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What is Operational Analytics?

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What is operational analytics?

Competition. Everywhere, businesses face competitive challenges to market share, product innovation, and holding on to exceptional talent. It's no wonder organizations constantly seek ways to gain a competitive advantage and streamline their operations. Operational analytics has emerged as a powerful tool to achieve these goals, offering real-time insights that drive informed decision-making and operational excellence.

So what is operational analytics? Operational analytics is the process of extracting real-time insights from customer data and operational data sources to improve decision-making for daily business operations. (At MongoDB, operational analytics is also referred to as operational intelligence or real-time business visibility.)

Unlike traditional analytics, which often relies on historical data stored in data warehouses, operational analytics leverages real-time data from operational systems to generate immediate, actionable insights. By syncing data directly from your data warehouse to your operational analytics platforms — including operational analytics tools like Tableau and other BI tools integrations — operational analytics streamlines workflows and enhances automation without incurring significant engineering efforts. This sub-category of business analytics emphasizes improving existing operations using real-time data.

The power of real-time data in operational analytics

Operational analytics plays a pivotal role in a variety of business functions, providing invaluable insights that drive efficiency and innovation.

By harnessing real-time data, operational analytics empowers businesses to make informed decisions swiftly and accurately, leading to improved operational performance and strategic advancements. These insights enable companies to streamline processes, reduce costs, and enhance customer experiences, thereby fostering a culture of continuous improvement and innovation.

At the core of operational analytics lies the ability to harness and analyze vast amounts of data from multiple sources in real time. This includes:

  • Customer data.
  • Sales data.
  • Production data.
  • Supply chain information.
  • Marketing campaign metrics.
  • Customer service interactions.

A holistic view of operations

By integrating data from various operational systems, businesses can gain a holistic view of their operations and identify areas for improvement. The key components of the data ecosystem in operational analytics include:

  • Data warehouses: Centralized repositories storing historical data for long-term trend analysis.

  • Data lakes: Large-scale storage systems handling diverse types of raw data for advanced analytics and machine learning applications.
  • Operational data stores: Databases designed to support real-time querying and analysis of current operational data.

  • Data pipelines: Automated processes ensuring smooth data flow from source systems to analytics platforms.

Implementing operational analytics

Here are some best practices for implementing operational analytics:

  • Define objectives: Clearly articulate the business goals and key metrics that operational analytics will address.
  • Identify data sources: Determine which operational systems and data sources will provide the most relevant information.

  • Establish data integration: Implement robust data pipelines and integration platforms to consolidate data from multiple sources.

  • Choose analytics tools: Select appropriate operational analytics tools and platforms that align with your business needs and technical capabilities.

  • Develop analytics models: Create predictive and prescriptive models using techniques such as data mining, machine learning, and artificial intelligence.

  • Empower users: Provide business users with access to analytics dashboards and tools, enabling them to leverage operational analytics in their daily work.

  • Continuous improvement: Regularly review and refine your operational analytics processes to ensure they remain aligned with evolving business needs.

Benefits of operational analytics

Implementing operational analytics can yield numerous benefits for organizations:

  • Improved decision-making: Real-time insights enable faster, more informed decision-making across all levels of the organization.

  • Enhanced operational efficiency: By identifying bottlenecks and inefficiencies, businesses can streamline processes and reduce costs.

  • Increased customer satisfaction: Analyzing customer data and usage patterns helps organizations tailor their products and services to meet customer needs more effectively.

  • Predictive maintenance: By analyzing equipment data, companies can anticipate maintenance needs, reducing downtime and extending asset lifespans.

  • Optimized resource allocation: Data-driven insights help organizations allocate resources more effectively, improving overall productivity.

Examples of teams leveraging operational analytics

Development and product teams

Developers and product managers use operational analytics to gain insights from application usage data. These insights are crucial for making informed decisions about product features, performance improvements, and user experience enhancements. By understanding how users interact with applications in real time, teams can prioritize feature development, swiftly address issues, and continuously improve the product.

Marketing teams

Marketers leverage operational analytics to analyze data from websites, shopping carts, and digital campaigns. Real-time insights enable them to adapt marketing strategies on the fly, optimizing campaigns for better performance and higher ROI. For example, understanding user behavior and purchase patterns helps marketers target the right audience segments with personalized offers, ultimately driving sales and customer engagement.

Operational teams

For operations teams, tracking order and shipment data is critical. Operational analytics allows these teams to monitor the entire supply chain in real time, identifying potential disruptions or inefficiencies. By analyzing logistics data, operations managers can optimize routes, manage inventory levels, and ensure timely deliveries, thereby improving overall operational efficiency.

