Augmented Analytics – What is It? Why? What Tools?


The evolution of data analysis has taken businesses from IT-centric BI delivery model to self-service BI and now it is poised to go beyond that and put the power of AI in the hands of business analysts. AI algorithms and Natural Language Processing (NLP) are now being built into modern day applications that put powerful machine learning capabilities in the hands of distributed users. This is augmented analytics and it is becoming more common. The fact that there are limited number of data scientists will not limit business units from leveraging the power of machine learning on organizational data.

Augmented analytics uses machine learning to change how analytic content is developed and used. The technology encompasses other modern analytical capabilities like data preparation, data management, business process management, process mining and data science. Organizations can also embed insights from augmented analytics into their own applications. Augmented analytics automates these processes to eliminate the need for data scientists.

Analyst house Gartner recently released its new Market Guide for Augmented Analytics Tools. Gartner highlighted the following providers in augmented analytics tools: Aible, AnswerRocket, Big Squid, DataRobot, DataStories International, dotData, H2O.ai, IBM, Microsoft, Oracle, Outlier, Prevedere, Qlik, Salesforce, SAP, SAS, Sisense, Tellius, ThoughtSpot, and TIBCO Software. There are a few others not mentioned by Gartner that are also noteworthy. Below is a brief synopsis about each tool, obtained through research and identification of each one’s key differentiating feature:

Aible

Aible’s artificial intelligence (AI) platform lets business users create custom AI based on real cost-benefit tradeoffs and operational constraints. Its product enables experts to enhance the data, create models and refine them at a broad level; it enables users to put in their business assumptions and adjustments, so that the models go from abstract to real-world; and it refines the predictions and recommendations on an individual level, down to the end users.

AnswerRocket

AnswerRocket offers a search-powered data analytics platform designed for business users. The product enables you to ask business questions in natural language, and no technical skills are needed to run reports or generate analysis. AnswerRocket features a combination of AI and machine learning, as well as advanced analytic functionality. The platform can also automate manual tasks and answer ad hoc questions quickly, all through native voice recognition.

Big Squid

Big Squid’s Kraken platform gives business users deeper insight into the value and quality of their existing data. By automating many repetitive tasks that data scientists usually perform, Kraken empowers more users to solve their own machine learning problems. Decision makers can create predictions, understand which metrics truly drive the business, and take action based on those insights.

DataRobot

DataRobot enables organizations to leverage the transformational power of AI by delivering the trusted enterprise AI platform combined with an AI-native strategic success team to help customers rapidly turn data into value. Its enterprise AI platform democratizes data science and automates the end-to-end process for building, deploying, and maintaining AI at scale.

DataStories

DataStories is a fully automated predictive analytics software which converts spreadsheet data into robust predictive models for regression and classification problems, and presents results in interactive data stories. Users can compute and view linear and non-linear correlations, discover an optimal predictive model with a minimum set of variables driving a desired outcome, explore possible actions with What-if scenarios and study outliers.

dotData

dotData delivers full-cycle data science automation for the enterprise. Its data science automation platform applies data transformation, cleansing, normalization, aggregation, and combination and transforms hundreds of tables with complex relationships and billions of rows into a single feature table, automating the most manual data science projects.

H2O.ai

H2O is a fully open source, distributed in-memory machine learning platform with linear scalability. H2O supports the most widely used statistical & machine learning algorithms including gradient boosted machines, generalized linear models, deep learning and more. It delivers automatic feature engineering, model validation, model tuning, model selection and deployment, machine learning interpretability, bring your own recipe, time-series and automatic pipeline generation for model scoring.

IBM

IBM offers BI and analytic capabilities under two products. The Cognos Analytics platform is an integrated self-service solution that allows users to access data to create dashboards and reports. IBM Watson Analytics offers a machine learning-enabled user experience that includes automated pattern detection, support for natural language query and generation, and embedded advanced analytics capabilities.

Microsoft

Microsoft’s platform, Power BI, is cloud-based and delivered on the Azure Cloud. On-prem capabilities also exist for individual users and for authoring. Power BI enables users to do data preparation, data discovery, and dashboards with the same design tool. The platform integrates with Excel and Office 365, and has a active user community that extends the tool’s capabilities.

Oracle

Oracle Analytics Cloud offers modern, AI-centric self-service analytics features for data preparation, visualization, enterprise reporting, augmented analysis, and natural language processing/generation. There’s a companion mobile BI app that learns what you are interested in as well. Analytics Cloud dashboards aggregates content from a variety of sources and systems so users can interact with it via the integrated experience.

Outlier

Outlier.ai is an Automated Business Analysis company that produces an eponymous data analysis platform that determines and analyzes data outliers and outlier patterns. Outlier.ai’s business analysis platform extracts data from internal and external sources and analyzes it to spot critical changes in behavior. It plugs into different platforms and makes sense of trends involving various customer segments, then uses this information to highlight a few of the most important changes happening daily.

