Traditional data analytics has been revamped; this is the age of augmented analytics. Organizations are now making assured, confident decisions without relying on specialized data science skills or IT support.
Ronald Van Loon, an SAP Partner, attended SAP TechEd Barcelona. He met experts like Orla Cullen and Richard J. Mooney and discussed their viewpoints and expertise on various subjects, including augmented analytics.
The entry of augmented analytics into the market has facilitated companies in applying Artificial Intelligence (AI) and Machine Learning (ML) to empower employees across the organization. In this way, they can:
⦁ Acquire easy-to-understand and detailed insights. Unlike traditional insights, with ML in analytics, they are able to tap into the “context.”
⦁ Observe patterns and trends.
⦁ Take advantage of one of the greatest gifts of augmented analytics: data democratization – allowing information to be accessible to the average non-technical end user.
⦁ Leverage AI techniques in transforming the content of analytics in terms of development, consumption, and sharing.
Analytics has shed its conventional roots and evolved to become intertwined with predictive, conversational, and automated capabilities. Businesses need an analytics solution that can meet the following requirements:
⦁ Facilitate every user to benefit from analytics.
⦁ Accelerate the insight-development phase to enhance business processes and their corresponding outcomes.
⦁ Take out the human bias from the equation during the unraveling and generation of new insights.
⦁ Speculate and forecast the future.
What Is Augmented Analytics?
According to Gartner – leading research and advisory firm, augmented analytics refers to:
“A next-generation data and analytics paradigm that uses machine learning to automate data preparation, insight discovery and insight sharing for a broad range of business users, operational workers and citizen data scientists.”
Augmented Analytics incorporates AI, machine learning, and business intelligence into existing workflows. It uses NLP (natural language processing), NLQ (natural language querying), and NLG (natural language generation) to enabled users to gain access to conversational analytics and user-friendly queries. The combination of machine learning and automation offers a view with one-click answers.
Augmented analytics is implemented in all kinds of industries, including sales and marketing, finance, HR, and accounting.
Augmented analytics provides a new user experience. Previous analytics solutions were limited to pre-defined KPI’s and required greater manual intervention. On the other hand, augmented analytics uncovers insights that are dynamic in nature for assessing content and figuring out the “important” parts from statistical information. The context here taps into user data such as what role they hold, what data they used to find in the past, and the data searched by their respective networks.
Augmented Analytics for All Business Users
The primary point of augmented analytics is to ensure that regular business users can benefit from it. Businesses no longer have to rely on someone with a data-related degree to make sense out of their analytics.
Benefits
⦁ Get more time to make confident decisions by optimizing data collection and display designs.
⦁ Exploit NLP to access critical information smoothly.
⦁ Use machine learning to present previously hidden insights automatically.
⦁ Equip analysts with correct and easy-to-use what-if simulation modeling solutions.
⦁ Seek contextual recommendations to assist with future actions.
In a bid to boost analytics using AI, business analysts dig into machine learning via familiar business intelligence (BI) and planning workflows. It discloses new features steadily. The contextual nature of the analytics facilitates users to receive broader and more profound insights in only a few clicks. In this way, they break free from depending on data scientists – for interpretation, and IT – for data collection.
As human involvement is minimized, this ensures that human bias is diminished too. Hence, augmented analytics promotes a decision-making culture that is solely based on facts and figures.
The use of SAP Analytics Cloud ensures that clients no longer work with cryptic queries to build the pathways for transmitting insights. With conversational queries, all you have to do is ask questions similar to how you do with a colleague, simplifying your search and analysis. In response, the system would grasp the subject like your experienced colleagues, and provide relevant, contextual explanations and visualizations – all of this is created in real time.
Popular Use Cases
*AG Real Estate
**PWC Reporting 5.0
Search to Insight
If a user requires an insight, then they can simply enter a general question on the subject matter, without specifying any dimensions or measures. This query can generate real-time information for connected trends and data relationships.
Smart Insights
In this stage, you can explore your data in more detail. Similarly, you can uncover hidden trends and patterns that were previously overlooked.
Smart Discovery
Smart Discovery allows users to examine relationships between variables. You can figure out the key influencers that boost your KPI’s and learn from them.
Smart Predict
Prediction is the final stage of the augmented analytics’ journey. It uses machine learning to learn from historical datasets and predicts future events.
Final Thoughts
Augmented analytics has reshaped how decision-makers take action. AI and ML eliminate human bias and speed up the pace of BI and planning workflows, so decision-makers take fast, assured actions from reliable information.
It is imperative that organizations adjust the perspectives of their work culture for decision-making, or else they cannot progress with modern-day digital transformation. They have to reduce their heavy dependence on data analysts, data scientists, and IT professionals. In the age of data, they are in severe need of quick and trusted insights.
Credit: Data Science Central By: Ronald van Loon
