Augmented Analytics automates data insight by utilizing Machine Learning and Natural Language Processing to automate data preparation and enable data sharing. This advanced use, manipulation and presentation of data simplifies data to present clear results and provides access to sophisticated tools so business users can make day-to-day decisions with confidence. Users can go beyond opinion and bias to get real insight and act on data quickly and accurately. Why is this important to your organization?
Automate the data cleansing process, and prepare data quickly and easily. Make it available to your organization and take the most time-consuming work out of the hands of your analysts so that they can do the most important work for your organization. By automating the data prep process, and opening up the BI tools to your business users, you can achieve your goals, decrease your expenses and improve the accuracy and clarity of your data.
Today, predictive analytics is, and must be, accessible to business users, if your enterprise is to grow and respond to the need for data democratization and increased productivity within the enterprise and to the rapid changes in the market, competition, resource and supplier needs and customer buying behavior. Every business user must have the tools to analyze data and make accurate, timely predictions and decisions. Your organization can truly benefit from predictive analytics and from the ease-of-use and sophistication of these self-serve tools.
What is Automated Machine Learning? It is, quite simply, the automated process of features and algorithm selection that supports planning. Business users can leverage machine learning and assisted predictive modeling to achieve the best fit and ensure that they use the most appropriate algorithm for the data they wish to analyze. Business users can take advantage of AutoML tools to explore patterns in data and receive suggestions to help them gain insight – all without dependence on IT or data scientists.
Advanced analytics is the logical tool to help a business optimize its investments and achieve its goals. But, when an organization is ready to consider the implementation of an Advanced Analytics solution, it is difficult to know what it needs to ensure that it can satisfy current and future requirements and ensure user adoption. If a business wants to assure that it has full coverage for its Advanced Analytics needs and can leverage all the benefits of advanced analytics, it should consider a solution with the necessary capabilities.
Natural Language Processing helps business users sort through integrated data sources (internal and external) to answer a question in the way the user can understand and will provide a foundation to simplify and speed the decision process with fact-based, data-driven analysis. The enterprise can find and use information using natural language queries, rather than complex queries, so business users can achieve results without the assistance of IT or business analysts. NLP presents results through Smart Visualization and contextual information delivered in natural language.
Think of the old paradigm where a software sales person or team would shuffle into your conference room and deliver a packaged presentation, followed by a packaged demo, in hopes of finding a hot button that appealed to you and somehow satisfied your need. That won't work today and a wise software company knows that. If you want to succeed in the market today, you have to get ahead of things. You need to understand what your prospective customers want, and need, and take into consideration the industry in which they work.
SSDP or Self-Serve Data Preparation is a crucial component of Advanced Data Discovery. With self-serve data prep, data analytics moves out of the sole domain of analysts and IT and into the domain of business users. With true self-serve business intelligence and analytics solutions, the average business user can perform data preparation, test theories and hypotheses by prototyping on their own and share clear, objective data with others. Self-serve tools allow users to leverage knowledge and skill and better perform against forecasts and plans.
How does an organization help the self-serve advanced analytics model grow and thrive? Responsibility lies in a number of places within the enterprise. If an organisation incorporates analytics into its strategy and business decisions, it will encourage the use of these tools within the organization. When a middle manager or team member understands that the senior management team values analytics and expects to see data-driven decisions, each business unit and department will embrace the use of self-serve advanced analytics.