April 8, 2021
April 8, 2021
Recently, I was contemplating the explosion in the term “ops” in the business world. You’ve got marketing ops, sales ops, data ops, IT ops, customer success ops, revenue ops, among many others. Collectively, this is becoming known as “BigOps”. I wanted to go further, though, and create an even higher level of ops called organizational ops, but then I realized I’d just accidentally spelled “oops.”
To be serious, though, it is actually quite daunting when you consider how many different workstreams cut across working groups and supporting systems in any business. Getting work done today has increasingly become a matter of corralling people as they execute business processes on multiple applications.
Why is this happening? It’s partly a good thing. The growth in Software-as-a-Service (SaaS) applications, along with mobile apps, has led to a situation where employees can get things done using best-of-breed technologies. They can do Customer Relationship Management (CRM) on Salesforce.com, create documents on Microsoft 365, store files on Box.com, manage sales orders on a cloud Enterprise Resource Planning (ERP) system, communicate on Zoom and so forth.
This “make it up as you go” approach to work has several downsides, however. It’s inefficient, for one thing. Toggling between apps is not ideal, especially when a team is trying to take care of business processes like quote-to-cash. It’s error prone, as are most processes that rely on several manual steps.
The scattered apps also each have their own data store, which results in wild disaggregation of customer data. The quote for Mr. John Smith in Arizona gets booked into the sales system as being from Mr. Jon Smyth in AZ and delivered to Ms. J. Smith in Ariz. Funny, right? Not really. This is a data quality nightmare that’s bad for operations and the brand.
The IT industry abhors an unsolved challenge, so some very innovative solutions have emerged to deal with this less than satisfactory operational situation. Some vendors offer platforms that enable non-technologists to automate processes and integrate disparate cloud-based systems. Without distracting IT people who have demanding tasks elsewhere in the business, a general businessperson could link CRM with ERP, Microsoft 365 and Box.com into a coherent, seamless workflow.
In another example, an automation platform could improve marketing efficiency. Users could upload a lead list, run it through a lead scoring application, route the lead to the right sales contacts and then send out personalized emails using a marketing automation tool. It could accomplish all of this in a single workflow that touches different systems. The workflow could even plug into a sentiment analysis solution that scores prospects based on subjective data like their social media comments about the brand.
Data quality can still be an issue, however, even with all this clever integration. For instance, if someone inputs data incorrectly at the start of the process, that error will be carried across all the systems it touches. For automated processes to deliver optimal results for the business, consistent, accurate and normalized data is required.
Data orchestration fills this gap. Itself a series of automated processes, data orchestration corrects errors and normalizes data. For example, the process would conform AZ and Ariz into a single, uniform data format for state names. A data orchestration solution can also enrich the data that’s flowing across the multiple systems, it might add four digits to each zip code, add missing job titles and so forth. Data orchestration may also become one of the systems integrated by the automation solution. It can route data to the correct system and user within the construct of a specific workflow. The data orchestration solution ensures that the right person sees the data at the right time.
The new generation of automation and integration solutions bring much-needed order and efficiency to business processes that are at risk of sprawling out of control. From chaos comes RevOps and other “ops” initiatives that are able to facilitate growth and improvements in process quality. Data is at the heart of it all. Without good data quality, the results will be sub-par and problematic. Data orchestration offers a way to maintain high data quality no matter how complex and interconnected the workflow may be. Finally, there can be the “BigOps” that so many business managers want, but without the “oops.”