Going Beyond BI: Activating Analytics

Data volume has increased exponentially over the last few years as more and more businesses have moved their operations online, unlocking huge potential for revenue gain, efficiency improvements, and overall risk reduction. 

Delivering on this new potential means adopting new capabilities and encouraging enterprise-wide collaboration to drive outcomes that move the needle. Data warehouses, lakes, and lakehouses have come a long way in powering enterprise-level analytics workloads and offering cloud-scale economics, but even these platforms have one fundamental flaw: they’re not accessible to non-technical business users. 

Your business stakeholders are the ones responsible for forecasting business performance and turning that understanding into actionable insights. As such, the big opportunity for every organization is enabling data activation across the business.

Reporting Isn’t the End State for Analytics

When you think about Business Intelligence (BI), the first thing that comes to mind is reporting, and answering questions like:

• What was the most ordered product last month?
• Which workspaces are most active? 
• Which customers are at risk of churning? 
• What marketing campaign led to the greatest number of conversions?
• Where are users getting stuck in the onboarding flow?

The answers to each of these questions can help you infer what is happening in your organization, but none of this data is actionable by your business teams. What does “actionable” data actually look like? Actionable data means putting data into the everyday tools they use to power operations so they can drive outcomes. It means skipping the report and going directly to execution based on conditions and logic set by the responsible business user.

While the initial promise of BI is to better understand data and even build accurate forecasts, the ultimate goal should always be to drive outcomes. Without this end goal in mind, it can be incredibly frustrating to find that valuable insights are rotting away, unused, in a report somewhere. It’s time that data and workflow systems come together to ensure timely action is taken on data-driven analysis. 

Driving ROI with Analytics

Many companies understand the inherent value of data, but it’s very difficult to measure the impact of that data organizationally. In other words, it’s much easier to quantify a sales team's impact than a data team's. 

With sales and marketing, value is intrinsically tied to the dollars that those teams bring in. You can expect to pay X amount of dollars for Y amount of return. For marketing, this manifests in the form of “conversions,” and for sales, this manifests in the form of “closed opportunities.”

Drawing a similar correlation to your data team is not so straightforward because they’re providing a foundation of data infrastructure that should power the entire organization. Data teams are focused on solving challenges like building data science models and performing identity resolution to build out a complete customer 360 framework.

If you’re going to invest in BI, you need to be able to quantify your ROI. In practice, this means putting your data to work for actual use cases. Here are a few examples of what this might look like in the context of marketing: 

• Uploading a live collaborative list of high-value customers to Facebook to target lookalike audiences.
• Triggering an email, SMS, or push notification based on a customer interaction with a personalized offer suggested by a machine learning model. 
• Refreshing your Facebook product catalog to ensure you’re not accidentally advertising products that are currently out of stock or forecasted to go out of stock.
• Uploading a Sigma input table driven suppression list to Google to ensure you’re not accidentally targeting users who purchased recently.

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With advertising, you can measure metrics like customer acquisition cost, return on ad spend, audience reach, etc. With email, SMS, and push notifications, you can measure click-through rates, engagement, and sales. In all cases, data activation translates the work of BI into measurable business impacts.

The Rift Between Analytics and Outcomes

Organizationally, “data” and “business” teams often work in separate silos. This makes it hard for data teams to translate their work into practical outcomes. In many organizations, data teams only interact with business teams when those teams have a request to support their specific objectives. Data teams will get requests like:

• Sales: “We want to build out a new sequence to upsell our existing accounts. Can you enrich our contact records in Salesforce with product usage data from our app?”
• Marketing: “We want to run a new retargeting campaign on Google. Can you give us a list of customers who viewed product X and abandoned their shopping carts in the last three days?”
• Support: “We’re trying to proactively identify and reduce customer churn. Are you able to create a churn score we can use in Zendesk to prioritize tickets for specific accounts?”

Self-serve platforms like Sigma have come a long way in enabling these teams by offering a spreadsheet-like user experience so they can power decisions across the organization, and now you can extend that decision making power to deliver on those actions.

Any time your business teams want to bring data into operational tools like Google Ads or Salesforce, your data teams need to manually pull CSVs or write custom scripts to integrate with third-party APIs. Both options are simply “duct tape” solutions that aren’t scalable or maintainable in the long run. 

Traditional Customer Data Platforms (CDPs) arose as the workaround to this problem, introducing a managed platform teams could use to store and activate data. These platforms collect data and sync it to downstream tools. The problem is these platforms weren’t designed to integrate with existing infrastructure like the data warehouse or BI tools. Ultimately, they just ended up creating a data silo and a fragmented and incomplete source of truth. 

Activating Analytics with The Composable CDP

The best way to turn analytics into action is to power your business tools directly. This is the core reason so many companies are adopting a Composable CDP. Whereas a traditional CDP operates as a separate entity, a Composable CDP acts as an activation layer that sits directly on top of your data warehouse and your analytics tools and data infrastructure (e.g., Sigma). 

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Data Activation, or Reverse ETL, is then used to sync data from your existing assets to your downstream tools. This gives you greater flexibility and control because you can access any and all of your data and maintain full control over it rather than handing over control to a black box that collects and stores its own data.

The Future of Analytics and Data Activation

Sigma introduced self-serve data for business teams, and Hightouch introduced self-serve for activation. The combination of these two platforms creates a seamless flow from BI to activation. Hightouch integrates directly with your data warehouse or your existing Sigma workbooks to push data to 200+ destinations. With Sigma and Hightouch, your business teams can go from insights to activation in minutes. If you’d like to learn more about the Composable CDP and how you can activate your existing Sigma workbooks, book your free trial with Sigma and Hightouch today.