“Having to manually find patterns in the data is like looking for a needle in the haystack. Augmented analytics helps find the needle faster by acting like a giant magnet hovering over the hay.”
(Gartner, Augmented Analytics is the Future of Analytics, Rita Sallam, Carlie Idoine, 30 October 2019)
Augmented Analytics is the necessary evolution of Data Analytics into a new approach to data analysis that integrates different AI techniques (machine learning, deep learning and NLP natural language processing, etc.), to make decision makers – not only AI specialists – better understand data and apply it in order to improve their business.
Augmented Analytics identifies purposive data (structured and unstructured) already present in the company, potentially adding and integrating new external data sources (Alternative Data) that are not traditionally processed. Once the data has been automatically and impartially cleaned and analyzed, it uncovers patterns and trends, identifies anomalies and predicts their causes. At the end of the process, it offers relevant and usable insights, proposing operational suggestions and different actions to take. The final decision is then left to the ‘human’ expert, whose intelligence has been in fact augmented by data processing.
Augmented Analytics is a fully scalable technology.
Data governance, enrichment and correct and efficient analysis will increasingly distinguish the companies that will be competitive from those that will go out of the market. This statement applies to all B2B and B2C companies, none excluded.
Despite the growing presence of Data Analytics systems, organizations have not been able to exploit their power to the fullest, resulting in a high rate of project attrition. Over time, data has grown in volume, become more complex and dynamic, and traditional Business Intelligence solutions have not been able to keep up. The shortcomings of Data Analytics that Augmented Analytics wants to solve are mainly those related to data extraction, management data mining, management difficulties, excessively long preparation times, problematic understanding and lack of data scientists. Not to mention that the data preparation, exploration and operation phases often used to be carried out manually.
Augmented Analytics is born to solve Data Analytics issues. Augmented Analytics makes data, insights, predictions and possible actions accessible and understandable by several people, not necessarily technical, at all company levels.
The future of analytics will be as simple to use and as accessible to everyone as Google search. This will shift advanced analytical power to the information consumer giving them capabilities previously only available to analysts and citizen data scientists. By 2025, augmented consumerization functionality will drive adoption of analytics and business intelligence capabilities beyond 50% for the first time, influencing more business processes and decisions (Source: Gartner, Top Trends in Data and Analytics, 2021).
“By 2020, augmented analytics will be a dominant driver of new purchases of analytics and business intelligence as well as data science and machine learning platforms, and of embedded analytics” Gartner Predicts
The term Augmented Analytics was coined in 2017 by the global research firm Gartner and is rapidly becoming an essential part of the future of all companies. Gartner today estimates that the Augmented Analytics market will reach $ 1.88 billion by 2022, with a 20.6% CAGR from 2017.
The Augmented Analytics guide makes companies more powerful, regardless of size and resources. Augmented Analytics augment decision making and results. Data is useless for a company unless the company is able to extract significant information from it. Many organizations, especially medium and small ones, are unable to extract relevant information and operational suggestions from the data. Without data scientists or resources available to interpret the data and transform it into commercial actions, the benefits of the data remain blocked. This situation changes with Augmented Analytics.
Thanks to AI, Augmented Analytics solutions look for models in the data or discover other valuable information without the involvement of data scientists. The analysis can therefore be made “speaking” and shared with the “humans”, ie the end users who thanks to the “machine” thus increase their intelligence. Reporting is easy to understand even for non-technical people.
To better understand, here is an example. You may have a range of data available indicating that your sales are down 25% year on year. But there are other questions that need to be answered before knowing what it really means. Is it because your marketing isn’t effective? Does it happen to others in your industry? Is the cause your product or your sales staff? So knowing just the data on the drop in revenue doesn’t make that information valuable to your organization.
A deeper “dive” is needed to find out what this really means. This deeper dive requires looking at information from multiple sources, cleaning up data, analyzing it without preconceptions, understanding the insights that have emerged and then communicating what could be done to improve sales. Augmented Analytics can also help predict a possible drop in sales before there has already been and suggest actions to avoid it.
Before Augmented Analytics, companies needed to hire data scientists or analysts to make sense of the data, and this was only financially possible for some. Even if the money were available to pay a data scientist, this could be difficult to find: the McKinsey Global Institute predicts that by 2024 there will be a shortage of approximately 250,000 data scientists in the United States alone. In addition, a significant part of what data scientists had to do (an estimated 80% of the time) was to collect data from various sources, label it and clean it up, which is a repetitive job that Artificial Intelligence can do quite easily and without errors. Gartner predicts that advances in augmented intelligence could make 40% of data science activities machine based by 2020.
In addition, data scientists rarely have a “business sense” to indicate the business action that must be taken from data analysis. Therefore, a salesperson with that “business sense” should work closely with the data scientist to acquire detailed information and transform it into business actions. And there is often no time to do this.
Augmented Analytics democratize data and allow all companies, regardless of their size, to extract meaningful information from multiple data sources, derive and propose intelligent business driving suggestions. Everyone can quickly get answers (“the vital shift from big data to big answers”).
Data scientists can rest easy: they will continue to be in high demand even with Augmented Analytics, no longer for repetitive data collection and preparation tasks, but for more strategic activities and special projects. Both in smaller and larger companies where Augmented Analytics will significantly speed up the projects that can start from already consolidated outputs.
More and more people (now named citizen data scientist) can be easily “empowered” by speaking data, which thus becomes part of their daily activities and decisions and not just reserved for data scientists in the strict sense and analysts. The data become an engaging and always active environment, which can also be interrogated vocally (conversational analytics). Intelligent assistants can inform decision makers when some positively or negatively anomalous data needs their immediate attention and reaction, as it happens, instead of waiting for it to be sought (see alerts of the Alternative Data Intelligence FinScience Platform).
Although softwares exist on the market to visualize and communicate the analyzes performed by data scientists to business decision makers, most of these tools do not analyze the data and noone proposes actions. Augmented Analytics are capable of doing everything. As Augmented Analytics applications see:
Augmented Analytics platforms will have increasingly relevant “social” components. Instead of creating reports and waiting to present them in a meeting, wasting precious time and slowing down the decision-making process, once the insights are identified, they will share them, tag other colleagues within the company and not only in their team, they will add notes and construct a broader narrative and above all they will be able to adapt the tactical or strategic transformation of the company or operations to these data, all within the platform.
Augmented Analytics systems will increasingly become productivity tools, efficiency amplifiers and revenue generators.