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What is Operational Analytics & Why You Should Use It

Operational analytics (OA) focuses on improving business operations by putting your data to work in the tools that run your business. Learn how the Hightouch Operational Analytics platform streamlines your OA here.

Kevin Moyer.

Kevin Moyer

Luke Kline.

Luke Kline

July 26, 2021

15 minutes

What is Operational Analytics & Why You Should Use It.

What is Operational Analytics?

It’s common to hear teams talk about the importance of “data-driven decision-making.” Although this was once a lofty aspiration, innovations in the data stack like data warehouses, data lakes, and BI tools have made it simpler and cheaper than ever before to make sense of real-time data.

The rise of machine learning, artificial intelligence, and data mining have all increased the value of data. There’s an unsolved challenge though, insights gathered from this data are only valuable once they are used to make a change in the business that shifts the needle forward. This is sometimes referred to as the "last mile of analytics."

Without that elusive last mile, analytics is at best a reactive report card for your business, and at worst, a waste of time. Hundreds of companies struggle with this challenge: all of the data lives in the warehouse. It’s easy to do reporting, but it’s extremely difficult to take action on that data.

When all the data is contained in your data warehouse it’s only accessible to your technical users who know how to write SQL. Your end-users across sales, marketing, customer support, customer success, etc. cannot leverage any of that information to make faster decisions that can positively affect your bottom line and lead to greater customer satisfaction.

Operational Analytics

Operationally Analytics is a category of business analytics that shifts the focus from simply understanding data from various software systems to actually putting that data to work in the tools that run business processes. Instead of just using dashboards to make decisions, Operational Analytics is about turning insights into action - automatically.

Operational Analytics vs Traditional Analytics

Analytics is focused on bringing all kinds of different data types together and visualizing them to paint a full picture of what's going on within the business to enable new capabilities. This data is typically presented in a Business Intelligence tool or dashboard and shared on a set interval (weekly, monthly, quarterly, etc.) with the broader organization. Traditional analytics is only focused on providing a high-level view of different KPIs for strategic decisions and everyday operations.

Operational Analytics leverages data to actually “do things”. An example of this could be triggering an email when a customer signs up or makes a purchase. Another example could be enriching CRM data with product usage data to provide real-time insights to sales and marketing teams. Operational Analytics is all about syncing data between systems to communicate with users, bill customers, alert employees, etc. Conversely, traditional analytics is often seen as one of many “destinations” for the operational data pipeline.

Basically, operations represents the actions taken to leverage data in real-time, and analytics embodies the business decisions which are formed on the data that exists in a dashboard. The challenge is that both are needed to run a successful business.

What Makes Operational Analytics Unique?

The core differentiator behind Operational Analytics is data accessibility. Operational Analytics democratizes the data in your warehouse so that your non-data teams can leverage that information in the tools like Hubspot, Google Ads, Salesforce, Braze, Iterable, Marketo, Amplitude, etc. By pushing data back into the native tools of your end-users, you establish a single source of truth across your organization and remove the blockers that exist between your business users and data teams.

The Challenge of Operational Analytics

The persistent challenge with operational data is that it's not easy to get your various tools to “talk” to one another. You need to figure out how to get data to flow dependably and accurately between each pair of applications. Tools like Fivetran, Matillion, Stitch, etc. have made it really easy to move your data into the warehouse, but moving data out of the warehouse is another challenge altogether.

Emails addressed to {{first_name}} showcase this notorious problem. Another challenge could be something as simple as a B2B software company trying to sync product usage data to a CRM so the sales team knows when to reach out to a customer, or an e-commerce company trying to sync purchase data to an ad network so that recent customers don’t get targeted with an ad for something they already bought.

Solving this problem usually requires your data team to build a custom integration for every data pipeline. Even worse, these pipelines are hard to build and difficult to maintain because APIs are constantly changing. Maintaining multiple pipelines at scale is nearly impossible unless your company has the resources of a behemoth like Google.

CDPs (Customer Data Platform) and point-to-point iPaaS (Integration Platform as a Service) solutions have tried to tackle this challenge but both have serious shortcomings. CDPs simply create another source of truth and iPaaS solutions require teams to build complex workflows that are nearly as difficult to maintain as data pipelines and neither of these two solutions lends itself to the modern data stack.

