_Top 5 tips for 'Real-Time Analytics'

Real-time analytics can revolutionize decision-making providing immediate valuable insights to drive strategic actions. From optimizing timing for analytics to fostering seamless data sharing across organizational boundaries, uncover the key strategies for success in leveraging real-time insights. Don’t miss out on unlocking the potential of real-time analytics to streamline operations and gain a competitive advantage.

Introduction

In today’s fast-paced digital landscape, organizations are harnessing the power of real-time analytics to drive their business strategies forward. Real-time analytics, the process of analyzing data as it is generated or received, has become essential for companies striving to stay ahead in an era of rapid technological advancement and evolving consumer demands. From digital business acceleration to continuous intelligence and the Internet of Things (IoT), the applications of real-time analytics are vast and varied.

In this article, we’ll explore the essence of real-time data analytics and its significance in modern business operations. We’ll delve into five essential practices for maximizing its effectiveness:

1. Determining the “right time” for analytics.

2. Reengineering decisions with the appropriate combination of BI, analytics, and AI tools.

3. Sharing relevant real-time data across organizational, geographic, and system boundaries.

4. Designing systems to present essential information at critical points in time.

5. Building guardrails into both automated and human decisions.

Embracing these practices is crucial for organizations aiming to thrive in a dynamic marketplace, enhancing decision-making processes and driving transformative outcomes.

Let’s uncover the fundamental principles of real-time analytics and how they empower businesses to stay ahead in an era of relentless innovation and change.

What exactly is a ‘real-time analytics’?

Real-time analytics is the process of analyzing data as it is generated or received, enabling organizations to make immediate, data-driven decisions in response to evolving events.

Real-time analytics examples:

In an industrial facility, real-time analysis of energy consumption data helps identify peak usage periods and potential energy wastage. Instead of just processing historical data, it can analyze data in real-time, pinpoint areas where energy is being wasted, such as inefficient equipment, non-optimal production schedules or unnecessary energy consumption. Overall, real-time analytics of energy usage data empowers industrial facilities to optimize energy consumption, reduce costs, process data, enhance operational efficiency, understand customer behavior and contribute to sustainability efforts by minimizing environmental impact.

While real-time analytics are inherent to event-driven environments, the integration of real-time analytics enhances the effectiveness of decision-making processes. Hence, the adoption of best practices outlined in this research becomes imperative for organizations to harness the full potential of real-time analytics in driving business outcomes and staying competitive.

Top 5 tips for ‘Real-Time Analytics’

1. Determining the ‘right time’ for analytics

Real-time analytics offer organizations the capability to respond promptly to unfolding events or anticipated occurrences. However, the definition of “real-time” can vary, encompassing both strict engineering criteria and what is often termed as “business real-time” or “near-real-time.” In these systems, calculations are executed swiftly, with input data being at least partially fresh, often supplemented by historical data to provide context. Yet, the determination of when analytics should occur in real-time depends on the specific demands of the business context.

Determining the optimal timing for analytics requires organizations to specify the latency requirements of their systems according to their unique business situations. While some may interpret real-time analytics as fast calculations on historical data, it’s crucial to differentiate true real-time analytics from mere interactive analytics.

Deciding when to use real-time analytics isn’t always easy. It depends on many things, like how quickly you need information and what kind of decision you’re making. Collaboration among data and analytics professionals, business analysts, and decision-makers is essential to figuring out if real-time analytics is right for a situation.

The main considerations in determining the right time to analyze data revolves around the degradation rate of decision value and the potential improvement of decision quality through extended analysis. For instance, decisions involving immediate customer responses or critical resource allocation demand real-time analytics, while complex decisions may benefit from a longer decision-making process to ensure accuracy and effectiveness.

While real-time analytics typically find applications in production decisions and occasionally in management control, strategic and tactical decisions often rely on insights obtained from a combination of real-time and historical data analysis. By understanding the nuances of timing in analytics, organizations can optimize decision-making processes.

2. Reengineering decisions with the appropriate combination of BI, data analysis, and AI tools

The ability to make timely and informed decisions can be the difference between success and stagnation. With the expansion of analytics, business intelligence (BI), and artificial intelligence (AI) tools, organizations have resources to optimize decision-making processes. But how can these tools be effectively combined to drive real-time insights and actions?

