Augmented analytics is starting to be used in applications that help users do something they cannot do on their own.
Continuous intelligence (CI) and artificial intelligence are increasingly being used for decision support. One way this strategy is manifesting itself is via augmented analytics.
How important is augmented analytics? Gartner rates it as one of the top ten technology trends in data and analytics. The list of the top ten, which was posted in November, by this year, augmented analytics will be a dominant driver of new purchases of analytics and business intelligence.
See also: Using Continuous Intelligence for Decision Support and Automation
Gartner defines augmented analytics in the following way: “Augmented analytics is the use of enabling technologies such as machine learning and AI to assist with data preparation, insight generation, and insight explanation to augment how people explore and analyze data in analytics and BI platforms. It also augments the expert and citizen data scientists by automating many aspects of data science, machine learning, and AI model development, management, and deployment.”
In practical terms, what does that mean? Augmented analytics is starting to be used for applications where users do not have to give up their thinking or intent to the algorithms. Instead, the algorithms help users do something they cannot do on their own.
For example, an augmented solution using machine learning algorithms might read everything that was published online in the last hour about a specific topic. Derive contextual messages from that content. And then summarize and present key information upon which a human makes a decision. Or an augmented analytics application might be used to optimize marketing efforts by narrowing the field of prospects for a product or service. The goal would be to develop a customized message for a specific person and deliver it at the right time. This approach will accelerate sales by eliminating the wasted time spent on unlikely prospects and personalizing efforts to reach those in need of an organization’s offerings.
Algorithms also might be used to spot patterns that were missed in the past. Gartner provided an example of this application in its posting: Gartner Top 10 Data and Analytics Trends. In that posting Gartner noted:
“Traditionally, banks targeted older customers for wealth management services, assuming that this age group would be the most interested. Using augmented analytics, banks found that younger clients (aged 20 to 35) are actually more likely to transition into wealth management — a clear example of how relying on business users to find patterns, and on data scientists to build models manually, may result in bias and incorrect conclusions.”
Augmented intelligence also can play a role in machine-to-machine applications. A prime area is in translation. Today, there are many available language translation programs. Some perform basic vocabulary lookups for text-based conversions. Others complement such capabilities by using Natural Language Processing in speech-to-text and text-to-speech applications.
Augmented systems might further enhance the quality and accuracy of a translation by using specialized knowledge of a dialect or region to, in a sense, suggest more suitable translations.
Endless possibilities and use cases
The financial services market is ripe for augmented analytics applications. Perhaps the biggest impact of CI and AI in financial regulatory processes is that the technology complements the work of skilled analysts. In most applications, the technology automates chores and speeds analysis, delivering guidance to analysts that can be used in their decision-making processes. Aiding decision support is, in fact, one of the common benefits of CI in many financial services applications.
A fundamental area where augmented support can deliver great benefits in financial services, but also other verticals, is in the data integration process that all CI and AI applications use. Such capabilities can facilitate streaming analytics, feeding of diverse data sources, and make use of machine learning and AI.
Data integration technologies help businesses capture and deliver critical dynamic data across an enterprise to expedite better decision making. Critical characteristics needed include the ability to consume, transform, and deliver data wherever it resides.
Up-to-date availability of data is essential to make the right decisions at the right time, minimizing latency. With data being delivered in the most efficient and effective way possible, organizations can deliver incremental changes with very low latency to the target systems to drive CI.
With real-time data, businesses can start to analyze that data to determine what is happening in their businesses, why it’s happening, and predict what happens next. Hitting the mark for fast and accurate decisions requires that analytics tools instantly turn data into relevant insights, giving the right people the right information at the right time and removing any obstacles.
Traditional solutions have no hope of making sense of the volume, variety, veracity, and velocity of data being created. Harnessing this flood of business data requires a new approach to BI, enabled by embedded AI. Augmented solutions could enable proper vetting of data to ensure the most relevant data and data that passes a business-defined quality level is used by CI and AI applications to make decisions.
Implementation choices
Organizations can make augmented analytics available to users in several ways.
Augmented analytics may be incorporated into existing dashboards. Traditional key performance indicators (KPIs) presented in dashboards could be complemented with augmented analytics.
Additionally, augmented analytics could be integrated into business processes. For example, a traditional job candidate review workflow might be enhanced to look for factors previously not considered. For instance, a company may use an algorithm that uses machine learning to scan social media for comments and sentiments of an applicant.
One more way augmented analytics will likely make its way into the mainstream is through vendor enhancements to their business intelligence and business process management solutions.
