Most companies have adopted data analytics and intelligence. But they struggle to use it effectively to maximize impact. According to Gartner’s marketing analytics study, more than half of companies can not measure the ROI in analytics.
Other complaints about the analytics investment include:
- Poor quality of data
- Unclear recommendation from data analytics
So, it is hard to interpret insights to take action accordingly. Augmented analytics, the next-level data analytics, is solving these challenges. It handles data to insights processes and implements actions based on data findings.
It automates the data-insight-action cycle, thus saving time and resources. Also, it gives you the confidence that it is currently lacking in traditional data analytics.
Let’s learn more about Augmented Analytics and its role in the future of business intelligence.
What is Augmented Analytics
Augmented analytics is a data analytics technique that uses AI technologies to automate analysis tasks. It leverages AI, ML, and natural language processing (NLP) to improve data handling, insight generation, and explanation.
AI facilitates automation, and ML helps the augmented analytics platform to learn from collected data to discover hidden insights. NLP provides easy and interactive data management using text and voice-enabled technologies.
Augmented analytics can be implemented using conversational interfaces. Hence, it will allow more people, including those less tech-savvy, to perform analysis. You can get fast answers, predictions, and the best recommendations by using voice, text, etc.
It will add valuable experience to how we interact, explore and understand data.
Augmented analytics can automatically search and display hidden data insights, trends, and anomalies based on a data set. So, you can run analysis and BI tasks without necessarily being a data specialist. It will free up business analysts to handle more high-value tasks.
But, business analyst roles are still in demand at the moment because augmented analytics is not yet mainstream. They include system analysts, BI analysts, information security analysts, network analysts, etc. The list is endless because they play a crucial role in bridging the gap between IT and business to drive the company forward.
Below are some of the roles of augmented analytics.
1. Data Democratization
Also, the models have user-friendly interfaces, including conversational ones, simplifying the use of the data across the company. You can just use simple English to run your queries instead of SQL queries or use voice to get insights on your datasets.
2. Accelerating Decision Making
Data-driven insights are still significant in strategic business operations. As mentioned earlier, integrating BI platforms with augmented analytics will enhance data accessibility.
It streamlines and speeds up the data analytics process. The whole process is automated, making it easier to clean, sort, analyze and visualize data within the shortest duration. Thus, it makes relevant, clear data insights available to anyone at the right time.
So, you will require less time and effort to find and analyze data. Therefore, business analysts can concentrate on strategic tasks and leave mundane and repetitive ones to augmented analytics. The high-speed delivery of data insights and the ease of understanding them will accelerate the decision-making process too.
3. Eliminate Human Errors and Drive Better Accuracy
We are prone to error as humans. It does not mean machines are 100% perfect. But all of us, including business analysts, are prone to error and bias.
On the contrary, integrating augmented analytics in BI platforms will reduce human interventions in the systems. So, the algorithms running them will have less exposure to human bias. So, you will get valuable market and company insights free of discrimination.
4. Controlling Operational Costs and Efficiency
You can use ML and AI to automate the data cleaning and preparation process through augmented analytics. Also, other functions like data discovery, statistical analysis, and others can be automated. So, the process will be faster and the insights uncovered in real-time.
Apart from enabling faster decision-making, you will not need more business analysts leading to lower operational costs.
5. Automated Recommendations to Activate Actions
Another distinctive role of augmented analytics is automated recommendation for easier activation of actions after insights. As mentioned earlier, augmented analytics will increase access to data. Users with various skill levels can easily query and uncover insights from enterprise data.
With augmented analytics, business intelligence platforms will get intelligent recommendations in an easily understandable manner. As a result, anyone in the company can uncover hidden opportunities and trends. The great part is that anyone can get these insights from the massive amount of data.
So, it will resolve the difficulty associated with understanding data insights from current business intelligence platforms. Users will get comprehensive automated insights and recommendations for better actions to drive positive ROI.
Apart from the above critical roles augmented analytics will play in next-generation BI, it comes with benefits too.
Benefits of Augmented Analytics in Business Intelligence
- It improves accuracy in BI analysis. Traditional BI platforms use manual processes alongside human IT support that are prone to human errors. As a result, data cleaning, preparation, and analysis can affect the accuracy of the process due to human factors.
In contrast, augmented analytics uses advanced IT to perform data and BI tasks accurately. It has little to no human intervention while operational.
The elimination of human intervention also leads to unbiased BI reports and insights. So, BI will enjoy reduced error and bias using augmented analytics.
- It saves company resources. Augmented analytics eliminates the need for more business analysts, especially for repetitive tasks. Additionally, the IT support team that aids their work will also be significantly downsized due to the automation of the data analytics pipeline.
Additionally, automated recommendations like those adopted by Netflix, Amazon,
etc., will drive a personalized experience. Augmented analytics can understand customers, markets, or operational situations better. As a result, it offers nuanced recommendations for better and maximum impact that can improve ROI.
Augmented analytics is disrupting business intelligence for the better. It will improve the efficiency, speed, and impact of next-generation BI. But, it puts business analyst roles at stake.
But the augmented systems will only handle repetitive tasks leaving business analysts to handle strategic BI tasks. It shows that getting insights will be easy, but systems supporting them may be too complex and technical. However, business analysts can scale up to AI engineers to build and maintain these complex systems.