Understanding Real-Time Data Analytics

While the term “real-time analytics” often brings to mind streaming data analysis, the reality is more nuanced. Almost any analytics, BI, or AI product can be utilized for near real-time analytics, provided it operates on current input data. Event stream processing (ESP) platforms, for example, are designed to continuously monitor business events, detecting patterns and triggering alerts in real-time. Additionally, predictive ML algorithms are increasingly integrated into ESP applications, enhancing their analytical capabilities.

BI and Analytics Platforms

While ESP platforms are great for watching events happen in real-time, other tools like Looker, Power BI, Qlik, and Tableau typically handle non-real-time data that may be hours, days, or weeks old. However, these tools can also be utilized for near-real-time dashboards and displays, providing users the latest information. Even familiar tools like Excel can be adapted for near-real-time operational dashboards, especially with special features for predicting and suggesting actions.

The Role of ML in Decision-Making

When we make machine learning (ML) models, it takes time because it’s a step-by-step process with lots of testing and training. But once we’ve made the model, its smart reasoning can be used in real-time decisions. This means organizations can make quick decisions based on predictions. Also, there’s a new kind of ML called adaptive machine learning, which lets models learn and improve in real-time.

Guiding Decision-Making with Models

In navigating the complex landscape of BI, analytics, and AI tools, organizations should develop decision models to guide their approach. These models help determine the most suitable combination of techniques, considering factors such as data availability, decision urgency, and desired outcomes. Many decisions may require a blend of analytical techniques and human judgment to achieve optimal results.

In conclusion, reengineering decisions with the appropriate combination of BI, analytics, and AI tools involves harnessing the strengths of each platform to enable real-time insights and actions. By using these tools effectively organizations can optimize decision-making process and gain competitive advantage.

3. Sharing relevant real-time data across organizational, geographic, and system boundaries

The ability to share real-time information and analytics across organizational boundaries is crucial for maintaining a competitive edge. However, achieving this goal requires a shift from traditional approaches to a more collaborative and integrated mindset.

Example

Traditional application systems often create information “silos” that hinder communication and collaboration between different departments. This lack of synchronization can lead to poor decision-making and negative customer experiences.

In manufacturing, outdated application systems often create communication barriers between departments, leading to inefficiencies and customer dissatisfaction. For instance, if production planning, inventory management, and quality control systems aren’t synchronized in real-time, there can be delays and errors. This can result in production running without necessary materials or despite quality issues, leading to wasted resources and delayed shipments. Real-time data sharing between these systems can prevent such issues, ensuring smooth operations, timely deliveries, and improved customer satisfaction.

How to overcome these challenges?

Organizations need to break down silos and establish a common operating picture where real-time data is shared seamlessly across departments and systems. This allows employees to make more informed decisions and avoid conflicts caused by incoherent information.

However, achieving a common operating picture doesn’t mean displaying identical information to everyone. Different users may require varying levels of detail and graphical presentations tailored to their unique decision-making needs.

By improving information sharing and enabling decisions based on predictive analytics, organizations can enhance internal operations, strengthen business processes, deliver better customer service, and increase customer satisfaction. Moreover, smarter cross-selling offers informed by real-time data stand a higher chance of success, driving overall business growth and success.

4. Designing systems to present essential information at critical points in time

In the realm of real-time operational decision-making, the abundance of data often leads to a phenomenon known as “infoglut,” where individuals feel overwhelmed by the sheer volume of information. Recognizing that human attention is a valuable resource, organizations are increasingly focused on conserving it through thoughtful system design. One strategy to achieve this is by automating decision-making processes, which not only saves time and resources but also ensures consistency and transparency. However, it’s important to keep in mind that certain decisions require human judgment due to subjective value assessments, study user behavior or unknown variables beyond the scope of algorithms.

When real-time analytics are employed to assist human decision-makers, the key lies in determining what information is essential for the decision at hand. This involves developing decision models that outline the necessary input data, decision algorithms, and potential results. Collaborating with business managers, subject matter experts, and decision-makers, analysts strive to create decision models that minimize information overload and focus on relevant data points.

In real-time decision-making scenarios, data visualization plays a crucial role in conveying information efficiently. Real-time dashboards provide continuous updates throughout the business day, offering a snapshot of operational status. Moreover, systems often use alerts to notify users of critical situations, following a “management by exception” approach. Careful design of alert thresholds is essential to avoid information overload, where users become numb to notifications, risking overlooking critical issues.

5. Building guardrails into both automated and human decisions

In the realm of real-time decision-making, it’s imperative to establish safeguards, known as guardrails, to validate the decisions made by both automated systems and human operators. Despite the rapid pace and large scale of these decision-making processes, it’s surprising how many systems lack these basic safeguards, which could significantly reduce errors.