Support teams

Customer support teams benefit from operational analytics by analyzing tickets, issues, and call data. This enables them to identify common customer problems, measure the effectiveness of support processes, and enhance the quality of service provided. With real-time insights, support teams can proactively address issues, reduce response times, and improve customer satisfaction.

Why is operational analytics important?

Operational analytics plays a critical role in the optimization of your operations. Its proper use leads to increased profits, better decision-making, and a competitive advantage. Operational analytics can be inserted in nearly all business processes, ranging from mass manufacturing to video games. Thanks to the addition of new analytics features, MongoDB Atlas is a platform that has all the characteristics of an operational analytics processing engine, as it supports complex queries, low data latency, low query latency, high query volume, live sync with data sources, and mixed types of data.

Operational analytics: Two real-world examples

For example, a manufacturer could optimize several lines of production by carefully monitoring key performance indicators to detect potential bottlenecks or unused capacity. We've talked about how Bosch uses IoT data collected and analyzed in real time to provide diagnostics and preventative measures when needed. Integrated business rules can even trigger alarms if a critical situation occurs. An online consumer packaged goods retailer, Boxed, shows how real-time business visibility can inform decision-making and make or break a company.

Implementing operational analytics: Challenges and considerations

While operational analytics offers significant benefits, implementing it effectively, especially with real-time data, is not always easy or fast. Organizations often face several challenges in this process:

  1. Data integration and processing: Most companies traditionally send their application data to a centralized repository (such as a data warehouse or data lake) via batch processing, typically at the end of the day or week. This data then undergoes an Extract, Transform, Load (ETL) process to convert it into a more usable format consistent with data from other sources.

This approach presents several issues:

  • The ETL process is time-consuming and complex to set up and maintain.
  • By the time data is processed, it may be outdated, limiting its usefulness for real-time operational decisions.
  • Consolidating data from multiple sources and formats can be intricate and time-intensive.
  1. Technology infrastructure: Implementing operational analytics often requires upgrades to existing IT systems and infrastructure. This may involve investing in new hardware, software, or cloud services capable of handling real-time data processing and analytics.

  2. Skills gap: Organizations may need to invest in training their existing workforce or hiring specialized data analysts and data scientists to fully leverage operational analytics tools and interpret the results effectively.

  3. Change management: Adopting a data-driven culture and encouraging employees across all levels to use analytics tools in their daily decision-making can be challenging. It requires a shift in mindset and work processes.

  4. Data security and privacy: As operational analytics often deals with sensitive business and customer data, ensuring the security and privacy of this information is crucial. This involves implementing robust security measures and complying with relevant data protection regulations.

By addressing these challenges proactively, organizations can more successfully implement operational analytics and harness its full potential for improving business operations and decision-making.

The future of operational analytics looks increasingly promising. The following trends and technologies are poised to reshape how businesses leverage data for real-time decision-making and operational excellence:

Advanced AI and machine learning (ML)

Advanced AI and machine learning will bring about significant changes. AI models will improve in forecasting future trends, helping businesses anticipate market changes and customer behaviors with greater precision. ML algorithms will handle complex decisions autonomously, allowing human resources to focus on more strategic tasks. AI systems will become more adept at detecting anomalies, identifying unusual patterns in real time and alerting businesses to potential issues or opportunities.

Internet of Things (IoT) integration

The Internet of Things (IoT) will also revolutionize operational analytics. IoT sensors will capture comprehensive data from all aspects of operations, enhancing analysis capabilities. Real-time IoT data will predict equipment failures more accurately, reducing downtime and maintenance costs. IoT systems will optimize inventory levels and reduce waste by providing real-time visibility. Furthermore, IoT devices will offer deeper insights into customer behavior and product usage patterns.

Edge computing

Edge computing will enable faster insights by processing data closer to its source. This allows businesses to make real-time decisions without delays from transmitting data to a central location. Local data processing will reduce bandwidth requirements and alleviate network strain. Edge computing will enhance privacy and security by keeping sensitive data processing at the edge. Additionally, it will ensure continued operation and data analysis even during network disruptions.

Augmented analytics

Augmented analytics will democratize data analytics with AI-powered tools. AI will automate data preparation by cleaning, organizing, and structuring data for analysis. Augmented analytics tools will automatically identify trends and anomalies in data. These tools will also generate human-readable narratives explaining complex data patterns. AI assistants will guide users through the analytics process, suggesting relevant visualizations and techniques.

