Most businesses are drowning in data they never actually use.
They’ve invested in collection infrastructure, built data warehouses, and connected dozens of tools, but when it comes time to do something meaningful with all that informatio (personalize a campaign, make a strategic decision, understand why customers are churning) the data is either inaccessible, unreliable, or stuck in a system that only three engineers know how to query.
That’s because collecting data is easy. Activating it is hard.
This guide covers what data activation means, the obstacles that prevent most organizations from doing it well, and how to build an activation strategy that turns your data from a storage cost into a competitive advantage.
What is data activation?
Data activation is the process of taking raw data, transforming it into a clean and aggregated format, and then using that data to make informed decisions. Examples of activating data can range from creating personalized audience segments based on demographic or behavioral data (for better ad targeting) to leveraging predictive analytics to understand the likelihood of an event occurring (based on historical data).
Activating data means – in part – that it’s accessible in relevant business applications like an email marketing platform, CRM, or business intelligence tool. Often, businesses store and consolidate their data in a central repository, like a data warehouse or data lake. However, getting data out of these repositories can prove difficult. With a process like reverse ETL, organizations can send data from the warehouse to downstream tools for activation.
Why is data activation important?
Collecting data without activating it is like stocking a kitchen without ever cooking. You’ve got all the ingredients, but nobody’s eating.
Here’s why data activation is important:
Your data is useless until it’s accessible
Most customer data ends up trapped in warehouses only analysts can query, in tools that don’t talk to each other, or in formats that require engineering work to translate. Marketing can’t use data locked in the data warehouse. Sales can’t see insights buried in support tickets.
Everyone has data, but nobody has the data they need, when they need it.
Data activation solves the accessibility problem by pushing data to the tools teams use. Your marketers shouldn’t need to file a ticket with engineering to build a campaign audience. Your support agents shouldn’t need to dig through three systems to understand a customer’s history.
Personalization requires real-time context
Customers expect personalized experiences. But personalization isn’t possible if your customer data is fragmented, outdated, or inaccessible.
You can’t recommend relevant products if you don’t know what someone browsed yesterday. You can’t suppress ads for products someone already bought if that purchase data hasn’t synced to your ad platform.
Data activation is what powers personalization at scale. It gets the right context to the right systems so you can treat customers like individuals rather than anonymous traffic.
Data has a shelf life
Customer data decays. People move, change jobs, abandon email addresses, shift preferences. The longer data sits unused, the less accurate it becomes.
Activating data quickly (ideally in real time) maximizes its value before it goes stale.
You’re already paying for this data
Collection infrastructure isn’t free. Storage isn’t free. Engineering time spent building pipelines isn’t free.
If you’ve invested in data collection but aren’t activating that data effectively, you’re paying for ingredients you’re throwing away.
4 obstacles that prevent data activation
Data activation can quickly become a complex process: from dealing with fragmented tech stacks, to grappling with identity resolution at scale, to a lack of engineering resources and bandwidth. Below, we list the top four obstacles to look out for when putting together your data activation strategy.
1. Fragmented data infrastructure
Fragmentation occurs when data is scattered across different, siloed systems – like in spreadsheets, a storage system like a data lake or data warehouse, or in the software where it originated.
Because data isn’t centralized, it’s challenging to draw cross-functional connections and insights. For example, payment data would go to finance and accounting teams, and marketing teams couldn’t easily access that information to create a campaign that centers around high-value customers.
2. Inability to tie events to specific customers
Behavioral data is more valuable when you know which customer performs what actions. Say you’re sending a next-purchase recommendation to customers who bought luggage from your store in the last seven days. You might recommend that they buy a carry-on bag next.
But if you learn that one person on your email list called customer service yesterday to complain about a broken handle, you’d want to hold off on sending him that cross-selling message. Instead, you may with the customer success team, and send the person a discounted offer instead to keep their loyalty.
Without the ability to tie events to specific customers, you can’t run personalized experiences. You may even send irrelevant messages that irk customers – and in the process, waste your marketing resources.
3. Unreliable data
It’s risky to make decisions based on data when you can’t be sure of its accuracy.
Say internal dashboards indicate that an e-book is your most effective lead-generation tool. But you don’t realize that your data set contains duplicates – one person actually downloaded the e-book five times, but it was counted as five different customers. This can lead to prioritizing the wrong channels for acquisition, and misunderstanding overall business performance. Unreliable data can stem from a lack of proper naming conventions, no tracking plan, or a lack of real-time data updates (to name a few).
4. Over reliance on technical teams
When non-technical teams completely rely on data engineers or analysts to help build audiences, or understand campaign performance and customer engagement metrics, delays will inevitably occur between an idea, insight, and action.
Especially as businesses grow, if engineers are responsible for manually maintaining the data infrastructure, there will be little to no time for innovation (not to mention the high technical cost of managing ETL pipelines and maintaining data integrity).
Knowing what to build in-house or when to outsource or automate certain processes can be tricky. One way to help businesses decide is to identify the “undifferentiated heavy lifting” that’s necessary but doesn’t contribute to the company’s competitive differentiators. With open-source and API-first platforms, businesses can benefit from certain out-of-the-box features without being completely pigeon-holed into an uncustomizable solution.
For example, the FinTech business ClearScore turned to Twilio Segment when they started to think about global expansion. Leveraging Twilio Segment’s data collection abilities and seamless integrations allowed ClearScore to free up 25% of engineers’ time. Twilio Segment also helps democratize data across, so marketers or customer success agents can autonomously use real-time insights to drive decision-making.
The 3 layers of successful data activation
There are different layers to successful data activation – from ensuring data accuracy to creating nuanced audience segments. Here’s what to keep in mind to ensure you’re using data to its full potential.
Layer 1: Accurate data you can trust
Collecting and cleaning data, in this day and age, is no small task. It often takes a combination of real-time event pipelines, batch processing, building integrations across your tech stack, and ensuring proper data governance is in place. Ensuring data accuracy takes a multi-pronged approach, with key components being:
* Establishing a single tracking plan for your organization that details what data you’re collecting, why you’re collecting it, where you’re collecting it from, and where it will be stored.
* Create standardized naming conventions for the data you collect, and use automation to enforce those conventions at scale (e.g., automatically blocking data that doesn’t fit your predefined standards).
* Prioritizing zero- or first-party data, which is a direct result of customer interactions with your brand.
* Implementing privacy guardrails, such as removing or masking personally identifiable information, to comply with global regulations and protect customers.
Layer 2: Identity resolution
Identity resolution is the process of stitching together every interaction a person has with your brand – no matter the channel or device they’re using – into a unified view (e.g, a customer profile). Identity resolution can be complex: what if someone is browsing products on an e-commerce site via their laptop, then on their mobile device via the store’s app, and then creates an account to purchase the items they’d been viewing. How do you attribute all that behavior to the same person?
With identity resolution, you can link previously anonymous user behavior to a known profile after that person is identified in your database.