Prevedere

Prevedere’s ERIN Predictive Analytics Cloud (EPAC) provides a way for companies to incorporate external data into their planning and forecasting processes. It predicts and quantifies the impact of external influences on future business performance. Businesses can incorporate the insights and knowledge they glean from EPAC to augment their existing processes and people. EPAC allows companies to make smarter and more proactive business decisions.

Pyramid Analytics

Pyramid Analytics offers data and analytics tool through its platform Pyramid v2020. The solution touts a server-based, multi-user analytics OS environment that provides self-service capabilities. Pyramid v2020 features a platform-agnostic architecture that allows users to manage data across any environment, regardless of technology. The tool enables those users to prepare, model, visualize, analyze, publish, and present data from web browsers and mobile devices.

Qlik

Qlik offers a broad spectrum of BI and analytics tools, which is headlined by the company’s offering Qlik Sense. The solution enables organizations to combine all their data sources into a single view. The Qlik Analytics Platform allows users to develop, extend and embed visual analytics in existing applications and portals. Embedded functionality is done within a common governance and security framework. Users can build and embed Qlik as simple mashups or integrate within applications, information services or IoT platforms.

RapidMiner

RapidMiner brings artificial intelligence to the enterprise through an open and extensible data science platform. Built for analytics teams, RapidMiner unifies the entire data science lifecycle from data prep to machine learning to predictive model deployment. RapidMiner uses a unified interface to manage various tasks though a graphical drag-and-drop approach. It offers pre-defined machine learning libraries but also incorporates numerous third-party libraries.

Salesforce

The Salesforce Einstein Analytics platform’s automated data discovery capabilities enable users to answer questions based on transparent and understandable AI models. Users can tailor analytics to their use case and enhance insights with precise recommendations and specific guidance. Einstein lets users create advanced experiences using customizable templates, third-party apps, or custom-build dashboards. Salesforce also has Tableau, which allows for data preparation, data discovery, and dashboards within the same tool.

SAP

SAP offers a broad range of BI and analytics tools in both enterprise and business-user driven editions. The company’s flagship BI portfolio is delivered via on-prem (BusinessObjects Enterprise), and cloud (BusinessObjects Cloud) deployments atop the SAP HANA Cloud. SAP also offers a suite of traditional BI capabilities for dashboards and reporting. The vendor’s data discovery tools are housed in the BusinessObjects solution, while additional functionality, including self-service visualization, are available through the SAP Lumira tool set.

SAS

SAS Visual Analytics is available on-prem or in the cloud. Visual Analytics allows users to visually explore data to automatically highlight key relationships, outliers, and clusters. Users can also take advantage of advanced visualizations and guided analysis through auto-charting. SAS tool can ingest data from diverse data sources and handle complex models. In addition to BI, SAS offers data management, IoT, personal data protection, and Hadoop tools.

Sisense

Sisense product allows users to combine data and uncover insights in a single interface without scripting, coding or assistance from IT. Sisense is sold as a single-stack solution with a back end for preparing and modeling data. It also features expansive analytical capabilities, and a front-end for dashboarding and visualization. Recent augmented analytics add-ons include data preparation through ElastiCube and augmented text deduplication.

Tellius

Tellius offers a search and AI-powered data analytics platform. The product features a proprietary Genius AI Engine that is designed to allow business users to ask questions about their data. Tellius has natural language processing that interacts with users in plain language and creates narratives alongside data visualizations as well. The tool can provide personalized recommendations by anticipating needs and automatically offering related insights and suggestions.

TIBCO Software

TIBCO’s BI and analytics portfolio comes in two main iterations: TIBCO Spotfire and TIBCO Jaspersoft. TIBCO Spotfire features interactive visualization, data preparation, enterprise-class governance, and advanced analytic capabilities. TIBCO Jaspersoft supports traditional reporting and embedded BI functionality.

ThoughtSpot

ThoughtSpot features a full-stack architecture and intuitive insight generation capabilities via the in-memory calculation engine. A distributed cluster manager provides customizable scaling options, and support for existing ETL solutions ensures proper connectivity to desired data sources. ThoughtSpot Embrace allows users to run search and AI analytics directly in existing databases, and supports Google Cloud Storage.

Yellowfin BI

Yellowfin specializes in dashboards and data visualization. Its platform features a machine learning algorithm called Assisted Insights that provides automatic answers in the form of easy-to-understand best practice visualizations and narratives. Yellowfin comes pre-built with a variety of dashboards, and users can embed interactive reports into third-party platforms, such as a web page, wiki, or company intranet.

Gartner expects augmented analytics to become the dominant driver of new business intelligence. Researcher project that at least half of all analytical queries will be generated via search, natural language processing or voice by 2021. Data scientist scarcity may soon no longer be a roadblock to data science initiatives in the enterprise.

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