Perhaps your business is considering an augmented analytics solution, or your enterprise already has some version of business intelligence or analytics and it wishes to upgrade or transition to a more beneficial solution. Maybe you just want to understand the analytics solution market better. Whatever the case, this guide will help you gather information on the topic of augmented analytics and, if you are looking for a solution, it will help you organize your thoughts and include the critical considerations and factors to ensure project success.
Before we dive into the details, let’s start with a definition.
What Is Augmented Analytics?
Gartner defines augmented analytics in this way:
“Augmented analytics is 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.”
If you are not already familiar with the term “citizen data scientist,” you might want a definition of that term as well. Citizen data scientists are business users who enjoy data democratization and improved data literacy by adopting analytics tools that are easy to use and allow users with average technical skills to participate in sophisticated analytics and to use that analysis to make day-to-day decisions.
As Springboard notes, “Augmented analytics is an example of human-machine interaction in the data science field.”
The augmented analytics solution is typically supported by natural language processing (NLP) to support users in the query and results process. As with Google and other NLP interfaces, augmented analytics allows the user to ask a question using simple human language and receive results in the same way, so the business user does not have to write code, create complex SQL queries or wait for the in-house IT team or data scientist team to create reports.
The augmented analytics search engine allows users to factor in several variables, e.g., a list of sales team members, a time period or range, a category of products or items, etc. So, you can ask, “Who sold the most bakery items in the Southwest region from September through November of 2022?” and you will receive your results and can visualize your data in a way that makes sense to you and your team.
How Has Analytics Evolved?
As with any other software or technology evolution, the analytics solution market has changed. As new technologies and techniques emerge, the wise analytics solution provider will leverage these new technologies to improve usability and create new features and functionalities.
The Inception of Business Intelligence (BI) Tools: Historically, the business intelligence solution market was limited to systems and solutions to support data scientists and IT teams. These systems required users to create SQL queries and/or write code to gather and analyze data from multiple systems across the enterprise. In the early days, there was no one solution for data scientists and IT teams to use. Rather, they wrote queries to pull information and derive data from systems without a simple interface.
The Inception of Self-Serve Analytics: When the self-serve analytics market emerged, the traditional business intelligence (BI) tools supported business users with features like key performance indicators (KPIs) to establish and measure results using objective metrics, and with flexible report formats that enabled data visualization in a way that was meaningful to users. What is self-service analytics? We should probably explain before we move on.
Here is a brief definition of self-service analytics: Self-serve analytics is a solution designed to support business users who do not have data science or IT skills. When these tools were introduced to the market, they provided support for the first iteration of a citizen data scientist approach.
Modern BI and Analytics: The evolution of business intelligence tools and analytics approaches saw the introduction of specific tools and features, designed for business users in a self-serve environment. The question “What is self-service analytics?” varied among enterprises, with some businesses limiting the use of these tools to power users and those team members who had a natural curiosity about analytics.
Augmented Analytics Explained: As self-serve augmented analytics began to evolve, new tools and techniques were added, and business users now enjoy the expanded capabilities of predictive analytics. Augmented analytics vs. predictive analytics is not really a question. The real question is how do you integrate predictive analytics into the world of the business user to allow them to test theories, prototype, and use sophisticated algorithms and analytical techniques in an environment that can still be considered “self-serve” and “user-friendly”? With the right self-serve solution, users can leverage techniques like time series forecasting (Holt-Winters, ARIMA and ARIMAX), Regression techniques (simple and multiple linear regression), Classification (naïve-bayes, decision tree, K-nearest neighbor, binary logistic regression), Association including frequent pattern mining, Correlation including Spearman and Karl Pearson, Clustering including K Means and hierarchical, hypothesis testing (one-way Anova, Paired T-test, Chi-Squared Test) and Descriptive Statistics (Mean, Median, Mode, etc.).
Augmented Analytics Today and In the Future: Today, augmented analytics has changed in a number of ways. The foundation of the technology now provides better performance with low-code and no-code options to improve scalability and reduce the size of the solution for easier implementation, use, and upgrade. Numerous publications, like BuiltIn and Berkeley, write about the expansion of artificial intelligence (AI) in analytics. As AI advances and its use becomes more dependable it will find its way into more analytical solutions and its incorporation means more user-friendly tools for business users with more capabilities and functionality.
Why Is Augmented Analytics Important?
Gartner predicts that “70% of organizations will rigorously track data quality levels via metrics, increasing data quality by 60% to significantly reduce operational risks and costs,” and that “90% of the world’s top 500 companies will have converged analytics governance into broader data and analytics governance initiatives,” and lastly, “60% of organizations will leverage machine-learning-enabled data quality technology for suggestions to reduce manual tasks for data quality improvement.”
So, what does all this mean to your business? Why is augmented analytics an important factor in your success? The typical business will find it difficult to achieve approval for a new software solution. Executives tend to look at technology from the lens of expense and time, and your team is probably no different. If you are to gain approval for a new augmented analytics solution or an upgrade of your existing system, you will need to focus on the importance of the solution to your enterprise, both at the organizational level and for its business users, stakeholders, customers, etc.
Here are a few reasons you should consider augmented analytics for your enterprise:
- If your business users have access to augmented analytics, your business analysts, data scientists, and IT team can focus on strategic issues and projects, without the burden of day-to-day analytical requests.
- Accessible augmented analytics, allows you to transition your business users to the citizen data scientist role to make better decisions, more quickly.
- Users can replace guesswork and opinion with fact-based presentations and recommendations for more measurable analysis of trends, product pricing, financial investment, manufacturing and production, and all other business factors.
It is not always easy to convince the management team of the wisdom of a new investment, but it is important to understand that the market for augmented analytics is evolving and that your competition is already on board and using these tools to engage with customers, create new products, and solve problems before they negatively impact the bottom line.
