In this era of big data, operational analytics presents both a challenge and an opportunity. The data tsunami arrives from different sources in real-time, overwhelming an unprepared team. However, this same data gives businesses a competitive advantage if they master it with the right technology and approach.

This article explores how operational analytics works, including use cases, best practices and implementation steps. Also, learn how to use data to guide daily decisions, streamline processes and grow revenue.

Inside this article:

What Is Operational Analytics?

Operational analytics is about using real-time data for daily decisions. Relevant business information flows from many sources into tools that analyze that data and identify problems and opportunities. This actionable data is then used by teams to inform their decision-making.

Although it is a subset of business analytics, operational analytics focuses on immediate action. With traditional analytics, IT specialists or analysts mine information stored in data warehouses. Then, using sometimes complex algorithms, they produce weekly, monthly or quarterly reports that executives use to make strategic decisions.

Today, however, companies must keep up with rapid changes in the business landscape. Operational analytics makes insights available in near-real-time for staff who need to address support tickets, repair remote equipment or adjust pricing or sales tactics.

In addition, operational analytics empowers predictive analytics by leveraging data mining and AI to connect data points. For example, this approach processes data and sends it to tools such as help desk and social media messaging platforms. This unified effort helps businesses achieve operational excellence.

Operational analytics differs from operational performance analytics, which provides current information and historical trends and KPIs. Operational performance analytics helps you pinpoint the business problems you want to solve. Knowing your goals enables you to choose the right data analytics setup and statistical models.

Operational analytics help teams keep up with client demands. Customers expect better products and services and nearly 100% uptime. To provide that, businesses need to operate efficiently. Learn more in our article on how to improve your operational efficiency.

Key Takeaways

  • Operational analytics enables companies to keep pace with rapid changes in the business landscape.
  • Data from multiple sources flows into systems that provide actionable insights on the fly.
  • The benefits of operational analytics include smoother operations, lower costs, better products and services and ultimately happier customers.
  • You can use operational analytics platforms in many fields and functions, such as agile development, customer support and predictive maintenance.

Operational Analytics vs. Business Analytics

Operational analytics helps teams make decisions in the moment. Business analytics, on the other hand, focuses on trend data to inform strategic planning. Operational analytics covers customers, orders, inventory and other pieces of the operations puzzle. Business analytics uses mainly historical data.

The business analytics approach studies historical trends and displays the results in dashboards, typically used by leadership. For example, business intelligence dashboards might show how long a product takes from the customer placing an order to when the packaged order leaves the warehouse.

Such insights help with staffing and processes. Read our article on business intelligence examples to learn more.

The operational analytics approach delivers insights not only to leadership but also to managers and frontline workers. This approach automates previously manual data-gathering tasks, such as searching and sorting on spreadsheets. The process guides decision-making in everyday transactions.

What’s the Difference? Operational Analytics vs. Business Analytics

Business Analytics Operational Analytics
Reviews historical trends and patterns Analyzes and produces insights in real-time or near real-time
Produces daily, weekly or monthly reports on KPIs Updates dashboards in real-time or uses automation to trigger decisions or events as part of a workflow
Reacts to patterns to create strategic plans Predicts future activity and prompts action
Provides a high-level, long-term view Provides a view into daily operations

How Operational Analytics Works

Operational analytics works when companies combine multiple relevant data sources. First, the information goes to a data warehouse where it can be sorted. Valuable insights then flow to operations teams so they can act right away.

One top data source today is Internet of Things (IoT) or connected devices. Other common sources are remote sensors, point-of-sale (POS) systems, customer relationship management (CRM) platforms and enterprise resource planning (ERP) software.

With operational analytics, all employees can access the results of rapid, complex calculations that previously required specialized IT or BI analyst skills and spreadsheets. These analytics now become part of automated workflows. For example, the system can surface communications from high-value customers from among thousands of complaints and questions.

Why You Should Use Operational Analytics

You should use operational analytics because it enables prompt decision-making at scale and can help teams solve problems. As a result, employees can make good decisions, quickly.