Natural language processing (NLP)

Improved NLP will make it easier for non-technical users to interact with analytics platforms. Users will query data and receive insights using natural language, similar to interacting with a virtual assistant. Voice-activated analytics will enhance accessibility, allowing hands-free access. NLP will enable real-time analysis of customer feedback and social media posts. Advanced NLP will also break down language barriers, allowing seamless data analysis across different languages.

Quantum computing

Quantum computing, though still in its early stages, has the potential to revolutionize operational analytics. Quantum computers will solve complex optimization problems in supply chain management, financial modeling, and resource allocation far faster than classical computers. Quantum algorithms will significantly enhance machine learning model performance used in predictive analytics.

Blockchain for data integrity

Blockchain technology will ensure the integrity and traceability of data used in operational analytics. Blockchain can provide an immutable record of data sources and transformations, ensuring transparency and trust in analytics results. It will facilitate secure and controlled sharing of operational data between different parties in a supply chain or business ecosystem.

MongoDB makes it easy

Our developer data platform allows you to combine data from multiple sources to create a single, refined dataset. This can be used for real-time analytics use cases, where insights from fresh data and low-latency queries are critical. MongoDB offers:

  • A flexible data model: Build with speed to meet market demand while maintaining agility as data requirements evolve and new data is introduced.

  • Aggregation framework: Surface insights faster and more easily integrate them into your apps and processes to enable better digital experiences for your customers.

  • Scalable platform: Ensure timing and latency requirements are met across real-time systems and applications as they grow.

  • Unified interface and API: Eliminate data silos so you spend more time making data work for you and less time working for your data.

  • Hybrid transactional-analytical processing (HTAP): Exercise greater business agility with HTAP for real-time data.

Find insights on live application data

In the simplest form of operational analytics, developers or other stakeholders want insights from a single application to help inform decision-making. The questions operational analytics can answer can be found by doing basic aggregations, sorts, searches, filters, etc., on a single dataset. MongoDB makes it easy to find those answers with the tool of your choice.

  • Atlas SQL Interface, Connectors, and Drivers enable you and your team to use popular SQL-based tools, such as Tableau and PowerBI, for analytical queries. Make Atlas data easily accessible to data analysts and other users who prefer doing deeper analysis with a SQL-based tool without any data engineering or ETL processes.

  • Atlas Charts is built for the document model and fully integrated with Atlas. It's quick to get started, build data visualizations, and share powerful insights all from the Atlas UI.

  • MongoDB Query API allows you to build modular, multi-stage aggregations on your data with your preferred coding language.

Combine several data sources for deeper analysis

Many insights that enhance decision-making at the operational level require blending data from multiple sources.

Atlas Data Federation allows you to seamlessly query, transform, and aggregate data from one or more MongoDB Atlas databases and AWS S3 buckets. Data Federation enables operational analytics via Atlas SQL Interface and Atlas Charts as it's fully integrated with both. You can also convert data into an analytical format and persist it into an S3 bucket with the $out stage for downstream systems to use.

Optimize for analytics without disrupting transactional workloads

As the amount of data required for your operational analytics grows, you shouldn't have to sacrifice simplicity and cost-efficiency to maintain performance in your analytical queries. MongoDB provides a couple of different levers to optimize your analytical workloads.

  • Atlas Database has analytics nodes with distinct infrastructure tiering. Analytics nodes have a replica data set of your primary node but are isolated so they can never be elected to be the primary node, nor will any queries slow down the performance of your primary node.

  • It enables isolating live application data for analytical queries, with fixed costs chosen separately from the rest of the Atlas cluster.

  • Atlas Data Lake (in preview) is a fully managed storage solution that provides the economics of cloud object storage and has the ability to reformat, partition, catalog, and capture statistics about the data to provide the best performance when queried.

Operational analytics: Embracing a strategic imperative

From simple ad hoc analysis to business intelligence dashboards (often referred to as operational analytics), it's important that your application data be easily extensible for better, real-time decision-making. Data extensibility is also important for machine learning whether that's model training, serving data for models in production, or observability.

MongoDB provides a suite of unified capabilities and connectors to make data collection and storage, data transformation, and insight delivery much easier. Blend data across clusters, cloud storage, and APIs and export in your preferred analytical format or simply connect your favorite BI tool directly to Atlas.

You can start building data and test your operational analytics workflow with the MongoDB Atlas free trial.