With increasing expectations for decision makers to have access to insightful data and act on it immediately, familiarity with Augmented Analytics has become essential. In this article, we introduce Augmented Analytics and present a few examples to illustrate its capabilities.
As the complexity and volume of data continues to grow, companies keep implementing new Business Intelligence (BI) and analytics principles to improve their data efforts in order to stay ahead of the competition..
Augmented Analytics (AA) is one such methodology gaining in popularity: it leverages 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 answer the following questions:
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 and with fewer headaches. It automates tasks which allows for quick decision-making and improved accuracy in predictions. Augmented analytics can also help non-technical users to benefit from more sophisticated tools in the data analysis process.
Before going into the technicalities, we will point out that AA is not the same as Data Science (DS), which focuses on the correct use of technologies such as ML, AI, and others. Instead, 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. AA methodologies can be tailored to meet a wide range of business needs and goals.
To better clarify how Augmented Analytics works, we’ll take a detailed look at one of its possible uses: 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 in which this approach might come in handy, consider a real estate company looking to better understand market trends through their rent and sales data. Using AA here would improve the quality of their rent and sales data, allowing for more effective analysis afterward.
It’s important to follow a systematic process in order to effectively use Augmented Analytics within a programming language such as Python .
Here is our step-by-step guide to approaching a data set such as 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:
By combining 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?
Global powerhouses including Coca-cola, Crédit Mutuel, Ryanair, and WhatsApp have already begun incorporating Augmented Analytics into their internal operations. And the trend is growing, with leading analytical BI tools such as Power BI and Tableau also embracing this approach.
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, banks and other finance 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 is now crucial, and 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 streamlined operations.
Although Augmented Analytics is changing the analytics and BI space and enabling companies to gain actionable insights faster than ever before, organizations should prepare for any obstacles this methodology may bring:
The potential gains from AA are so attractive that some companies tend to jump in 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 it. Establishing a solid data foundation will help you:
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.