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Top Data Analytics Trends to look out for in 2020

Data Analytics trends

Published on Nov 07, 2019

Everything has been moving towards machine learning and automation, the truth of it is, we just want to eliminate the need for manual work and human error! Saving time and increasing productivity is every business’s operational focus, how we achieve that has depended on the technological capabilities of the time. 2020 will be particularly interesting because of some major advancements in hyper-focused data-centric technologies aimed at optimizing the operational workflow.

Here are some to watch out for:

Machine Learning

Machine learning is the application of artificial intelligence (AI) algorithms that provide the systems with the ability to automatically learn and improve from experience without being explicitly programmed to do so. It focuses on the development of computer programs that can access data and use it to learn for themselves with the main aim of minimizing the level of human intervention or assistance. The algorithms simulate human learning capabilities that help the system automatically improve through this experience thus delivering accurate results.

ML eliminates the need of doing manual hours while enhancing security, network performance and reducing operational cost and time at the same time.

Explainable AI

The buzz word ‘Artificial Intelligence’ or ‘AI’ is used and heard everywhere from science fiction movies to tech giants like google and facebook creating real-life AI software. But apart from the basic definition, very few people can actually explain how an AI model works and acts the way it does. This gives rise to a certain degree of unpredictability in the way the AI model takes the decisions and gives results. Explainable AI or XAI area inspects and tries to understand the steps and models involved in making decisions. XAI systems are expected to answer questions like:  Why did the AI system make a specific prediction or decision? When will the AI system succeed and when will it fail? When will AI systems give enough confidence in the result that you can trust it, and how can the AI system correct errors that may arise?

This transparency and traceability in the steps involved can provide visibility for cutting edge AI models without compromising too much performance or accuracy.

Continuous Intelligence

Real-time, latest and up to date analytics when integrated within a business operation, leveraging the available historical data along with the current data to prescribe actions in response to events is known as Continuous Intelligence. Using multiple technologoies such as augmented analytics, ML, event stream processing, etc, continuous intelligence enables companies to deliver better outcomes as it involves more relevant, real-time data in decision-making algorithms. Deploying continuous intelligence results in faster troubleshooting, faster data insight with support for business cycles, pace and the creation of a new value.

However, we are not quite there yet.

What makes continuous intelligence difficult is feeding a business’s analytics systems with high volumes of real-time streaming data in a way that is robust, secure, and yet highly consumable. The ability to combine “always-on,” streaming data ingestion and integration with real-time complex event processing, enrichment with rules and optimization logic, and streaming analytics is key to enabling continuous intelligence.

Augmented Analysis

The data analytics field especially when dealing with big data is pretty complex. This complexity arises from the numerous sources from which the data is gathered such as social media posts, marketing releases, web analytics, etc. This collected data further needs to be refined and a thorough quality check needs to be done so that the data scientist can glean meaningful and actionable insights. It is estimated that data scientist spend 80% of their time doing these manual tasks. This is where augmented analytics comes in. It is the enhancement of data analytics procedure using Machine Learning (ML) and Natural Language Processing (NLP) to automate the process of scrubbing, parsing and returning key data for analysis.

Data analysis automation

Recently, in a test conducted by researchers from MIT, a prototype called Data Science Machine was implemented in several data science contents where this automation performed with 96% accuracy doing what took humans months of decoding for prediction algorithms, in a few hours[1].

Data analysis automation, depending upon the level of sophistication and technology applied can take only a few hours to a few weeks to process, analyze and understand any amount of data. This directly translates to a reduction in operational costs, improvement of operational efficiency and increased scalability.

Conversational Analytics

Conversation analytics is the transcribing of speech into data in order to gain insights on customer behavior. Conversation analytics solutions typically include:

  • A transcription engine that converts speech to data
  • An indexing layer that makes the data searchable
  • A query and search user interface to allow the user to define requirements and carry out searches
  • Reporting applications to present the analytics, often in graphical form

For management, conversation analytics makes every speech-driven data transparent through an easy search utility and powerful, graphics-driven reporting. Managers can set the budgets of campaigns and check which aren’t delivering ROI, or dive deep into customer sentiment to better understand the needs of their target audience and cultivate trust and customer loyalty.

These insights provide accurate, up-to-date answers to questions about product quality, search keywords, user needs, and customer experience.

For marketers, with conversation analytics, they can efficiently score leads, spot patterns — which are the most effective segment of their audience. This saves time and improves the accuracy of outreach leading to higher customer satisfaction.

Augmented Data Management

Garbage In = Garbage Out

This is the key principle that directs the practices of data management. The sheer growing quantity of data that must be gathered, cleaned and fed into systems for analysis needs an efficient, streamlined and economical structure of management to reduce the chance of ‘garbage’ input which could result in ‘garbage’ output.

Augmented information management takes care of this downside by taking advantage of automation, Machine Learning and AI capabilities to form information management practices self-sustaining. Augmented data management techniques will increase the level and extend of data cleansing, characterizing, and linking while creating recent record–matching and merging algorithms.