As the complexity and volume of data continue growing, companies are implementing ever-newer Business Intelligence (BI) and analytics principles to improve their data efforts and, thus, leave the competition far behind.

One such methodology gaining popularity is Augmented Analytics (AA): leveraging the power of Machine Learning (ML), Artificial Intelligence (AI), and Natural Language Processing (NLP) to gain critical insights faster and more precisely.

In this article, we’ll uncover the answers to the following questions:

• What is Augmented Analytics, and how does it work?
• What are the benefits and challenges of Augmented Analytics?
• How is Augmented Analytics used in organizations today?

Ready? Let’s dive in.

Augmented Analytics is a fresh concept, first defined by Gartner in 2017 as

“the use of enabling technologies such as machine learning and AI to assist with data preparation, insight generation and insight explanation to augment how people explore and analyze data in analytics and BI platforms. It also augments the expert and citizen data scientists by automating many aspects of data science, machine learning, and AI model development, management and deployment.”

In a nutshell, augmented analytics allows us to get results faster with fewer headaches. It does so by automating tasks which in turn allows for quick decision-making and improved accuracy in predictions. Furthermore, augmented analytics can help non-technical users to benefit from more sophisticated tools in the data analysis process.

Before we go too deep into the technicalities, it is important to understand that AA is not the same as Data Science (DS), which focuses on utilizing technologies such as ML, AI, and others correctly. On the other hand, AA uses automated algorithms and ML to augment human intelligence in the data analysis process, allowing users to easily access and analyze large volumes of text data. With that in mind, AA methodologies can be tailored to meet a wide range of business needs and goals.

To help you better understand how Augmented Analytics works, we will take a detailed look at one of the possible uses of AA: the augmented data cleaning process. By breaking down the methodology into smaller steps, we can test different approaches along the way, following an efficient and solution-oriented process.  

While there are endless possibilities where this approach might come in handy, consider a real estate company looking to better understand market trends through their rent and sales data. The goal of utilizing AA here is to improve the quality of their rent and sales data to prepare for a more effective analysis afterward. AA can help us speed up and simplify this process.

As you can see, it is important to follow a systematic process to utilize Augmented Analytics within a programming language like Python effectively.

Here is our step-by-step guide on how to approach a data set like in the real estate example:

1. Data acquisition: Retrieve data from its source through scraping, downloading, or importing.

2. Tool alignment: Import all necessary libraries.

3. Data discovery: Explore the raw data using a custom or pre-existing method of your choice.

4. AA deployment during data cleaning:

Human experiences:

• Familiarize yourself with the data description  to understand the raw data, its context, availability, and appropriate use.
• Remove special symbols and formatting errors.
• Review duplicates and decide on deletion.
• Eliminate any irrelevant and unnecessary data.
• Standardize and unify the data.

Artificial experiences:

• Apply ML, AI, and NLP approaches depending on business goals and needs. 
• Use artificial tools to automate human experiences, where possible.
• Eliminate errors.
• Handle missing values using ML.
• Use NLP to analyze language, correct grammar, convert speech to text, and to translate between languages.
• Review and compare results.

As it combines the capabilities of Business Intelligence and the advanced features of Artificial Intelligence, Augmented Analytics offers new opportunities for organizations seeking to bring their business processes to the next level. But how?

• Increased data quality: automated analysis of billions of records reduces the risk of bias, the number of errors, and inaccurate information, therefore increasing the quality of strategies, plans, and tactical decisions.
• Faster insights: real-time processing streamlines the immediate flow of information to the end user, allowing them to drive business decision-making.
• Improved efficiency: automating repetitive tasks helps companies implement more efficient processes within the product’s life cycle, saving time and money.

Global powerhouses like Coca-cola, Crédit Mutuel, Ryanair, and WhatsApp, have already begun incorporating Augmented Analytics into their internal operations. And the trend is only growing, with leading analytical BI tools, such as Power BI and Tableau, also embracing this approach.

Moreover, many new cutting-edge products from innovators like AnswerRocket, The Analytics OS (Pyramid v2020), Qlik Sense, TIBCO Spotfire, QBO, and Sisy have made analytical work faster and more efficient.

Augmented Analytics is changing almost all industries. For instance, finance structures, such as banks and services, have adopted this methodology to increase fraud detection and reduce risks by allowing continuous auditing and monitoring. Some marketing departments have tapped into AA to optimize campaigns and customer loyalty policies.

The automated data storytelling that AA offers is highly valued among manufacturing companies. Because the manufacturing sector is exposed to many vulnerabilities, simplifying complex information in seconds has become crucial:  implementing AI algorithms is the answer. Dashboards like those offered by us at dyvenia in Power BI showcase the data story in an easy-to-understand visual format, offering valuable insights and streamlining operations.

Even though Augmented Analytics is changing the analytics and BI space, enabling companies to gain actionable insights faster than ever before, organizations should prepare for the obstacles this methodology may bring:

• Timelines: The denser the data to analyze, the more time it will take to integrate the different devices using and reporting the data.
• Interpretation: AA requires interpreting the patterns of human language and expressions into a language the computer can understand; likewise, AA needs to convert recognized patterns into actionable insights via language understood by users on an intuitive and immediate level.
• Privacy: Safety of personal data and policy compliance with both organizational and legal requirements.

The potential gains from AA are so attractive that some companies tend to jump into it without a clear vision and sensible objectives. Before falling head over heels for AA, you first need to build the foundation to get the most out of Augmented Analytics. Establishing a solid data foundation will help you:

• Integrate your applications to get a unified view of data.
• Improve data experience across the enterprise to build trust in data.
• Introduce data governance principles and policies to ensure that privacy concerns are properly addressed.

By housing all needed data capabilities, dyvenia helps organizations to build efficient, expandable, and future-proof data foundations that allow for the seamless creation of scalable AI/ML and BI projects.

Seeking to harness the power of your data? Reach out to our team to see how we can support you in your data journey.

Although Augmented Analytics comes with challenges, its implementation has proven to be valuable for a wide range of industries, including finance, marketing, and manufacturing. By combining BI and analytics capabilities with technologies such as AI, ML, and NLP, business decision-makers can gain critical insights faster, increase the quality of the obtained data, and significantly improve efficiency by automating redundant processes.