The beauty of analytics data (which turns out to be the key to unlocking Operational Analytics) is that it's often the only realm where your different datasets live together harmoniously. To be specific, your data warehouse tends to be the single source of truth for all of the data. Analytics data is tied together neatly through models that form the foundation of the digestible, contextual charts that analytics tools like Tableau, Looker, Thoughtspot, etc., provide.

Thanks to innovations in data warehouses and the surrounding ecosystem, bringing data together for analytics has never been easier or more cost-effective. Dashboards are useful in decision-making for your overall business, but they don't really provide a tangible way for you to act on that data.

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, companies are now beginning to use the warehouse as not just the foundation for analytics, but as the "hub" for all operational data workflows. This is Operational Analytics.

The circle of data integration - Lucid.jpeg

The warehouse as a hub for customer data

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

  • It's simple to aggregate and integrate your data into data warehouses; it's what they're designed for, and data integration tools like Fivetran have made this even easier by building connectors to handle your data pipelines. Your teams can now 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 delivered until recently. Cloud data platforms like Snowflake have completely revolutionized the data warehousing space, fully separating storage/compute, enabling unlimited scalability and faster query performance, all in low code SQL-based platforms. Likewise, tools like dbt have made it really easy to model and transform data so you can leverage it. Once the data is in your warehouse and fully transformed, the logical step is to make that data available in your operational systems so that your business users can take action on it without having to go through the data teams.

  • Since companies typically own their warehouse, the data never has to leave your environment and fall into the hands of yet another vendor. This means there is more security and less to worry about as it relates to regulations around HIPAA, GDPR, etc… Every organization wants full control of its data - this is the future.

  • This approach also breaks down silos between your data teams and business teams by creating a clear handoff: data teams own the raw data and model it into clean data, which then empowers business teams to manage and sync that data into the tools they need to run the business. Even better, your engineering team can finally focus on the jobs they were actually hired for and your business teams can get access to the data they need to make instant decisions. When data silos exist, everyone in your organization has a slightly different view of the data. This is only solved by taking the transformed data out of your warehouse and syncing it into your operational tools. Doing this establishes your data warehouse as the single source of truth. Your business teams will no longer have to worry about how old the data is or how recently it was updated.

How does Operational Analytics Work?

This new “analytics layer” has completely transformed the way analytics is done. With the warehouse at the center of your technology stack, it's easier than ever before to address multiple analytics use cases. Implementing Operational Analytics on top of this layer only requires a few core components, but your architecture should end up looking something like this:

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Operational Analytics in practice

Why Operational Analytics is Important & why you should use it

It's really easy to get stuck on your data. All too often, good ideas come out of analytics but they fizzle into nothing when you actually need to put your data to work. Reporting alone is necessary, but not sufficient. It doesn't actually drive the actions that move the needle forward. Modern companies can no longer just make data-driven decisions. You need to act on your data and do so automatically.

Operational analytics is important because it enables “continuous analytics”. By moving data out of the warehouse and into your SaaS applications you create a feedback loop where you are constantly enriching the data in your business tools and reducing the turnaround time to get access to that same data.

When your ELT tool collects data from your external sources and ingests it back into the warehouse it will be cleaner and more accurate than before. Given time, this will not only improve your data models but will also help turn your data into a competitive advantage because your marketing team will be able to iterate and experiment instantly with no delays.

Now you know how operational analytics can benefit you, let's look at the different ways you can use it in your company.

Operational Analytics Use Cases

There is a near unlimited amount of use cases that Operational Analytics can solve and discover. However, many of the most practical use cases tend to be focused around automation, product analytics, and in between sales and marketing teams were frequently updated and accurate data is extremely important to day-to-day decisions.

How is Operational Analytics used in Sales?

A common example of Operational Analytics is often found within SaaS companies leveraging a freemium model. New users can sign up and use the product up to a certain limit, at which point they then have to pay.

Product usage data is captured in a BI dashboard and used 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. Sales reps who spend more time personalizing their outreach towards a specific use case usually yield better results.

