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.

By Kevin Moyer and Luke Kline on

Ops

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, infrastructure innovations in the data stack, data warehouses, data lakes, and BI tools have made it simpler and cheaper than ever before to actually 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 data are only valuable once they are used to make a change in the business that moves the needle. 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 businesses, and at worst, a waste of time. Hundreds of companies struggle with the last-mile of analytics problem: All of their important data lives in the warehouse, which makes for easy reporting, but it's too hard to take action on that data. When all of the data is contained in the data warehouse, it is only accessible by the data teams, which means that the end-users i.e. sales/marketing cannot leverage the data in real-time to take actions and make meaningful decisions that can positively impact the bottom line and lead to greater customer satisfaction.

Operational Analytics

Operational 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.

What Makes Operational Analytics Unique?

The core differentiator behind Operational Analytics is data accessibility. Operational Analytics democratizes data across organizations so that non-data teams can leverage that information in the tools that they use day in and day out. By pushing the data back into the native tools of the end-users, businesses establish a single source of truth across the entire organization because every data source showcases the most recent and updated version of the data. This means that different business teams will always be aligned and working towards the same goal and contributing to the organization’s success.

The Difference Between Operational Data & Analytical Data

Every company typically uses data either for operations or analytics purposes. Operations leverages new data to actually “do things.” An example of this type of analytical processing could be triggering an email when a customer signs up or makes a purchase. Another example could be enriching CRM data with product data. On the other hand, analytics tries to understand what is going on within the business by building executive dashboards and showing information like KPIs across sales, marketing, finance, etc. Analytics is really focused on providing a high-level view of data for executive decision-making.

Operational data is all about syncing data between systems to communicate with users, bill customers, alert employees, etc. Ideally, corporations can use it to automate different aspects of the business; which in turn lowers the chance of human error and improves overall efficiency in a variety of different areas. Conversely, analytics is often seen as one of many "destinations" for the operational data pipeline.

The Challenge of Operational Data

The persistent challenge with operational data is that it's not easy to get various tools to "talk to" one another. For each pair of tools, businesses need to figure out how to get data to flow dependably and accurately between each other. Data integration is an extremely challenging problem. This is why there are so many vendors in the space (i.e. Fivetran, Matillion, Stitch, Talend, etc.). Most companies strive to create an end-to-end flow from data acquisition, data ingestion, and data persistence. 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 their sales rep 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 purchasers don’t get targeted for something they already bought.

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

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 different datasets live together harmoniously. To be specific, the data warehouse tends to be the single source of truth across the business 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 the overall business, but they don’t really provide a tangible way for business users to act on the data.

Why is Operational Analytics becoming popular?

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 their analytics, but as the "hub" for all operational data workflows. This is operational analytics.

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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 data into data warehouses; it's what they're designed for, and various data integration tools have made this even easier by building various connectors to handle the data pipelining side of things. 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 really delivered until recently. Cloud data platforms like Snowflake have completely revolutionized the data warehousing space by creating a SaaS solution built on AWS, Azure, and Google; fully separating storage/compute, enabling for unlimited scalability and faster query performance, all in a low code SQL based platform. Likewise, tools like dbt have made it really easy to model and transform the data so that the business can leverage it. Once the data is in the warehouse and fully transformed, the path of least resistance is to just send that data out where it needs to go, which should be back into the operational systems of record so that the business users can leverage it without having to go through the data teams.

  • Since companies typically own their warehouse, the data never has to leave your purview 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 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, this means that engineering teams can focus on the jobs they were actually hired for and business teams like sales, marketing, and product can instantly get access to the data they need to make important decisions. When data silos exist, everyone has a slightly different view of the data, so if the transformed data is taken out of the data warehouse and pushed back into the operational systems, a single source of truth now exists across the entire organization. Business users like sales, marketing, and product will no longer have to worry about how old the data is or how recently it was updated.

All of these factors are dramatically changing how companies think about analytics.

Why Operational Analytics is Important?

In the past analytics focused on understanding the 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 they fizzle into nothing when data actually needs to be put to work.

It’s really easy to get stuck on the data. 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. They need to act on those decisions with data and do so automatically.

The best way to do this is by empowering business users. Most people don’t know SQL, so if data is taken out of the warehouse and pushed back into the operational systems SQL is no longer a barrier for different teams when it comes to asking questions and getting access to the data. This is Operational Analytics.

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 found between sales and marketing teams because frequently updated and accurate data is extremely relevant to their 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. Users can typically sign up and use the product up to a certain limit, at which point they then have to pay. Analytics is then done 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. Often, sales reps who spend more time personalizing their outreach to the specific use case tend to over-perform.

However, salespeople typically have to track down this information across a variety of systems like Slack, Salesforce, etc., in order to get the full scoop before sending out a personalized and relevant email. 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.

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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.

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

Marketing Ops at Retool

How is Operational Analytics Used in Marketing?

One of the biggest challenges in marketing across every single industry is data accessibility. Nearly every organization today uses data systems like a data warehouse to transform and consolidate the data into a single location for reporting purposes. The data is available to the data teams, but it is difficult for marketers to access without a deep knowledge of SQL.

Since most marketers don’t know SQL, this is challenging. As a workaround to this problem, data analysts often deliver various datasets to marketing teams in the form of a CSV. These are manually uploaded into the various systems, whether it be a customer relationship management (CRM) platform like Salesforce or a product analytics platform like Amplitude. Even worse, engineering typically has a backlog of other more important priorities to address before the request from marketing, so it can take a substantial amount of time meaning the data is stale and unusable for marketing purposes. By the time it is usable, the customer or prospect is at a different point in their journey.

With Operational Analytics, marketing teams can improve customer experiences by sending lifecycle marketing campaigns to customers across any channel as soon as they invite a friend or abandon a shopping cart. 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, they 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, iterate, and ask different questions.

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 in these product analytic tools to generate more insights. This enables more complex and deeper analysis on a more granular level and also ensures that different teams have the same view of the customer.

Zeplin was actually able to achieve this using Hightouch. Mixpanel, Zeplin's product analytics tool, had events and reporting for how certain customers were using the product. But it didn't have context into Zeplin specific concepts, such as how many Projects that a given user had. They used Hightouch to enrich group profiles in Mixpanel so that the team could answer questions like "How often do organizations with 5 collaborators login using SSO?". This enriched data helped Zeplin segment their customer base and decide their 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. These tools have extremely fast response times.

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

Cloud databases do a great job of providing the business leaders and executives with a high-level detail of data in dashboards powered through Business Intelligence tools like Looker, Tableau, etc, but they do little to actually share that data with the business users. One of the major benefits of a cloud data warehouse like Snowflake is that it creates a single source of truth. However, this single source of truth exists solely within Snowflake, creating a data silo.

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 organizations share the insights derived in the data warehouse across the organization. Operational Analytics accelerates data enrichment, pushing transformed and clean data back into operational systems so that non-technical users can leverage it to generate value for the organization. Ultimately, Operational Analytics provides increased visibility which leads to better decisions across the entire company

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 or schedule a call with us here: 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.

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