Introduction to Augmented Analytics
Augmented analytics represents the next wave of disruption in the field of analytics. In this article we introduce the concept of augmented analytics and its potential to deliver much more detailed and relevant insight into the copious amounts of data held by organisations these days. We’ll also show the ease at which these insights can be accessed, making it easy for non-technical users to obtain and leverage, on a self-service basis.
Under the practices of traditional business intelligence, it is no longer possible for users to explore every feasible combination, pattern or trend using current analytical approaches, without then having to determine whether their findings are even relevant or actionable. The concept of augmented analytics was originally referenced in a Gartner report and was defined as “an approach that automates insights using machine learning and natural-language generation.”
As Big Data becomes bigger, businesses have become increasingly saturated with data, making it difficult to extract what is relevant for specific users. Augmented analytics plays a big part in deciphering the relevant business requirements and creates contextual relevance with less bias, transforming how users consume data, interact with it, make decisions and leverage the insights gained.
Augmented analytics consist of three main components: machine learning, natural-language generation and automating insights.
Machine learning can be described as programs or algorithms that are capable of learning from data and adapting to different uses without relying explicitly on programmed rules to do so. As these machines process more and more data, the algorithms are continually tested/challenged and as such, this “learning” process becomes increasingly accurate over time, with very few mistakes – just as we learn to improve our skills and become more proficient through repetitive experience, over time. Applied to analytics, machine learning can provide substantial augmentation in:
1. Data preparation – data quality, data profiling, modelling, harmonisation
2. Data discovery – find, visualise and narrate relevant findings such as correlations, predictions and exceptions
3. Data science and machine learning – automate major aspects of advanced analytical modelling
Machine learning can facilitate unbiased decisions and impartial contextual awareness with analytics outputs. As such, it will transform how users now interact with data and how they consume and act on insights.
Natural-Language Generation (NLG) concentrates on data analysis outputs and is defined as the process that translates a machine’s findings into words and phrases that lay–people can understand. Therefore, NLG is closely aligned to machine learning because it enables the average, non-technical person to understand what’s occurring in their data, in simple language
NLG is all about “humanising” these algorithms so users can easily understand and apply the insights they are receiving. NLG is also particularly effective when applied to the search functions of augmented analytics platforms, allowing the potential for users to be able to engage in a basic conversation with the machine – i.e.: asking a question and receiving a spoken response or perhaps generating a visualisation. This capability does not just deliver the required information, it can also ask for additional material, facilitating deeper, more accurate analysis as a two-way interaction.
The insights derived from augmented analytics algorithms would usually require a number of highly qualified technical people and would take weeks to produce. And quite often, data scientists and analysts would be required to interpret and translate it into usable, effective information for business users. By combining machine learning and NLG, businesses can automate the labour-intensive process of analysing data and communicating the results to business users in a fraction of the time, without the added requirement of an expert or power-user to facilitate it. Quite simply, entire business strategies can be effectively and efficiently created using the data-driven insights derived from augmented analytics.
Augmented Analytics – Business Advantages
There are tremendous business benefits for applying augmented analytics:
Deeper, more extensive data analysis – Augmented analytics takes care of all the hard processing work. It compares extensive combinations of data, even faster than ever before, and can accurately determine the factors that influence your outputs.
For example, Marketing executives can investigate beyond the usual basic analysis questions using AI capabilities. The machine can drill down and look for deeper insights such as the correlation between age demographic, buying frequency and churn risk, the findings of which can then be provided as a report using natural language.
Results are obtained faster – Augmented analytics, using the enhanced processing power of machine learning and NLG, deliver answers and insight in seconds and can be driven / obtained by non-technical users.
For example, CXOs in the boardroom need to better understand customer behaviour to minimise churn and capitalize on opportunities. To manage this optimally, they would benefit from immediate access to data and the ability to automate the analysis process. In virtually real time, an executive can go straight to the data and learn which streams are rich in attracting customers and retaining them as soon as these questions are asked, and theories are formed. They no longer have to wait for days or weeks for an answer or report to come back. With augmented analytics, questions can be immediately asked and answered by virtually any team member, or even through the platform’s automated insight capabilities.
Better use of resources – Augmented analytics offers ease of use by non-technical users. This means that the capability is of a self-service nature leaving the functions of the business analysts and data scientists to be focused on navigating the business in the areas that machines cannot yet support
Actionable Insights – through a simplified data analysis process, quickly and easily obtain insight into your data to drive business strategy
For example, Digital marketing has no shortage of data available. Using AI functionality, marketers can extensively leverage the use of all available data. They can monitor and synchronise their digital traffic and advertising data, creating a comprehensive profile view of visitors to a particular website. Further to this, an augmented solution will simplify the automated creation of personalised Web experiences for site visitors over traditional software applications.
These benefits provide a dynamic platform built on a foundation of robust business strategy that can be responsive to the evolving shifts in the market and changing demands of the consumer. And with a focus on efficiency, users can answer business questions with more current, up to date insights. Rapid, agile analytics drive revenue – whether it is outperforming competitors or searching for market niches or simply navigating your business forward, augmented analytics plays a crucial role.
Augmented analytics platforms process data much faster than traditional forms of analytics and business intelligence, with minimal human interaction and human bias. Finding comprehensive, accurate and unbiased conclusions from vast sources and volumes of data can be challenging, without accommodating every factor that can influence the results. Using the enhanced processing power of machine learning, augmented analytics can crunch much more data at significantly faster rates, more accurately and with less human intervention or influence. And when machine learning is combined with NLG, it provides the most comprehensive and interactive insight into the data with a focus on self-service, efficiency and ease. Quite simply, augmented analytics empowers business users to efficiently act on the insights they obtain to drive strategy and navigate their business forward.