Operational analytics also:

  • Reveals trends and patterns that you might not otherwise see in large volumes of raw data.
  • Shifts the emphasis from customer surveys. You’re not asking the customer for insights after the fact; you’re observing how they behave.
  • Improves production capacity forecasting.
  • Reveals ways to streamline a variety of processes.
  • Reduces downtime because you can see problems as they occur or even predict failures.
  • Optimizes self-service analytics. Authorized employees can pull data and visualize it as they need it without involving IT or specialized analysts.

Benefits of Operational Analytics

The benefits of operational analytics vary based on department, but a common threat is the ability to predict problems and respond to oncoming issues before customers even notice. Overall, operational analytics reduces downtime, increases productivity and improves forecasting.

Operational analytics makes connections for you. You don’t have to go into each data source or program to do tasks yourself, such as identifying product use in one tool and then relaying that to your email engine. The resulting benefits include the following:

  • Improved customer experiences: Operational analytics can help teams troubleshoot problems as they arise. Precise intelligence can offer customers personalized deals and experiences.
  • Improved customer satisfaction: Reviewing trends helps companies identify new products and services to offer customers.
  • Faster decision-making: Make smarter decisions every day rather than waiting for quarterly or monthly reports. Operational analytics provides information to the people who have the ability to tweak processes and workflows.
  • Increased productivity: Operational analytics can reveal waste and duplication of effort as well as limiting manual steps, such as updating spreadsheets and sending email asking for data.
  • Increased profits: Machine learning and statistical models highlight efficiencies that help you save money.
  • More team member engagement: Everyone has the power to make better decisions based on improved intelligence.

Why Is Operational Analytics Important?

In this age of cloud computing, mobile devices and IoT, companies accumulate large volumes of data. With traditional approaches, much of it sits unused. The insights that operational analytics make possible help companies avoid problems and find opportunities.

On an employee level, teams can now use all that data to make better daily decisions, head off problems and satisfy customers.

How Is Data Analytics Used in Operations?

Data analytics systems provide operational insights that vary based on industry. The commonality is that these tools compile real-time data and put it in the hands of people who can act on it right away. That increases efficiency, improves customer satisfaction and reduces costs.

Analytical models can help solve supply chain problems for businesses. For example, BOPIS, or “buy online, pick up in store” is popular with customers, but physical outlets must have stock available for pickup. Modeling helps with inventory management by forecasting demand based on geography. Systems can also generate reports on shopper behavior to influence how retailers price items and schedule sales.

Operational Analytics Use Cases

The use cases for operational analytics apply to many fields, from banking, ecommerce and finance to marketing, manufacturing, pharmaceuticals, sales and utilities, and run the gamut from customer support to agile development.

Almost every metric tracked through operational analytics ultimately traces back to customer satisfaction. Satisfied customers should help your bottom line. The ability to see a user’s activity and, more importantly, learn what attracts them gives you the information to meet their needs more effectively and efficiently.

Specific operational analytics use cases include:

  • Agile development: Dev teams can leverage operational analytics to provide near-real-time feedback on product use patterns so companies can improve their apps based on how people actually use them.
  • Customer support: Smart systems can detect first-time users of internet-enabled products or services and offer proactive welcome messages and quick-start guides. For higher-value products, customer service reps could reach out with a text or phone call to welcome clients or help with problem setups.
  • Personalized offers: Complex analysis can go beyond demographic segmentation to create a 1:1 relationship. For example, a store might input a customer’s blouse size and color preferences and automatically send a personalized email when new stock arrives that would appeal to the buyer.
  • Predictive maintenance: Services from SaaS providers to utilities can use real-time data analysis to monitor for malfunctions. For water utilities, remote sensors provide regular data feeds, and divergence from expected usage models can highlight pipe breaks and leaks before customers report street flooding. An internet service provider could spot a power outage or downed wire and dispatch a team, then post an expected fix time before complaints roll in.
  • Risk modeling: Banks and lending institutions use the predictive capabilities of operational analytics systems to assess credit risk based on customers’ past purchases and payment histories. Platforms can also apply analytical models to volumes of data to detect patterns of fraud.
  • Cross-selling: Predictive analytics leverages the power of your existing customer base to increase sales. The modeling can suggest new products for clerks or websites to recommend, such as an automotive supply store suggesting its wiper blades to an existing tire customer.
  • Churn reduction: Winning new customers is one of the most expensive undertakings in sales. With operational analytics, models can detect when customers are at risk of leaving and tempt them with new offers. For example, a restaurant may email a customer who has skipped her weekly lunch order with a coupon for a free side of fries with the next sandwich.
  • Marketing: By analyzing customer activities and online behavior, predictive analytics correlates buyers to potential purchases and shows the best channels for promoting new products to the existing customer base.
  • Prioritizing work: Operational analytics platforms can present choices so operations staff can act on them. For example, a customer service platform might sort through incoming tickets and automatically push requests from loyalty club members to the top of the line.

Examples of Real-life Operational Analytics in Action

A range of companies use operational metrics and KPIs to analyze masses of data and deliver customized customer experiences and preventative maintenance, among other benefits.

  • Customized experiences: Amazon uses data on past purchases and product views to tailor what a user sees with each page visit. Nissan tracks ecommerce sites to determine what car models and colors are popular in geographic regions. The Land O’Lakes food company uses data analytics and AI to set prices.
  • Preventive maintenance: Shell uses predictive analytics to determine the functional life of drilling machine parts. Understanding when components will fail saved the company millions in inventory storage costs. Duke Energy, an electrical utility based in North Carolina, implemented a predictive analytics program employing sensors in 50 power plants. When a turbine began returning unusual vibration, maintenance staff discovered loose bolts, potentially saving millions from a complete malfunction.

Operational Analytics Best Practices

Operational analytics best practices are all about asking the right questions and having the right data. When you understand the problems you want to solve, analytics expands beyond collecting and describing historical trends. The approach displays predictive capabilities.

  • Customize data-analysis software: Ensure that the correct data goes in. Find out how the system notes missing values. Platforms often impute or assign values based on reference to the processes the data contributes to. Investigate the data model to check results.
  • Decision-making must be analytics-based: Even with analysis, companies may still use a manual approach to decision-making. This approach may impede their ability to action analysis results.
  • Build analysis capabilities: Use descriptive analytics and key performance indicators to build your operational analytics savvy. Descriptive analytics consider what has happened. KPIs provide a way to track patterns.
  • Use business questions to determine the data and data model: What answers does the business need? For example, will master data about suppliers and customers, reference data about currencies and compliance, or transactional data about orders and payrolls serve best? Then consider what data model can best transform this data for your purposes.
  • Incorporate outside data: Data is never perfect. If you have doubts about collecting data, try using free data sets or buying data from services. Another option that requires less storage and potentially reduces errors is data sampling.

How to Implement Operational Analytics

To implement operational analytics, start with your business goals and operational goals. Second, determine your operational KPIs to support the goals. Third, make sure the data you need is available and accurate. Then, choose an analytics platform and train the team on it. Finally, implement the system and adjust as necessary.

Here are the more detailed steps to implement operational analytics and KPIs:

