6 Ways Businesses Use Augmented Analytics for Better Decision-Making

image of a man's torso with his arm outstretched with its palm facing up with a floating blue translucent orb hovering above it to symbolize augmented analytics

In today’s data-rich business environment, the ability to efficiently collect, analyze, and interpret information is crucial for effective decision-making.

Augmented analytics is gaining importance in the business landscape as companies recognize the need for faster and more accurate insights.

To fully leverage augmented analytics’s potential, take some time to explore its definition, the benefits of implementing it, and the roles needed to utilize this transformative technology.

What is augmented analytics?

Augmented analytics integrates artificial intelligence (AI), machine learning (ML), and natural language processing (NLP) to enhance data analysis and insight generation.

By automating data preparation and analysis processes, it accelerates the time to actionable insights, empowering business users with valuable information for better decision-making.

6 benefits of augmented analytics

Benefits of augmented analytics include simplified data discovery and visualization, enhanced data governance, and automated reporting and insights generation.

1. Simplified data discovery and visualization

Augmented analytics tools simplify data discovery by automating the analytics process, removing the need for manual data manipulation and analysis.

Through intuitive visualization features, users can quickly uncover trends, patterns, and correlations.

This capability enables business teams to generate meaningful insights, facilitating more effective strategic planning.

2. Enhanced data governance

Augmented analytics strengthens data governance by ensuring data security, compliance, and integrity.

Automated analytics workflows enforce data policies, substantially reducing the risk of data breaches while building trust in the analytics process.

This fosters an environment where accurate insights enhance decision-making, providing a solid foundation for business strategies.

This safeguards the integrity of business operations and ensures that decisions are based on the most reliable data.

3. Personalized recommendations

AI-powered analytics provides tailored recommendations for various departments based on predictive analytics.

For instance, marketing teams can refine their marketing campaigns through data-driven insights, and customer service teams can optimize their approach based on customer behaviors.

These personalized recommendations improve the customer experience and lead to more informed decisions.

4. Predictive and prescriptive analytics

Predictive analytics forecasts future outcomes by analyzing historical data, while prescriptive analytics recommends specific actions to achieve desired business goals.

For example, demand forecasting helps businesses optimize their inventory, while predictive maintenance ensures efficient supply chain management.

These analytics capabilities guide business operations toward achieving real business outcomes.

5. Automated reporting and insights generation

Natural Language Generation (NLG) and conversational analytics allow for automated reporting and insight generation.

These features reduce the workload of manual data analysis, providing faster and more accurate predictions.

Automated reports enable business leaders to make better, real-time decisions without delays.

6. Faster identification of business opportunities

Augmented analytics helps companies identify emerging market trends and untapped business opportunities by analyzing massive datasets using AI-driven techniques.

For example, customer segmentation can reveal new segments to target, while pricing strategies can be optimized using predictive insights leading to faster identification of opportunities offering a competitive edge.

Roles needed to leverage augmented analytics

Roles needed to leverage augmented analytics include data scientists/analysts, machine learning engineers, business intelligence developers, data governance specialists, and domain experts.

1. Data scientists/analysts

Data scientists and analysts play a pivotal role in leveraging augmented analytics, as they build predictive models, analyze vast amounts of data, and interpret insights for strategic planning purposes.

Their expertise in machine learning algorithms and predictive analytics is crucial in guiding marketing teams and other departments to achieve their objectives.

Through their analytical skills, they transform raw data into actionable strategies that drive the business forward.

2. Machine learning engineers

Machine learning engineers are instrumental in designing and maintaining the algorithms that refine insights from data.

Their expertise is crucial in creating accurate predictive models and in the development of new analytics features that continuously enhance decision-making processes.

By applying their deep understanding of both data science and software engineering, they ensure that augmented analytics platforms remain at the forefront of technological innovation.

3. Business intelligence developers

Business intelligence developers specialize in creating and optimizing data dashboards and reports, utilizing analytics platforms such as Qlik Cloud Analytics to streamline the analytics process.

They play a crucial role in ensuring that insights are visualized in an intuitive and accessible manner, supporting faster insights and quicker and more effective decision-making.

Their expertise helps businesses translate complex datasets into clear, actionable insights that can drive strategy and performance.

4. Data governance specialists

Data governance specialists are pivotal in maintaining data integrity, ensuring compliance, and developing a robust data governance framework.

They implement and enforce data policies that align with overarching business goals, safeguarding sensitive information and mitigating security risks.

Their role is crucial in establishing a secure data environment that supports informed decision-making and operational efficiency.

5. Domain experts

Domain experts serve as the bridge between technical data analytics teams and the strategic business units they support.

They ensure that predictive insights and analytics strategies are tailored to meet the unique needs of their respective departments, from marketing to finance.

Their deep understanding of domain-specific challenges and opportunities ensures that analytics solutions are relevant and impactful, driving targeted business outcomes.

The future of augmented analytics for business intelligence (BI)

Augmented analytics provides businesses with unprecedented opportunities for data-driven discovery and business intelligence.

Having the right roles and technologies will be crucial to fully leverage its potential.

Companies should invest in augmented analytics tools and skilled professionals to gain a competitive edge in an evolving business environment.

By embracing this transformative technology, organizations can achieve meaningful insights and valuable outcomes.

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