Data drives everything in business these days.
Data that companies collect, also known as business intelligence, helps drive decisions so that businesses make the best, most budget-friendly, and future-proof decisions that provide long-term benefits and increase revenue.
Augmented analytics pushes this approach further by leveraging AI, machine learning, and NLP (natural language processing) to automate data preparation and insight generation.
Streamlining analysis transforms business intelligence (BI), enabling faster, data-driven decision-making. It also reduces manual staff efforts and makes advanced analytics accessible to technical and non-technical users.
This article defines augmented analytics, outlines its key benefits for BI, and discusses real-world applications, challenges, and considerations. By the end, you’ll know if you need it to optimize your business intelligence and make the best decisions.
What is Augmented Analytics?
Augmented analytics is a way to use computers to help people understand data faster. It is part of business intelligence (BI), which helps companies make smart choices using information.
AI, machine learning, and natural language processing (NLP) help by organizing data, finding patterns, and explaining results without humans doing everything. Traditional analytics needs people to collect and study data manually, which takes more time.
Augmented analytics does this automatically, so businesses can get answers quickly and make better, smarter decisions faster.
Key Benefits of Augmented Analytics in Business Intelligence
You may be a bit tired of hearing how great AI is now, so bear with us. The way augmented analytics uses AI offers several powerful benefits that save money and time and help employees avoid repetitive tasks. Knowing these benefits can help you decide whether this technology is the right addition to your tech stack.
Automated Data Preparation
Data wrangling, the practice of gathering all the data you need from various sources, is time-consuming and repetitive if employees do it manually. Augmented analytics eliminates much of the repetition and potential burnout, reduces overheads, and saves time.
Faster Insight Generation
In the past, business data analysts analysed data manually, which was time-consuming and costly. Augmented analytics reduces the number of staff needed for this process by using AI-powered algorithms to detect patterns and trends in real time.
Improved Decision-Making
Historically, experts would have to make predictions on market trends, and sometimes they would be right and sometimes wrong. It was guessed by casting a broad net, but augmented analytics uses AI to process large volumes of historical data as a more reliable basis for predicting future trends.
Real-World Applications of Augmented Analytics
It’s easy to read about a technical topic like augmented analytics without fully understanding it. How it works in practice when we put it in real world applications can increase understanding of what it is, how it works and whether you might need it in your organization.
Marketing Optimization
AI is a powerful marketing tool. It allows marketers to collect massive volumes of data on consumer behaviours, sites they visit, and what they buy. This data is useful for predicting what consumer trends might occur in the future, and an advertising intelligence platform is a popular way to collect and analyze customer data for useful insights.
Financial Forecasting
But augmented analytics isn’t just powerful for predicting customer trends. It can also use its powerful AI technology to predict how financial markets will change over time. Companies can use this to plan their financial strategy and know when to spend and when to cut back on investments.
Supply Chain Efficiency
Supply chains can also benefit from augmented analytics. It makes it easier for companies to keep track of their products at every stage, from design to delivery. When companies use data extracted using augmented analytics, they can make accurate predictions about when they need to restock products or adjust delivery routes to save time, money and satisfy customers.
Consider these real-world applications for augmented analytics. Would this technology fit in your organization? If the problems within this section feel familiar, this technology might be the solution you’ve been waiting for.
Conclusion
Augmented analytics changes the perception of what companies can do with data and how quickly they can transform data into insights.
It has applications in marketing, customer experience, and financial forecasting to satisfy customers, show them products they’d like to buy and help companies avoid bad investments.
Now is the time to adopt AI-powered analytics to drive better decisions that use data, not just chance for better outcomes. Explore the available augmented analytics tools and strategies to use this technology before your competitors do and win higher revenue and success before market peers do.
