Traditional business intelligence (BI) is no longer effective. Born in a siloed world, BI tools were driven by IT departments and lacked adaptability to dynamic data. With the evolution of data engineering and the rise of pluggable data architectures, the traditional purpose of BI solutions is now obsolete. Today, data is dynamic, requiring real-time insights delivered at the point of action. 

The highly competitive markets of today demand hyper-personalized user experiences and interactions. Static reports and dashboards are no longer sufficient; automation is essential to keep pace with the speed of business. Analytics has also evolved, reshaping how organizations leverage data to drive decision-making. Traditional analytics primarily focused on providing insights into past events. However, with technological advancements, predictive analytics, which anticipates future outcomes based on historical data, has taken center stage. This shift reflects the growing demand for proactive decision-making to stay ahead in competitive markets.

Embedded analytics has led to fragmented user experiences and limited insights. Contextual analytics represents the next phase of embedded analytics, offering guided insights directly within the application interface. By providing contextualized insights, it enhances efficiency and facilitates decision-making without the need to switch between different modules or contexts. This evolution addresses longstanding limitations, such as manual data analysis and inconsistent dashboard utilization.

Contextual analytics has transformative potential in improving user experience and driving business success. 

Understanding contextual analytics

Data is at the center of making informed decisions that lead to real outcomes. It is no longer just numbers; it is actionable insights that add real value. Driven by factors such as customer behavior and advances in data management, these insights are essential for staying ahead of the market. We have seen a sea change in how businesses manage data, with security as a top priority and practical application as a by-product. This means delivering easy-to-understand insights, solving critical business questions and helping decision-making where it matters most. But here is the problem: traditional data analytics tools often demand users to step away from their core tasks just to access insights. 

Automation allows businesses to access these insights quickly and customize them to meet their specific needs, keeping them agile and competitive in a fast-paced environment. Contextual analytics seamlessly integrates analytics right into your software's everyday workflows. By blending analytics with transactions, contextual analytics turns your application into a dynamic decision-making hub. Whether you are streamlining processes, spotting revenue opportunities or managing risks, contextual analytics gives you the tools you need to tackle the complexities of the business world with confidence.

The use cases

The integration of contextual information represents a significant leap forward in analytics capabilities. Rather than treating data and analytics as separate entities, these tools tap into contextual knowledge embedded within enterprise systems such as ERP and CRM. This shift enables more informed decision-making by delivering relevant insights tailored to specific contexts.

What makes these new tools stand out is their ability to automate the discovery of insights, reducing the need for human expertise. Augmented analytics, which encompasses predictive analytics, machine learning and natural language processing, empowers users to effortlessly derive actionable insights. By automating data preparation, analysis and interpretation, these tools democratize analytics, allowing non-experts to make data-driven decisions.

Consider the impact across various domains:

• In human resources, contextual analytics streamline candidate selection processes by aligning skill sets with job requirements, thus optimizing recruitment efforts
• In supply chain management, contextual analytics utilize ERP data to optimize inventory levels and predict fulfilment needs, thereby enhancing operational efficiency
• In retail, it enables store managers to access daily demand forecasts instantly and provides them with the option to create shift schedules for the upcoming week, ensuring optimal staffing levels
• It helps inventory managers analyze trends, monitor stock levels and replenish orders without having to switch screens, driving efficiency
• Processes involving routine data processing, whether in logistics, finance, manufacturing or sales, can benefit from on-the-fly alerts, prompts and insights, facilitating smoother operations and informed decision-making

The benefits

The implementation of contextual analytics has yielded tangible benefits for organizations across industries. Existing success stories underscore the transformative potential of these technologies in driving business outcomes. 

As leading vendors continue to roll out augmented analytics solutions, the landscape of enterprise analytics is poised for significant disruption. These tools promise to democratize data analytics, empowering organizations of all sizes to derive actionable insights and stay competitive in an increasingly data-driven world. 

Some of the reasons why updating your software with contextual analytics can unlock new avenues of decision-making and analytical value are as follows:

• Optimized user experience: Contextual analytics integrates critical metrics into your software, enhancing both the analytical and user experience. Users can access relevant information instantly while working, reducing data fatigue and human error and allowing them to focus on other critical tasks
• Unique analytical experiences: Contextual analytics enables professionals to deliver intuitive insights, such as in-line charts and customized dashboards, enhancing the user experience. Advanced features like automated business monitoring and AI-assisted explanations make for highly differentiated product offerings
• White-labelled solution with fast speed-to-market: Contrary to common misconceptions, implementing contextual analytics is straightforward and does not require a complete overhaul of your software. Modern analytics solutions offer easy deployment, allowing professionals to focus on their core tasks
• Increased revenue opportunities: Contextual analytics drives customers towards higher-priced packages by offering dynamic insights and AI-assisted features. This increases the average contract value and helps sales teams win more deals, ultimately boosting revenue

Navigating the challenges

Despite the progress, leveraging data and AI effectively remains a challenge for many organizations. Predictive models require extensive historical data and specialized expertise, limiting their applicability. Moreover, empowering users with descriptive analytics often entails significant time investments, hindering widespread adoption. 

One of the shortcomings of existing analytics approaches is their focus on specific functions or units within organizations. This siloed approach overlooks the interconnected nature of business processes, limiting the in-depth understanding of complex issues. Integrating contextual information into analytics models has been challenging due to data silos and poor data cataloging practices, further impeding insightful analysis. 

However, there is an emerging wave of enterprise analytics, driven by automation and contextual intelligence. Fueled by AI and automated processes, these innovative solutions are breaking down the barrier between data and decision-making, providing timely insights directly to decision-makers with minimal analyst involvement.

HCLTech Advantage Analytics

HCLTech Advantage Analytics is a multi-dimensional solution designed to bring the needs of any data-driven enterprise within a framework that responds to the current technological trends. It helps you take your business to the next level in today’s data-driven environment. The solution includes six key offerings: 

Insights-Modernize: Simplifies the process of modernizing your analytics platform by automating the process of migration from your legacy BI systems to your modern analytics platform, with a no-code approach.

Insights-Agent: A conversational platform that uses natural language processing (NLP) and artificial intelligence (AI) to enable personalized customer interactions across different channels.

Insights-Propeller: Provides data-driven insights directly to business users at the point of action, helping enterprises make smarter decisions.

Insights-APIs: Platform-agnostic solution that empowers the entire insights value chain. Supports easy movement from data to insights with just a few API calls. 

Insights-Embed: Injects interactive visualizations, predictive analytics, and more into business applications. 

Insights-Stories: Storytelling helps organizations contextualize their data, making it easier to understand and act on.

Benefits include:

• Maximized value of existing investments 
• Expanded analytics capabilities leveraging your existing toolset
• Reusable, customized solutions and high-quality insights at a lower cost
• Reliable delivery based on our extensive experience in building analytics platforms
• Swift insights delivery through customizable automation tools

The way ahead 

Contextual analytics is not just about data and analysis. It is about the story and context around the data. Contextual analysis looks at different aspects of the data, such as customer, industry and competitor perspectives. It also looks at social, policy and economic factors that shape the outcome. 

The emergence of contextual analytics is a game-changer for enterprise decision-making. Automation and contextual intelligence can unlock unique insights from your data, driving innovation and growth. Taking advantage of these transformative technologies opens a whole new world of data-driven decision-making.