  1. Determine or confirm your goals. This step applies to your overall business goals and operational goals. Talk to senior executives, department managers and team members to ensure everyone is aligned.
  2. Determine or confirm your operational KPIs that support the goals. You must measure the right data points that drive operational efficiency and effectiveness. Again, make sure all levels of the company are aligned. If not, make sure everyone is aware of areas of disagreement. Evaluate them and confirm the approach with the decision-makers.
  3. Ensure data availability and accuracy. Ensure that you can access the necessary data, which may originate in different platforms and departments. Identify any obstacles and plan to overcome them or work around them.
  4. Choose an analytics platform that ties together all the necessary data points. Today’s platforms can offer real-time data and dashboards to help optimize operations from start to finish. Conduct your selection process needs thoroughly and carefully. If you already have a platform, ensure that it provides the necessary data and displays it in a user-friendly way.
  5. Train the team to use the platform. Make sure the team sees its potential to find inefficiencies and optimize your operations.
  6. Do a test implementation. You might try a few data points first to ensure everything is working. Then, confirm with the people using it.
  7. Fully implement the system. Again, talk to the people using the system to ensure it’s working as expected. If not, consider adjustments and make them.
  8. Revisit the system. Make sure in the coming weeks and months that everything is working properly.
  9. Report on how operational analytics are helping or not helping meet goals. You might find that you need further adjustments, such as a new KPI or goal.

Best Business Model for Deploying Operational Analytics

The best business model for deploying operational analytics answers business questions rather than data questions. Although data scientists and analysts alone cannot build the perfect model that successfully queries the correct data.

Instead, any data project must use knowledge of the customer and business goals to produce insights. These insights will predict customer behavior, anticipate service problems or forecast sales opportunities. Other best practices include the following:

  1. Decide on measurable goals: Understand what business problems an operational analytics platform solves. Select the right KPIs and metrics that best align with the stated goals. Choose five to 12 to start.
  2. Start with a single use-case: Start with one business problem, one goal or one KPI you intend to implement. This can serve as a test and will allow more adjustments before you deploy the entire setup.
  3. Obtain management support: Few projects go far without a management champion.
  4. Communicate the value throughout the organization: Implementation can’t be a project only for IT. Everyone must understand how their work can benefit from operational analytics.

Rise and Future of Operational Analytics

Adoption of operational analytics has grown steadily in tandem with the growth of big data and the IoT universe. In 2019, Verified Market Research pegged the operational analytics market value at $7.92 billion and projected that it will reach $28.74 billion by 2027. “Intelligent” devices appear in homes, offices and the field and ranges from doorbells to remote utility sensors.

Organizations that benefit from operational analytics in the future will demonstrate specific characteristics. They will have executive support. They will have standard operational reporting to support current activities and concerns but will always seek to go deeper. They will incorporate a wide range of data sources and pursue operational analytics as an interdisciplinary effort, with a centralized team, not strictly as an IT project. They will tie efforts to resulting tangible business benefits that can prove the value of operational analytics and enable them to grow their programs.

Big Data and Machine Learning: The Backbone of Operational Intelligence

Today’s companies depend on big data. Operational analytics systems gather this massive amount of data from multiple sources, often in real-time. The systems then use machine learning and statistical models to extract insights into daily operations. Teams use dashboards that visualize the data, allowing them to find inefficiencies and opportunities.

Teams may not notice problems immediately if they rely only on traditional analytics and long-term trend analysis. They can keep up with operational details more effectively with analytics platforms using big data and machine learning. They can turn strong operational intelligence into operational excellence.

Benefits of Operational Analytics Tools

Many companies gather data in customer relationship management (CRM) and enterprise resource planning (ERP) platforms. But often, data is siloed, so it’s not contributing to cross-department efforts. Integrated CRM and ERP tools are able to pull data from a wide range of sources and derive insights for the teams that need them the most.

With automation, operational analytics tools are able to sort and prioritize information and share it with other systems, so you don’t have to open spreadsheets and send emails manually. Operational analytics platforms are robust enough to manage continual incoming data streams and large volumes of queries.

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How NetSuite Helps Operations Managers Reach Operational Excellence

Operations managers can make better daily decisions by using the artificial intelligence and machine learning-based capabilities of NetSuite Business Intelligence.

With the NetSuite SuiteAnalytics Workbook, you can easily apply filters and create real-time visualizations. Drag and drop modules in the dashboards to create charts and pivot tables. Build the KPI dashboards you need, whether you have experience with coding or not. You can even save and share workbooks to promote business intelligence across your organization.