Technologies like augmented analytics put a growing emphasis on freeing up human efforts for more creative and neural tasks.
Analytics and business intelligence (BI) has grown in prominence over the decades to enable businesses to leverage the power of data to spot gaps in the market, move first and capitalize on them. Augmented analytics can help more organizations take advantage of these benefits.
Up until a few years ago, not many companies were using data-led decisions, so even basic application of data analytics and BI practices were resulting in a fresh perspective, helping companies to look at things differently.
See also: Why Business Intelligence is a Big Deal
But as time passed, companies started to find it rather straightforward, at least while using the vanilla versions of the platforms, which is an occurrence when users lack the technical know-how of analytics platforms. The result: struggling adoption due to the low value of insights generated.
This continues to be a primary concern even now because the end-users of these platforms are senior business users, and they neither have the technical know-how nor the time to slice and dice through the barrage of data generated from day-to-day business operations. They rely on data analysts or the MIS team to do the number crunching for them. But there is a catch. The analysts lack business understanding, and that at many times results in a not-so-comprehensive reporting as per the liking of the business decision-makers.
Augmented Analytics to the Rescue
Augmented analytics nails these problem areas and offers much more. It allows the end-users, typically the non-technical users, to get automated reports well-designed for their persona, business unit, or area of work at specified intervals.
The recent Gartner’s Magic Quadrant for Analytics & BI Platforms 2021 rightly points out that data visualization will get commoditized, and the focus will shift towards augmenting analytics to fuel adoption. If the same Gartner report is to be believed, augmented analytics will push the adoption of Analytics & BI platforms beyond the 30% mark, which speaks volumes about the potential it possesses for enterprises.
Having said that, people often misconstrue augmented analytics as being just an extension and automation of data analytics and BI. But that is not true. While it definitely automates mundane and repetitive tasks, it also allows users to look at data as seasoned data scientists would. It is a system enabled by emerging technologies and human inputs that learns, gets trained, and gathers intelligence gradually to reduce reliance on human inputs for similar actions. So, it doesn’t replace the human effort but rather eliminates their constant monitoring for routine activities, freeing up time for human intelligence in unexplored areas.
Now let us look at these transformative technologies working behind the scenes and in what way they deliver faster and accurate insights.
1) Natural Language Processing and Querying for Conversational Insights
It eliminates the need for putting complex parameterized queries and codes to find the answer to complex questions. Users can key in queries in conversational lexicons, and the NLP helps to decode it, followed by appropriate modeling and data processing to automatically present insights.
After each user interaction, it also accepts their feedback on the automatically served reports and learns from their interactions/ behaviors to self-improve each time and present better and context-aware interpretations of data.
2) Machine Learning to Prescribe the Best Course of Action
Machine learning adds some significant value on the prescriptive analytics side. It provides recommendations based on the defined KPIs and helps analyze multiple scenarios or output possibilities against different courses of action. Consumers of augmented analytics rely on these machine-led insights to avoid overlooking any crucial business metrics.
These two technologies add great value on the user-interaction side and truly hold the key towards improving end-user adoption.
3) Artificial Intelligence (AI) to Build a Solid Data Analytics Foundation
AI in augmented analytics comes into play right when the data is coming into the users’ environment and how they interact with it. As per this Euroclear report, enterprises still use about 12% of their data for business decision-making. So, what happens to the rest 88% of the data? It goes unnoticed, is unused or unmined.
AI operates to identify these untapped data sources and make schemas suggestions to further enrich the data used for analysis.
In a nutshell, these technologies get into action right from data collection to unearthing the hidden data to parsing and, at the end, presenting it for consumption in the most meaningful way possible for the business users.
In an increasingly technology-reliant world, human intelligence is still a defining commodity, and it doesn’t seem to be changing anytime soon. Technologies like augmented analytics put a growing emphasis on freeing up human efforts for more creative and neural tasks. We still need to go very far in the field of data and decision science. The destination would be instilling confidence to a point where data-driven decision-making would feel effortless. It would be as naturally ingrained for business decision-making as reading Amazon reviews of a product before making the purchase or using Google Maps to navigate even in a city you have lived in for years.