Top 10 Features of Augmented Analytics Platform
Big Data has been making significant strides in the last decade. Databases have grown to be so large, complicated, and fast-moving that the traditional systems are no longer capable of handling them. Consequently, the duties of data analysts are becoming increasingly challenging that eventually leads to discrepancies and unreliable conclusions. This is where augmented analytics comes in to save the day. It ensures democratization of AI across the entire data value chain. It also automates most of the processes involved in analytics and gives bite-sized inputs for human analysts. However, with a new augmented analytics tool emerging in the market every day, it might be difficult for analysts to choose the best one from the available options. So, in this blog, we have put together a set of ten things to look for in an augmented analytics platform.
1. Conversational Analytics
This feature enables you to converse with your data using conversational AI and natural language processing (NLP). With Conversational AI, analytics becomes more like a Google search where you get your query answered in natural language. This analytics model uses a natural language query (NLQ) to ask questions to the analytics platform and get answers to the questions in a natural language using Natural Language Generation (NLG). This simplicity eliminates the requirement for technical expertise in query languages such as SQL, R, or Python, thus enabling anyone in the organization to become a citizen data scientist.
2. Automated ML
Machine Learning deciphers patterns from historic data and understands things that are invisible to the human eye. For example, ML models reveal linkages and hidden patterns in the datasets in a much more efficient way. Also, the processing is done much faster.
Automated ML also simplifies the otherwise time-consuming processes such as the creation of dynamic what-if simulations, speculation of information such as trends, significant influencers, and outliers, and much more.
3. Intelligent Predictive Analytics
Businesses can create more accurate and data-driven decisions faster and more efficiently with predictive analytics. It enables you to speculate about possible events and be prepared for the future. To automate advanced predictive analysis and enrich your business experience with more data and insight, use three types of machine learning algorithms: classification, regression, and time-series analysis.
4. Smart Insights
Smart insights offer you a consumable form of reports. This feature allows you to delve further into your data visualizations without going through the process manually. You may reveal the key contributors behind values or variances, understand the factors influencing your KPIs, and more with a single click. On the fly, sophisticated algorithms offer additional visualizations for your data story, as well as text explanations and context provided in natural language.
5. Reduced Analytical Bias
Allowing the machine to analyze data can help reduce analytical bias. Assumptions are made when you don’t know what you’re looking for. Those assumptions can often lead to the usage of specific evidence to back up those assumptions. Augmented analytics can reduce bias by analyzing a larger range of data and focusing just on statistically significant elements.
6. Faster Data Preparation
Data from numerous sources is integrated considerably more quickly using augmented data preparation. Data quality and enrichment recommendations are generated automatically by the system, and even automates the profiling, tagging, and annotation of your data and cleans it for accurate analysis in a fraction of the time.
7. Immediate Automated Analytics
Because analysis is automated and can be configured to run at any time, the heavy lifting of manually sifting through large volumes of complex data (due to a lack of skills or time restrictions) is greatly minimized. If your data management tools detect a spike, dip, or shift, they can also automate the dissemination of that information, allowing users to act quickly.
8. Improved Data Transformations
Automating boring but important data transformation activities, such as physically connecting schemas together or doing comparison calculations, is possible with augmented data preparation. Without the need for manual involvement, algorithms discover schemas and connect data from many sources.
9. Machine assisted insights
These insights can be in machine-generated visualization, calculation creation, and variance analysis and are often triggered by the user asking a question. Augmented analytics platform auto-generates the analysis of a problem, calculations, and building of any charts. It ensures deeper analysis of a query to answer vital questions quickly.
Augmented Analytics platform makes the creation of dashboards and visualization simple for business users. It draws on data relevant to the user’s natural language query and auto-visualizes in seconds instead of manually visualizing the data to create the graph, scatter plots, and pivot tables. These capabilities will enable users to adjust the visualizations tailored to their needs and audience.
Is your organization is planning to adopt Augmented Analytics platform? If yes, feel free to share your thoughts in the comments section below.
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