Guardrails serve as secondary components that assess the validity of decisions generated by primary analytic systems or human judgment. These components employ various mechanisms such as range checks, circuit breakers, and rule-based scoring models to evaluate decisions before they are executed.

The purpose of these guardrails is to ensure that the decisions, whether made by humans or machines, align with logical and policy-based criteria. Human decisions are scrutinized for potential errors, compliance with policies, and susceptibility to fraud or manipulation, while machine-generated decisions are evaluated for computational accuracy and adherence to common sense.

However, even with automated secondary guardrails in place, human oversight remains essential as decision-making systems may behave irrationally without realizing it. Human monitors play a crucial role in periodically reviewing decisions, equipped with the authority to halt or shut down processes if they detect any anomalies or issues.

Conclusion

To achieve successful real-time data analysis implementation within an organization, data and analytics leaders should prioritize five key actions:

1. Determining Optimal Timing
Collaborate with business stakeholders to determine the appropriate timing for analytics, considering business conditions and requirements. Recognize that real-time may not always be the most suitable approach for decision-making.

2. Enhancing Decision-Making
Improve the precision and effectiveness of real-time decisions by leveraging a tailored mix of reporting, alerting, machine learning (ML), event stream processing, optimization, and other AI tools operating on real-time data.

3. Promoting Situation Awareness
Foster organization-wide situation awareness by facilitating the sharing of relevant real-time data across organizational, geographic, and system boundaries, both within the organization and its ecosystem.

4. Streamlining Information Delivery
Design systems that present essential information only at critical junctures, conserving decision-makers’ time and focusing their attention on pertinent details.

5. Mitigating Risks
Reduce the risk of flaws in real-time analytics solutions by implementing guardrails into both automated and human decisions. These guardrails should include secondary analytic components to verify the reasonableness of decisions before execution.

By implementing these five key strategies, organizations can unlock the full potential of real-time analytics, identify trends, enhancing decision-making processes and driving operational efficiency. Embracing these recommendations empowers data and analytics leaders to navigate the complexities of real-time analytics and stay ahead in the ever-evolving business environment.

Sources:

• 5 Essential Practices for Real-Time Analytics”, W. Roy Shulte, Pieter den Hamer, Gartner, 2023r. 
• Event stream processing for real-time analytics is documented in this book: K.M. Chandy,
• W.R. Schulte, “Event Processing: Designing IT Systems for Agile Companies,” McGraw-Hill, 2010.
• R. Wirth and J. Hipp, “CRISP-DM: Towards a Standard Process Model for Data Mining.”
• Decision modeling is further explained in the book: J. Purchase, J. Taylor, “Real-World
• Decision Modeling with DMN,” Meghan-Kiffer Press, 2016.

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Terms related to real-time analytics

Analytics and data analytics are topics that are often described using English phrases. To help readers understand the industry terms, we translate some of the most popular ones:

Batch processing

Batch analytics is a technique in which data is collected in large quantities and processed as a single batch over a period of time. For example, batch analytics may involve processing bank transactions at the end of the day or generating reports based on the collected data. Unlike real-time analysis, batch processing takes place with a delay, which is acceptable in many applications where an immediate result is not critical.

Data warehouses

Data warehousing is a central data repository that is used to store and manage large amounts of data sources. Customer data in a data warehouse is usually organised in a way that allows for easy reporting and analysis. The data warehouse is designed to support the company’s decision-making processes by allowing quick access to consolidated information.

Hadoop Map-Reduce

Hadoop Map-Reduce is a programming model and associated implementation for processing large data sets in a distributed computing environment. The Map-Reduce model consists of two main stages: Map, where the input data is split into small chunks and processed in parallel, and Reduce, where the mapping results are aggregated and combined to produce the final result. Hadoop is an open source platform that implements this model and is widely used for big data analytics.

ETL (extract, transform, load)

ETL is a process that involves three steps: extracting data from various sources, transforming this data into the appropriate format and structure, and loading the transformed data into a target system such as a data warehouse. ETL is a key element in data integration, enabling the consolidation and harmonisation of data from different sources for further analysis and reporting.

Stream processing

Stream processing or stream analysis is a real-time data analysis technique that involves the continuous processing of incoming data streams. Unlike batch analytics, where data is collected and processed one at a time, stream processing allows data to be analysed as it arrives.

Keywords: real-time data analytics, data analytics, business intelligence, articifial intelligence, real-time data processing, big data, real-time data streams