) field values to a company’s specifications and filling in missing data values, such as a deriving value of a city and state field based on a postal code value.
Some data orchestration solutions let marketers create their own business processes from scratch, which allows companies to replicate the processes that many point solutions offer so that they can simplify their MarTech stack.
The next time you identify an application in your MarTech stack that isn’t delivering the value that you were promised, or you find your team struggling with a painful manual process that’s keeping you and your team from focusing on strategic work, it’d be a good opportunity to learn more about Data Orchestration solutions such as Openprise, and see how they can help.
Recommended Read: Interview with Ed King, Founder & CEO of Openprise