In a normal business day, waiting to get critical data analyzed and presented in the form of an understandable report can be a waste of valuable time for business users of all levels. Moreover, a report developed by expert Data Scientists or other IT staff may be beyond ordinary comprehension, amounting to even more wasted time and effort.
To help solve this problem, next-generation Data Analytics are designed to make the often “guarded” domain of Data Science easily accessible to the common data users. Augmented Analytics and Data Discovery make it much easier for the non-tech employees to indulge in guided Data Analysis with advanced tools.
The Augmented Analytics process enables the extraction of intelligence through built-in trend recognition and pattern recognition tools. To a large extent, the Data Analysis process is automated, and sophisticated Machine Learning (ML) and Natural Language Processing (NLP) features aid this next-generation Business Analytics activity.
Augmented Analytics: The Next-Generation BI Platform
In Gartner’s paper, Augmented Analytics Is the Future of Data and Analytics, the authors define Augmented Analytics as a new approach to automating the Business Analytics process through the use of Machine Learning and Natural Language Processing.
To help in demystifying the long term of “Augmented Analytics and Smart Data Discovery,” think of an Artificial Intelligence (AI) enabled Analytics platform. AI, ML, and NLP technologies combine to strengthen the automation capabilities of Analytics and Business Intelligence (BI) platforms with the Citizen Data Scientist in mind. What this means for global businesses is that, very soon, next-generation BI/Analytics platforms will have embedded AI technologies for quick and improved decision making by all types of business executives.
The primary focus of the Hype Cycle for Analytics and Business Intelligence, 2017, is that BI platform vendors will have to modernize their solutions from “visual data discovery to AI-enabled Augmented Analytics” to empower business leaders with a key market differentiator.
A Move Forward for Self-Service BI
Kartik Patel’s DATAVERSITY® article, What is Augmented Analytics and Why Does it Matter? offers some techniques for identifying the appropriate solution for a given problem. Patel argues that Smart Data Discovery takes a leap forward from just revealing the trends, patterns, and inter-relationships of data, and helps users leverage the discovered data for profitable opportunities. With Augmented Analytics and Smart Data Discovery, users have the actual tools to make “strategic, operational and tactical plans” for their businesses. In many ways, this form of analytics allows the user to act in the present and plan for the future at the same time, without the help of any IT staff.
According to a Forbes article Gartner: Top 10 Strategic Technology Trends for 2018, many major software vendors are currently battling to be the first vendor to offer AI-powered Analytics products and solutions. In the article, trend number two – “Intelligent Apps and Analytics” – indicates that, if Analytics vendors wish to remain competitive, they will have to incorporate Machine Learning and NLP in their business solutions very quickly to usher in the next generation of business analytics.
Smart Data Discovery: What it is and What it Promises
In the article Smart Data Discovery Will Enable a New Class of Citizen Data Scientist, readers of this post will find that the next-generation Business Analytics platforms will make it easy for the Citizen Data Scientists to extract insights from Advanced Analytics tools. The Analytics software market vendors are all gearing up to implement Smart Data Discovery in their platforms. Smart Data Discovery, also known as “Augmented Intelligence” is the next game-changer for the Business Analytics space.
The 2017 Gartner report titled Critical Capabilities for BI and Analytics Platforms, indicates that by 2021, the top businesses will differentiate themselves from their less fortunate peers by adopting Smart Data Discovery platforms “at twice the rate, and will deliver twice the business value.”
Smart Data Discovery tools are known to cleanse and prepare data, find hidden patterns correlations, and deliver insights without user intervention. Just the way Self-Service BI disrupted the world of traditional BI, Augmented Analytics and Smart Data Discovery will disrupt the world of Self-Service BI even further.
As the saying goes, “A picture is worth more than a thousand words.” The book The Visual Imperative: Creating a Visual Culture of Data Discovery provides a powerful background to this journey of “disruption, transformation, and reinvention” in the world of Business Analytics. In an era where business data rules, the advanced technologies that allow businesses to capitalize on that data through visual Analytics, will likely win the race!
AI-Enabled Knowledge Discovery Solutions
Augmented Analytics and Data Discovery leverages Artificial Intelligence to enable the business users to extract more value from their data with less effort or with less technical knowledge. “Cognitive search” is the new buzzword in the world of Business Analytics. This form of advanced search methodology makes use of AI-enabled Analytics technologies like Machine Learning or NLP to deliver “ingested, comprehended, and organized” content aggregated from diverse data sources.
The positive aspect of cognitive search technique is that it can ingest both structured and unstructured data. To evaluate the cognitive-search vendors, Forrester conducted an extensive survey of top vendors. You will find the results of the Forrester survey in The Forrester Wave™: Cognitive Search and Knowledge Discovery Solutions, Q2 2017.
Augmented Analytics and Data Discovery for the Analytics-as-a-Service Market
According to The Forrester Wave™: Insights Service Providers, Q1 2017, the Analytics-as-a -Service market is growing steadily as most strapped business executives are forced to depend on outsourced BI services for timely delivery of market intelligence and actionable insights. In fact, recently, the business leaders have begun to form strategic partnerships with insights service providers who often provide custom solutions to business problems.
Right now, till Augmented Analytics and Data Discovery platforms gain more adoption and maturity, these intermediate service providers will make use of the available technologies to aid businesses reap the maximum benefits from their analytics investments.
A big challenge for service providers right now is loading IoT data on storage as fast as they come in. The only way business users can extract actionable insights from IoT data is by mapping the incoming data against the historical data through advanced visualization capabilities. This is one environment where Augmented Analytics and Smart data Discovery will aid insights service providers.
Where Big Data Meets Business Analytics
The presence of Big Data is everywhere in the Business Analytics space.
Many attempts have been made to make Big Data work with traditional BI, but most efforts have failed because of the sheer magnitude and complexity of the data. Big Data has for the first time, not only provided better alternatives for data handling but has also democratized the availability of data through technologies like NoSQL.
The Forrester post An Approach To Converge The Worlds of Big Data And BI suggests that no matter how democratic the data repositories become, the need for improved Data Quality, Data Governance, Master Data Management, Data Modeling, and many other BI issues will not go away.
Hadoop has partially solved the problem of Data Management through hubs and Data Lakes, which provide far better alternatives to Data Warehouses and Data Marts. Now, Augmented Analytics and Data Discovery tools may further democratize business analytics by enabling the user to easily access, visualize, and analyze data without the help of technical staff.
While many IT Departments are struggling to find clever ways to collect and visualize such vast troves of diverse data types, a great value to marketers may emerge on the intersection of online and offline data. As live devices continue to proliferate the all types of business operations and processes, the next challenge for Analytics system vendors is to make all organizational data work together to deliver the best results.
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