In today’s business landscape, organisations are increasingly seeking quicker and more meaningful insights from their data to drive informed decision making. The rise of augmented analytics, powered by artificial intelligence and machine learning, is revolutionising the way businesses generate, consume, and share business intelligence and analytics. This blog explores the concept of augmented analytics, its key components, benefits, and challenges, and how it is reshaping the business landscape.
Understanding Augmented Analytics
Traditionally, data analytics required specialised expertise and software, making it inaccessible to many business users. Augmented analytics leverages AI and related technologies to bridge this gap, enabling business users to generate deeper insights faster. It encompasses three key components: machine learning, natural language technologies, and automation.
Machine Learning
ML algorithms rapidly analyse vast volumes of historical data, identify patterns, detect anomalies, and generate insights and recommendations. These algorithms continuously learn from new data without human intervention, enabling the automation of data analysis and prediction processes.
Natural Language Technologies
Natural language processing and natural language generation facilitate human-computer interactions. NLP allows users to engage with machines in natural language, while NLG translates computer-generated results into human-readable language. This enables business users to have conversational interactions with the analytics system, making data analysis more intuitive and accessible.
Automation
Augmented analytics automates manual tasks throughout the data analytics lifecycle. It streamlines data preparation, accelerates model building and deployment, and automates the creation and distribution of reports. Automation saves time, increases productivity, and allows users to focus on deriving insights rather than performing repetitive tasks.
Benefits of Augmented Analytics
- Improved Decision Making: Augmented analytics empowers business users to take ownership of data analytics and generate actionable insights. Customised data dashboards, reports, and data storytelling techniques make complex data more comprehensible, enabling informed decision making based on data-driven evidence.
- Data Democratisation: Augmented analytics fosters data literacy across departments by involving more people in data analytics. As business teams become comfortable working with data and collaborating, the organisational culture shifts, leading to increased innovation and value creation.
- Faster Data Preparation: Augmented data preparation techniques guide users in selecting, consolidating, cleaning, and formatting data sets. This streamlines the process of building ML models and reduces the time required for data preparation, enabling faster insights generation.
- Reduced Analytical Bias: ML algorithms minimise bias by analysing large volumes of data and identifying patterns objectively. Automated workflows also reduce manual errors, ensuring more accurate and trustworthy results.
- Time and Cost Savings: Automation of manual processes frees up data science teams to focus on more complex tasks. Increased data literacy among business users enables them to take on simpler analytics projects independently, further optimising resource allocation.
Challenges of Augmented Analytics
While augmented analytics offers significant benefits, organisations should be prepared to address certain challenges when using the technology as well.
- Misconceptions of AI: Some employees may fear that AI technologies will replace them. Transparent communication and education are key to helping them understand that augmented analytics enhances human capabilities and requires domain expertise. Rather than human roles being replaced by AI, it is more helpful to think of AI as a tool to be leveraged in order make processes easier and more efficient.
- Poor Data Literacy: Providing training workshops and mentors can help improve data literacy among business teams. Teaching key data concepts and promoting successful augmented analytics projects will instill confidence and encourage wider adoption.
- Ineffective Data and Model Management: High-quality data and regularly updated ML models are essential to generate reliable insights. Organisations must invest in robust data management practices and ensure the accuracy and relevancy of data assets.
- Irrelevant Results: Users need guidance on generating meaningful insights that align with their roles and responsibilities.
Use Case of Augmented Analytics
Augmented analytics in Power BI provides users with advanced capabilities to explore, analyse, and gain insights from their data. Here’s a closer look at how Power BI incorporates augmented analytics.
Natural Language Query
Power BI allows users to ask questions about their data using natural language queries. With the Q&A feature, users can type or speak questions in plain language, and Power BI generates visualisations and answers based on the data available. This capability makes data exploration more intuitive and accessible to a broader range of users, regardless of their technical expertise.
Automated Insights
Power BI’s automated insights feature, called Quick Insights, uses AI and ML algorithms to analyse data and automatically identify patterns, correlations, and outliers. With a single click, users can apply ‘Quick Insights’ to a selected dataset or visual to generate a set of pre-built visualisations and key findings. This saves time and helps users discover hidden insights within their data without manual exploration.
Smart Data Preparation
Data preparation is a critical step in the analytics process, and Power BI offers intelligent data preparation capabilities. The platform uses AI to analyse and profile the data, identify potential data quality issues, and suggest data cleansing and transformation steps. This smart data preparation feature assists users in cleaning, transforming, and shaping their data for analysis more efficiently.
Smart Data Visualisation
Power BI incorporates smart data visualisation capabilities to enhance data exploration and presentation. The platform automatically selects appropriate visualisations based on the data type, distribution, and context. Additionally, Power BI provides smart recommendations for additional visuals that could enhance the understanding of the data. This helps users create compelling and insightful visualisations without extensive manual configuration.
AI-powered Analytics Functions
Power BI includes a wide range of AI-powered analytics functions that users can leverage to perform advanced calculations and analysis on their data. These functions include forecasting, clustering, sentiment analysis, key driver analysis, and more. Users can apply these functions to uncover trends, make predictions, and gain deeper insights from their data.
By integrating augmented analytics capabilities, Power BI empowers users to explore data, generate automated insights, and make data-driven decisions more effectively. The AI and ML algorithms within Power BI assist users in understanding their data, uncovering patterns, and presenting insights in a user-friendly and intuitive manner, enabling users at all levels of technical expertise to derive value from their data.
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