What is Operational Analytics?
Tags: database, analytics, operations, operationalanalytics
Published: 2021-08-25 02:15:38

## Operational Analytics

It's common to hear teams talk about the importance of "data-driven decision-making". Once a lofty aspiration, innovations in data warehouses and BI tools have made it simpler and cheaper than ever to actually make sense of data. But there's an unsolved challenge - insights gathered from analytics are only valuable once they're actually used to make a change in the business that moves the needle. This is sometimes referred to the "[last mile of analytics](https://www.forbes.com/sites/adrianbridgwater/2018/08/17/why-business-fails-to-travel-the-last-mile-of-analytics/?sh=604e5d116b8d)." 

Without that elusive last mile, analytics is at best a reactive report card for your business, and at worst, a waste of time. At Hightouch, we've worked with hundreds of companies who struggled with the last-mile of analytics problem: all of their important data lives in the warehouse, reporting is solid, but it's too hard to take action on that data. 

> [Operational analytics](https://hightouch.io/blog/what-is-operational-analytics/) is an approach to analytics that shifts the focus from simply **understanding** data to actually **putting that data to work** in the tools that run your business. Instead of just using dashboard data to make decisions, operational analytics is about turning insights into action - automatically. 

## The two uses for data

Every company generally uses data in 2 ways:

- **Operations**: using data to actually "do things". For example, triggering an email when a new customer signs up or makes a purchase.
- **Analytics**: using data to understand what's going on in the business. For example, building an executive dashboard with KPIs across sales, marketing, finance, etc.

Operational data is all about syncing data between systems to do things like communicate with users, bill customers, alert employees, etc. Analytics is often see as one of many "destinations" for the operational data pipeline. 

The persistent challenge with operational data is that it's not easy to get your various tools to "talk to" one another. For each pair of tools, you need to figure out how to get data to flow dependably and accurately between them. If you've ever gotten an email addressed to first_name, you've seen this notorious challenge rear its head. 

Some other examples of operational data workflows: 

- A B2B software company syncing product usage data to a CRM so a sales rep knows when to reach out to a customer.
- An ecommerce company syncing purchase data to an ad network so that recent purchasers don't get targeted for something they already bought.

Analytics, on the other hand, is about bringing all kinds of different data together and visualizing it in a way that paints a picture of what's going on in the business. 

The beauty of analytics data (which turns out to be the key that unlocks operational analytics) is that it's often the only realm where different datasets live together harmoniously - most often in a data warehouse. Analytics data is tied together neatly through models that form the foundation of the digestible, contextual charts that analytics tools provide.

Thanks to innovations in data warehouses and the surrounding ecosystem, bringing data together for analytics has never been easier or more cost-effective.

## The analytics layer is a hub, not a spoke
It just so happens that what was previously thought of as the "analytics layer" turns out to be the perfect foundation for operational data workflows, and an antidote to those challenges associated with getting systems to "talk to" one another.

As opposed to creating point-to-point connections between tools ("spokes"), companies are now beginning to use the warehouse as not just the foundation for their analytics, but as the "hub" for all operational data workflows. This is operational analytics. 

![image](https://dev-to-uploads.s3.amazonaws.com/uploads/articles/rpqd1lakvzbqr2hsawdo.png)
 

There are a few reasons why the "analytics layer" is the ideal hub for operational workflows:

- It's <u>simple to aggregate and integrate data</u> in data warehouses; it's what they're built for. Teams can easily bring customer data, billing data, employee data, and other datasets together into the fabled *single view of the customer,* a promise made by many SaaS vendors who haven't really delivered. Once the data's in the warehouse, the path of least resistance is to just send that data out where it needs to go.
- <u>Security</u>: companies **own** their warehouse, so data never has to leave your purview and fall into the hands of yet another vendor.
- This approach <u>breaks down silos</u> between data teams and business teams by creating a clear handoff: data teams own the raw data and model it into clean data, which empowers business teams to own the management and sync of that data to the tools they need to run the business.

This approach is dramatically changing how companies think about analytics. 

Analytics has always been about understanding your business and using that knowledge to make decisions. The problem comes when those decisions have to actually get carried out. All too often, good ideas come out of analytics, but fizzle into nothing when data actually needs to be put to work. 

Reporting alone is necessary, but not sufficient. It doesn't actually drive the actions that move the needle. Modern companies can no longer just make data-driven decisions. They need to act on those decisions with data and do so automatically. This is operational analytics.

## An example of Operational Analytics
Let's take an example of what it might look like in practice.

Imagine you work at a software company with a freemium model. Users can sign up for free and use the product up to a certain limit, at which point they then have to pay. You might use analytics in the form of a BI dashboard to track the number of signups, the percentage of users who convert to a paid account, and the effectiveness of sales reps in converting those customers to paid. You find that the sales reps who spend time personalizing the outreach to free users with information about their specific use case tend to over-perform.

Currently, these sales reps need to track down information across systems - Slack, Salesforce, and others - in order to get the full scoop before sending out a personalized, relevant email from Hubspot. This is where operational analytics can help.

With operational analytics, the same data that's feeding your BI dashboard can be automatically synced into Hubspot. For instance, Hubspot contact and account records can be enriched with with information like: whether or not the user has fully onboarded, the last login date, and the integrations that the user has set up. Now, sales reps don't need to track down information and spend time manually writing personalized emails. This data can be used to automate that outreach, leaving reps more time to help customers. 

![image](https://dev-to-uploads.s3.amazonaws.com/uploads/articles/xtnvpev6u4gtlm8757id.png)
 

This isn't a hypothetical example. It's exactly how [Retool used Hightouch](https://hightouch.io/customers/retool) for operational analytics. Once Retool began using analytics not just for reporting, but for action, they saw some pretty staggering results, including **a 32% increase in reply rate** on emails, as well as **500% increase in click rate and increased feature adoption.** 


### Want to get started?
If you're ready to get started with operational analytics, we're happy to help. Feel free to create a shared Slack channel with us here at this link: [https://api.hightouch.io/api/misc/shared_slack](https://api.hightouch.io/api/misc/shared_slack) or schedule a call with us here: [https://calendly.com/mwhittle5/meeting](https://calendly.com/mwhittle5/meeting). There's a lot to consider with operational analytics, and some teams might not be ready for Hightouch just yet. That's okay; we aren't pushy and will do our best to help.