To personalize on an individual level, salespeople have to track down this information across a variety of systems like Slack, Salesforce, etc. With Operational Analytics, the same data that is feeding into a BI dashboard is automatically synced into a CRM like Salesforce. This means that contact and account records are enriched to show whether or not the user is fully onboarded, their last login date, and the user's integrations. This leaves the sales team more time to help existing customers and crack into new accounts.

Hubspot_3.png

This isn't a hypothetical example. It's exactly how Retool used Hightouch 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 a 500% increase in click rate and increased feature adoption.

Jake Levinger.

We have really granular data about our customers in the warehouse, but it generally gathers dust there. Hightouch makes this data more valuable and actionable by allowing us to make it available across our marketing stack and business systems. All without relying on product and eng.

Jake Levinger

Jake Levinger

Marketing Ops

How is Operational Analytics Used in Marketing?

Since most marketers don't know SQL, it can often be challenging to access the customer data that exists within the warehouse. As a workaround to this problem, data analysts are forced to deliver various datasets to marketing teams in the form of a CSV, and data engineers are tasked with building net new data pipelines for every single business tool whether it be a CRM like Salesforce, an Ad platform like Google, or a product analytics platform like Amplitude.

Even worse, engineering typically has a backlog of other more important priorities to address, so it can take a substantial amount of time to deliver data to marketing and by the time it is made available the customer or prospect is already at a different point in their journey.

With Operational Analytics, data is synced in real-time, enabling marketing teams to improve customer experiences by sending lifecycle marketing campaigns to customers across any channel as soon as they take an action (example: abandoning a shopping, creating a workspace, downloading a whitepaper, etc.) They can also increase ROAS by retargeting customers who visited a pricing page and exclude customers who already purchased.

Additionally, they can identify high-value customers by creating lookalike audiences and also send conversion events to different ad networks to optimize targeting and customer acquisition costs. Best of all, marketing teams don't have to wait around for engineering to give them access to the data they need to run their campaigns. Instead, they can rapidly experiment and iterate.

How is Operational Analytics Used in Product Analytics?

Businesses are also leveraging Operational Analytics in Product Analytics platforms like Amplitude, and Mixpanel to help companies derive better insights into how their customers are using their products. A common use case revolves around getting information like user id, service area, or product usage information into these product analytic tools to generate more insights. This enables more complex and deeper analysis on a more granular level, while also ensuring that different teams have the same view of the customer.

Zeplin was able to achieve this using Hightouch. Mixpanel, Zeplin's product analytics tool, had events and reports on how certain customers were using the product. But it didn't have context into Zeplin specific concepts, such as how many Projects a given user had, so Zeplin used Hightouch to enrich group profiles in Mixpanel. This enabled the team to answer questions like "How often do organizations with 5 collaborators log in using SSO?". All of this helped Zeplin segment its customer base and decide on its pricing strategy.

How is Operational Analytics Used in Automation?

Automation is extremely crucial to organizations for multiple reasons. It eliminates human error, speeds up processes, and also gives teams greater visibility into different aspects of their data. One of the best use cases for automation is around messaging and notifications in tools like Slack and Mattermost. This is because these tools have extremely fast response times. Vendr uses Operational Analytics to address this exact use case.

Many companies today connect these communication tools with their various data sources to alert customer success teams when accounts go dormant, share high-intent leads and transactions with the sales team, send product usage characteristics to the product team, and provide insight into various campaigns for marketers. Best of all, this information is all shared in real-time. Automation doesn’t just stop at notifications and messaging either, there is an unlimited amount of use cases that could be addressed within each specific tool. We’ve written two posts on this exact topic:

The Benefits of Operational Analytics

Data silos are a huge problem and they are not going away. It's a challenge across all industries. Operational Analytics acts as the final piece of the puzzle, helping share the insights derived in the data warehouse across the organization by accelerating data enrichment and pushing transformed and clean data back into operational systems.

Ultimately, Operational Analytics democratizes the single source of truth that exists in your warehouse and makes it available in real-time in the tools your business users leverage to make daily decisions. With Operational Analytics flowing from your warehouse, your teams can finally focus on the work that matters most, without having to hop back and forth between various tools, spreadsheets, and dashboards to get the information they need.

How to Get Started with Operational Analytics

If you want to get started with Operational Analytics there is no reason to wait. We’ve created a complete guide you can follow to get started.

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