Augmented Analytics, an approach that automates insights using machine learning and natural-language generation marks the next wave of disruption in the data and analytics market. Data Analytics allows Artificial Intelligence to find appropriate and meaningful information using Machine Learning. ML is the connection between data analytics and AI since ML is the process of learning from data. Data and analytics leaders should plan to adopt augmented analytics as platform capabilities mature.
Artificial Intelligence is a large field and Machine Learning is an application of AI. It gives the systems the ability to automatically learn and improve from earlier experiences without being explicitly programmed. The term “smart data discovery” was introduced formally in 2017. Today, it is reigning as a powerful differentiator across industries. To get clear insights, most organisations are now focusing on building models and integrating data for simplifying it and automating the tasks.
Data Preparation for Augmented Analytics
Augmented Analytics cleanses and prepares the data automatically, deciphers the hidden patterns in data and builds models using them. Preparation of data empowers business users with access to meaningful data for testing theories and hypotheses, without any help from data scientists or IT staff. It allows users access to crucial data and information and allows them to connect to various data sources such as Cloud. Users will be able to integrate the data and leverage auto-suggested relationships, hierarchies, type casts, etc. Then cleaning and reducing the unimportant parameters of the data comes next. This will help to reduce noise and identify trends and patterns. Thus, interpreting data becomes easier. It also helps in statistical algorithms like clustering, binning and regression. AI algorithms are used to interpret the data and present insights and recommendation to take suitable action.
Traditional Methods of Data Analytics are:
- Expensive to implement and not cost-effective
- Time-consuming and slow in providing desired results
- Manual dependency is high, such as on data analyst and data scientist
- Human predictions are prone to greater errors
Benefits of Augmented Analytics
- These solutions allow the data scientists and IT community to focus on strategic issues and special projects.
- Accessible augmented analytics creates Citizen Data Scientists and improves accountability and empowerment.
- Advances in smart data discovery and other sophisticated techniques and solutions can positively impact ROI and TCO.
- These solutions produce better decisions, more accurate business predictions and measurable analysis of product and service offerings, pricing, financials, production and other aspects of business.
- Augmented data preparation and related tools will improve user adoption, data popularity, social BI integration and data literacy.
- Rather than strictly following predictions based on statistical analysis, “gut feeling” may be used for decision-making. Use of business sense may introduce bias, but predictions tend to me more accurate this way.
Augmented analytics will soon bring data-driven insight to the masses. It has already transformed the entire workflow of analytics. Thus enhanced the way in which data analysts’ access data and work on insights. Modern business intelligence analytics involves automated data preparation, data science platforms and automated insights. These will be embedded in the enterprise applications and conversational analytics in the future. This market is changing rapidly with new tools and improvements introduced every year. It is important to monitor the progress of analytics.
