The Data Foundation for AI and Machine Learning: 4 Initiatives to Get Started
There’s a lot of buzz in the market these days about artificial intelligence and machine learning, but the truth is that it wouldn’t be in the best interest of most organizations to consider such techniques given the state of their data foundation.
While some organizations use these AI techniques in isolated or limited ways today, there’s exponential promise in the future for those wise enough to lay the foundation now. It’s not a simple task, but that doesn’t mean it has to be difficult. And if you do it the right way, you’ll be picking up small wins along the way leading to a big bang down the road. You just need the tools, processes and people to make it happen.
Here are 4 initiatives that you can start today that will lead to AI and Machine Learning success in the future:
1. Data Collection Foundation – Ensure Access to a Robust Volume of Data
You can’t act on what you can’t see. Data is how organizations can “see” and understand customers in the market today. This is why it’s so critical that your data strategy is built on a foundation that enables the most robust possible data collection and enrichment. So many companies today skip straight to data unification without even considering how robust their data collection foundation is…not only what data is available, but also the process of adding/removing/changing data collection over time because that will directly correlate with actually producing a comprehensive data set. If you unify incomplete data, you’ll just get the same misleading and uneven view of your customer that you’ve always had.
Here are some questions to ask yourself to determine if you data collection foundation is up to snuff:
- Can anyone in the organization setup tracking? Or only those with certain technical skills? If there are hurdles for setting up tracking, these will manifest as gaps in your data foundation.
- Can you collect data server-side (API gateways, cloud delivery, etc) and client-side (pixels, tracking tags)? Some data can only be attained in one way or the other, so you better have both.
- Is your data collection vendor neutral? Or is it locked in to a particular product suite? One company will never be able to get all data, so your data strategy needs to account for a multitude of sources.
- Is your data collection real time? Or as close to real time as possible? Your data should move at a speed equal or greater than the speed of your business processes. Ensuring real-time access to data ensures you can act in the moment a customer needs you.
- Does your data collection tool allow you to transform and standardize data as it’s being collected? If not it will lead to inaccuracies and delays later on that can render the value of the data useless.
2. Data Unification – Get Your Customer Data Clean and Correlated in One Place
To truly understand customers and deliver a relevant, timely experience, you need a comprehensive view of every individual customer that includes all sources of data your organization has. This is difficult because the number of technologies organizations are using continues to climb. Often, the data collected by various systems overlaps or doesn’t speak the same “language,” making integration difficult and time consuming. This is why it’s so important to standardize and clean your data up front at collection, so that it eases correlation and activation.
Ideally this view of your customer should extend all the way from the first touchpoint, even if you didn’t know the identity of the customer at that time. This view also needs to extend across channels and devices as that data is critical to knowing when and where to deliver the right message.
This single view of the customer will become the basis for triggering actions that deliver a unified and relevant customer experience.
3. Connect That Data to Systems of Record in Your MarTech Stack
Insights without action have no value. You can’t do anything to retain a customer now, if it takes you 3 months to generate the insight that the customer is at risk of churn.
Just like your data collection foundation should be vendor neutral, your data activation should also be vendor neutral to allow data to flow to whatever system or channel needs it, whenever it’s needed. This means that the system that holds your single view of the customer should be agile enough to integrate across as many channels as possible…and as easily as possible, so turnkey integrations are critical. Connecting systems point to point isn’t good enough since it’s inevitably incomplete, and eventually turns into an unstable mess of unsustainable integrations.
4. Map the Flow of Data and Actions Across the Customer Journey and Organization – Create a Data Supply Chain
Just like the customer journey is fragmented across devices and channels, your organization is fragmented across technologies and employee specialties. This fragmentation is debilitating for organizations attempting to understand consumers and provide tailored, unified messaging. Despite how long this promise has been around, it is truly attainable with the volume and breadth of connected data available.
Visualizing the flow of your customer and that data across their journey and your organization allows you to identify where internal or technological silos may exist that handicap customer experience. By knowing how data and customers flow, you can identify areas where cross-channel experiences can be improved. For example, opportunities to improve customer service experience based on marketing data, or marketing opportunities based on customer product usage. Make sure you have a cross-functional group to create this flow and brainstorm opportunities for improvement.
And Don’t Forget Privacy and Security
Last, but not least…a quick reminder that privacy and data security are important. The tools you put in place today can either set you up for a compliance disaster down the line or they can give you the tools you need to implement strong data governance practices. Make sure the solutions you pick will allow you to shift not only with changing consumer behaviors and advancing technology, but also evolving regulatory landscapes in the US and around the world.
If you are ready to start considering how you can get your data foundation in order, request a demo today and we can share with you the approach we’ve taken with companies like Toys “R” Us, Hewlett-Packard Enterprise and Cathay Pacific Airlines.