Businesses are constantly seeking innovative ways to extract meaningful insights from their vast amounts of data. Traditional approaches to data analysis and decision-making are no longer sufficient to keep up with the ever-increasing complexity and volume of information. This is where augmented analytics comes into play, revolutionising the way businesses derive value from their data. In this blog, we will dive into the realm of augmented analytics in Power BI and explore how it empowers organisations to make data-driven decisions.
What is augmented analytics?
Augmented analytics refers to the integration of artificial intelligence (AI) and machine learning (ML) techniques into the analytics process. It leverages advanced algorithms and automation tools to augment human intelligence, enabling users to discover insights, generate reports, and make informed decisions based on data-driven evidence.
The four key components of augmented analytics:
Natural Language Processing (NLP):
Augmented analytics platforms incorporate NLP capabilities to understand and process human language. This allows users to interact with the data using natural language queries, making it accessible to individuals without a background in data analysis.
Data Preparation and Integration:
Traditional data preparation processes are time-consuming and often involve manual efforts. Augmented analytics automates data cleansing, transformation, and integration tasks, enabling faster and more efficient data preparation for analysis.
Data Discovery and Visualisation:
Augmented analytics tools employ advanced algorithms to automatically analyse data sets, identify patterns, and generate visualisations. These visualisations make it easier for users to interpret complex information and gain actionable insights.
Advanced Analytics and Predictive Modelling:
With augmented analytics, businesses can leverage sophisticated statistical and machine learning techniques without requiring deep expertise in data science. These tools enable organisations to build predictive models, forecast trends, and make data-driven predictions.
Augmented analytics is incorporated into Power BI dashboards via the Q&A visual.
It comes out of the box and you don’t need to purchase other services. Depending on how much data is in the report, Power BI will have a few suggestions to start with.
Clicking on the Q&A set-up is where you can teach Power BI to understand questions better and refine results. The more data you put in, the more it will develop its machine learning.
Teach Q&A allows you to define terms more precisely. For instance, the question (which can be your own business terminology) can be linked to an opportunity created in a region
Managed terms can be deleted if what you have taught is no longer useful.
Benefits of augmented analytics in Power BI:
- The democratisation of data: It eliminates the dependency on data specialists by empowering users to perform complex analysis independently. It enables individuals across the organisation to explore data, gain insights, and make informed decisions.
- Improved efficiency and productivity: Accelerating the analytics lifecycle. It reduces manual efforts in data preparation, analysis, and report generation, freeing up valuable time for users to focus on higher-value tasks.
- Enhanced decision-making: Providing users with actionable insights by uncovering hidden patterns and trends in data. It enables organisations to make data-driven decisions quickly and confidently.
- Proactive insights: It will actively monitor data streams in real-time, allowing businesses to detect anomalies, identify emerging trends, and take proactive measures. It helps organisations to respond swiftly to changing market dynamics.
- Scalability and agility: The platforms can handle large volumes of data and scale seamlessly as the organisation’s data needs grow. They provide flexibility and adaptability to evolving business requirements, ensuring continued value extraction from data assets.
Augmented analytics represents a paradigm shift in the way organisations leverage their data. By combining AI, ML, and automation, businesses can unlock the true potential of their data assets.
Augmented analytics democratises data, enhances decision-making, and empowers users to explore insights independently. As organisations embrace this transformative approach, they will be well-positioned to thrive in the era of data-driven decision-making.