The Secrets of Successful Customer Data Orchestration
We love data. We’re crazy about it. We dream about it. It’s at the heart of everything we do, and we can tell you from experience that the key to world-class marketing campaigns is a strong data foundation. That said, many modern marketers are practically drowning in data. The solution is customer data orchestration, the relatively new process of automating data management — gathering it from multiple sources, combining it, and preparing it for use.
The Problem With Bad Data
Without a principled data orchestration framework, too many companies are storing data in disorganized, disjointed silos. To make matters worse, the problem often extends well beyond the marketing department, with different teams maintaining their own data, leaving the company vulnerable to conflicting, inaccurate, outdated and even compromised data. In fact, Experian found that 30% of company data is inaccurate, and MIT’s Sloan Management Review estimates that most companies lose 15% to 20% of their revenue because of bad data.
Those are some scary, scary figures, but thankfully these problems can be solved! According to MIT Sloan:
“Companies that have invested in fixing the sources of poor data — including AT&T, Royal Dutch Shell, Chevron, and Morningstar — have found great success. They lead us to conclude that the root causes of 80% or more of errors can be eliminated; that up to two-thirds of the measurable costs can be permanently eliminated; and that trust improves as the data does.”
Of course, it’s much better to proactively create better data-related workflows through data orchestration than respond to bad data after it’s already infested your company.
So What is Data Orchestration?
Customer data orchestration is the process of automating the flow of data through your organization — gathering it from multiple sources, cleansing it, combining it, and making it available for analysis and use by other tools.
Imagine your data pipelines as something like an actual, physical water treatment system. Water is collected from various points of origin and pumped through the system, where it enters a treatment facility. There, all the water is combined and treated. Then, it’s sent out from the treatment facility, pumped into buildings across the city. A data orchestration platform works in much the same way.
The Five Stages of Data Orchestration
1. Collecting and Preparing Raw Data
Data needs to be gathered from all of the places it lives. That includes points of origin out in the world (like forms filled out by customers, surveys, etc.), as well other data silos already belonging to your organization. For example, your sales team may use a customer relationship platform (CRM), your IT staff may use a separate website analytics platform, and your marketers may all use an email marketing service. All three may have separate datasets that are being maintained by separate teams, and they’re not being reconciled or used together. The data orchestration platform pulls the data from all of those locations, formats, labels and/or augments it, and combines it into one master dataset.
2. Transforming Data
Data from different sources needs to be standardized — and manual reconciliation of data is a time-consuming process that’s easily streamlined through a data orchestration framework. Consider something as simple as a name. Separate data sources might store the record for a customer’s name several different ways, including:
- Michael Scott
- Scott, Michael
- Michael G. Scott
- Michael Gary Scott
Imagine being tasked with manually reformatting thousands and thousands of customer names, then inputting that data into a new tool. Not exactly the best use of anyone’s time, is it?
With the right data orchestration software, a series of automated checks can transform all of your separate records into an identical format for consistent use across your company’s systems. You can also remove duplicate data and generate automated analytics reports.
3. Enrichment and Stitching
Data enrichment refers to enhancing your data points with additional context gleaned from other systems or data points. Stitching is combining different pieces of data for a particular purpose. If you were building a basic demographic profile on your customers, for example, it might be useful to combine their level of education with their occupation or yearly income, since these data points are interrelated.
4. Decision Making
Your data orchestration software can perform different functions based on predetermined rules, including ranking the quality of data or weighting the data by importance. In the most advanced, next-gen data orchestration setups, such as when using Simon Data’s platform, your customer data platform might automatically use this data to recommend or implement marketing initiatives.
Once your data is clean, enriched and standardized, your data orchestration platform can sync it to the othertools your team uses, as well as internal data warehouses or data lakes.
Reimagining Data Orchestration with a Customer Data Platform
So how do you actually implement customer data orchestration? Simple: with a customer data platform (CDP).
A CDP is software that unifies customer data across sources for use by other systems and teams. Next-gen CDPs like The Simon Platform have built-in cloud-based marketing tools, so they can directly leverage that data to create dynamic cross-channel marketing campaigns that forge strong relationships with your audience throughout their entire lifelong customer journey.
Why Industry Leaders Choose Simon Data for Customer Data Orchestration
With streamlined workflows and an intuitive user interface, not only does The Simon Platform deliver seamless data orchestration, it empowers marketers to use that data to craft sophisticated omnichannel trigger marketing campaigns that create cohesive customer experiences and transition first-time customers into lifelong brand advocates.