The Modern Enterprise’s Introduction to Augmented Analytics
Gartner predicts that decision intelligence will be at the heart of more than 33% of all MNCs by 2023. Analysts practicing the same will be developing highly effective data-driven models on the back of technologies like AI, ML, NLG, NLU, and NLP.
In fact, more than 75% of the enterprises would no longer define these technologies as pilots or proofs-of-concept. They will be made a part of the mainstream IT systems that, in turn, will increasingly become intertwined with the core business processes.
All of this will happen because augmented analytics will have created a paradigm shift in the AI and data science professions. Data is more important than ever before. But, more importantly, it’s becoming easier to collect and access the data that matters the most.
In the past, you had to focus on a phased approach – a one-off, single-purpose data crunching – which delivered results that would only be relevant to a particular department or a subset of your business. You would then have to re-link or retrofit the insights from that single data set to the wider context of your business.
Augmented analytics has changed all that. You can build influential, in-depth observations in minutes (even seconds) and not days or weeks. It’s fast, agile, and with inbuilt transparency. And most importantly, it’s immediately actionable for your business. That said, let’s take a closer look.
Augmented Analytics — A Primer
First defined in 2017 by Rita Sallam, Cindi Howson, and Carlie Idoine (the three analysts from Gartner), the term Augmented Analytics puts forth the case for the democratization of data analytics.
In concrete terms, augmented analytics combines data with domain knowledge and AI/ML to create actionable insights for an enterprise. Instead of a one-off, a one-shot approach, it allows for faster deep insights’ generation from the data with appropriate feedback — all within a defined scope, thus, supporting the achievement of an enterprise’s KPIs.
This is primarily the reason why it seems to be an event-based approach that heads deeper and prevents going wider.
One of the best examples of augmented analytics at work is the way IBM leverages augmented intelligence to advance the financial sector, in general, and the banking system, in particular. Through the facilitation of an “evidence-based, probabilistic system,” IBM is able to empower virtual agents with real-time comprehension and reasoning capabilities. Not to mention that the augmented intelligence systems possess the capability of reading through more than 800 million textual pages and reporting on the adherence to regulations or compliance or the need to augment specific clauses.
What’s in Store for a Modern Enterprise?
Kathy Leake, the CEO of Crux Intelligence, argues that many companies take an “under-informed and ill-leveraged approach to data and analytics.” As a result, they commit the blunder of “putting rudimentary systems in place without understanding what they do or how to maximize their performance.”
On the other end of the spectrum, there are those who take a much more proactive approach to analytics. They try to overcome data silo-ization with scale. In their pursuit to avoid that, they end up throwing a ton of data at conventional analytics without having a proper overarching framework or even an understanding of what they might be looking for.
In both cases, the enterprises aren’t able to effectively tame the vast data supply chain, and it’s precisely this inefficiency in leveraging data that directly impacts a business’s decision-making processes.
Augmented analytics addresses the above issues and more by ensuring real-time data integration through the deployment of intuitive, visual data exploration and analytics tools.
Here’s how:
1. It Puts the Power of Knowledge at Your Fingertips
Whether it’s through the augmentation of your IT systems through AI or the deployment of natural language generation, augmented analytics is able to leverage knowledge. For example, through analytics software, you can easily find statistical or graphical answers to simple business queries like “What sales were made through the call center to customers who called in seeking the resolution of that issue?” and “How much did we make through our credit card sales through this specific promotion to that particular type of customer segment?”
2. It Dramatically Decreases the Dependency on On-Premises Technology
You don’t need to invest in expensive, formal IT infrastructure to create an augmented analytics approach. The technology favors a cloud-based approach to data storage and retrieval and is well-equipped to process all data via cloud applications. This enables the data to be instantly available, thus giving rise to an event-driven approach that can run in conjunction with constant monitoring of live data streams.
3. It Eases the Incineration of Unnecessary Data
In an age where companies worldwide are enacting strict data-protection measures, there’s a heightened need to have a systematic approach to eliminate unnecessary data. This is exactly where augmented analytics comes in and brings about the much-needed level of transparency. With augmented analytics, you can deliver instant insights to all relevant stakeholders — whether it’s the management, operations, or the legal department — and ensure that no data is being unnecessarily stored or shared.
4. It Promotes the Empowerment of Data Ownership
By virtue of augmented analytics, you are guaranteed to retain power over your data. It gives you the ability to control your technology, thus allowing you to regulate how it’s deployed and implemented. Furthermore, it supports deploying the right type of technology with the right level of flexibility to deliver actionable insights.
5. It Removes the Sting of Data Silo-ization
As mentioned above, there’s no need to worry about data silo-ization with augmented analytics. Simply put, it fosters the deployment of a set of tools that are sensitive to the needs of modern business leaders.
6. It Personalizes the Analysis
Through its ability to parse through text, augmented analytics makes it really easy to identify relevant content – the aforementioned IBM augmented intelligence system is a case in point. Such a tool ensures the extension of relevant contextual details, thus allowing for a more personalized analysis. On the same note, it allows for intelligent automation of repetitive tasks to ensure the business process remains agile.
In a Nutshell
Augmented analytics is chalking a new chapter in enterprises’ data-driven analytics approaches. It aims to achieve a balance between humans and machines to advance decision intelligence. With the availability of real-time data that’s enriched with user-friendly tools and interfaces, you can be assured that analytics is moving forward at a pace that will enable your enterprise to stay ahead of the competition.
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