What is Augmented analytics and why is it important

It has always been said that information is power, and this has never been truer than it is today. In today’s business eco-system, data and information are vital to business management and administration. However, the nature of data is ever fluid and changing. As a result, a lot of data exists for analysis presently. Properly applied, this data can take enterprises to whole new levels. To deal with this, data scientists came up with Augmented Analytics.

augmented analytics

What is Augmented Analytics?

This is an approach that analyses all available data to automatically generate insights by using natural language generation together with machine learning. Since data is becoming more complex by the day, relying on human intuition to pick through all the variables is difficult, resource tasking and not to mention time-consuming. With Augmented Analytics, you get fast accurate insights into critical decisions.

How can Augmented Analytics transform your business?

  1. The hardest part of data analysis is filtering the noise from the actionable data. With Augmented Data Discovery and Augmented Analytics, all data is combed, and correlations, patterns and outliers are identified. This enables management to only see what is relevant. By using Natural Language Generation (NLG) software systems, narratives and reports are created which gives highly summarized detail of input data. With this information, anyone in the business can become a citizen data scientists (CDS) which in turn helps eliminate false and irrelevant insights.                                     
  2. Once the insights are generated, a vast range of business users, CDS, operational workers and data scientists can receive them and manipulate to fit the desired CDS are important because although their primary field is not statistics and analysis, they use big data tools to create models. Gartner has predicted that by 2020, citizen data scientists will surpass the number of advanced analyses produced than regular data scientists.                                                                                                                    
  3. With CDS honing their craft and becoming more independent, there is an opportunity for high specialization for expert data scientists. This will allow for symbiosis and adaptation of enterprise-based models into regular applications giving the small business access to data multinationals have. This will mean that the bottleneck between CDS and expert data scientists will be eliminated thus allowing CDS to act on insights presented to them.                                                                                 
  4. Apart from just presenting insights, Augmented Analytics will also include some solutions to handle arising issues such as risk management, uncertainties and problem-solving. Because of their analytical capabilities employing Artificial Intelligence and Machine Learning algorithms, CDS can see all possible probabilities and choose the best one. With that, the users can fully see the impact of their operational decisions with pinpoint.                                                                                                         
  5. Machine Learning algorithms are embedded with a system called “gradient descent” which essentially means it constantly improves models and solutions according to the incoming data. This means that management can also be fluid in its decision making so that models and rules can be as precise as possible.                                                                                                                                                
  6. Since the computational power of humans is a lot less than that of computers, an analyst will manually have to test different data combinations for insights. This is time-consuming and prone to human negligence and bias which leads to decreased efficiency and missing insights. With Augmented Analytics, various algorithms can be employed to identify clusters, correlations, relationships and outliers in a fast, and accurate way.

Augmented Analytics beats traditional data analytics methods which are;

  • Non-cost effective and expensive to implement and upkeep.
  • Slow in producing desired results.
  • Dependence on manual systems is high because, without the data scientist or analyst, nothing can get done.
  • Human predictions are a lot less accurate because they can factor only so many variables without bias.
  • Sometimes “intuition” will be used in the place of scientific predictions backed by stone cold statistics.                                                                                                                                                            

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