Augmented Analytics: What It Is And How To Assess A Potential Platform

CEO of Crux Intelligence and four-time founder/board member. Putting AI in the hands of every business user.

Today's business environment is dominated by a single topic: data. With each enterprise sitting on a potential goldmine of data from user behavior, sales, marketing campaigns and systems operations, company leaders are under pressure to derive as much value as possible from their first-party resources.

Many companies, however, have taken an under-informed and ill-leveraged approach to data and analytics, putting rudimentary systems in place without understanding what they do or how to maximize their performance.

In the past, effective data analytics required expensive, highly specialized talent to sift through mountains of raw data. However, a growing collection of augmented analytics tools have more recently evolved, making data-driven insights accessible to any business, regardless of whether they have the budget to field a large, experienced data analysis team.

What exactly is augmented analytics, and how does it help to democratize data-backed decision-making?

Augmented analytics, according to Gartner, "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."

As more companies begin to turn to augmented analytics to support their operations, what should they look for when assessing a new tool or platform? Here are five questions to ask before investing in augmented analytics technology.

Does it support your data sources and models?

Augmented analytics platforms are not one-size-fits-all. They vary widely in terms of the types of data they can ingest, as well as their ability to connect to different storage platforms. While the vast majority of platforms can ingest structured data, many platforms will struggle to process and categorize unstructured data. Before committing to a platform, your company must understand its own data assets and whether or not they will be compatible with augmented analytics.

Depending on the maturity of your existing data programs, you will also want to check whether an augmented analytics platform supports your data models and needs. More complicated deployments would involve knowledge graphs and multiple fact tables; high-performance augmented analytics tools will be able to integrate these models easily, while less developed tools will lack these capabilities. 

How much of the analytics functions are automated?

The essential value proposition for augmented analytics is that it automates many of the most tedious processes in data collection and organization. Any augmented analytics tool should be able to apply machine learning tools to a company's datasets in order to automatically generate actionable insights.

When choosing an augmented analytics platform, companies should work to understand what insights will be generated automatically and how those could inform business decisions. In some cases, platforms will automatically identify the most important aspects of a dataset; in others, the platform may proactively generate a nudge toward a certain process improvement.

 How does it display and communicate insights?

The most daunting aspect of data analysis for business leaders and citizen analysts is the unvarnished appearance of raw datasets in a single spreadsheet. Augmented analytics can overcome this by automatically displaying data in interactive charts and dashboards. Different tools will offer different functionalities, whether they're in the form of heat maps, standard bar charts or even geographic maps that match a company's physical locations. In some cases, augmented analytics platforms will go beyond visuals to deliver data insights in a narrative form; this storytelling feature can take much of the guesswork out of the analysis and make it easy for data employees to explain key takeaways to executives.

What are its natural language capabilities?

The more user-friendly an augmented analytics platform, the faster an enterprise will be able to find value. Natural language processing (NLP) allows the end user to either speak or type a query using typical business terms; the platform does the work of converting your language into terms understood by the algorithms. NLP is undoubtedly the most effective tool for democratizing data insights, as it removes the need for any prior knowledge before using the analytics tools.

In addition to NLP, natural language generation (NLG) can create rich, readable descriptions of the insights generated from the data. Advanced NLG functions can adapt in real-time: As the user explores or interacts with the datasets, the NLG narratives will change to clarify the key takeaways of each set.

Is it secure and easy to manage?

Finally, and perhaps most importantly, an augmented analytics platform must protect your sensitive first-party data assets at all times. Before choosing a specific tool, your business must do its due diligence to ensure that security needs are being met. What authentication tools are used to administer access to the platform? How are users onboarded and can their access be limited to only essential functions? In today's threat environment, an augmented analytics platform should support encryption keys and multi-factor authentication — the most effective current tools for safeguarding company data. This is particularly important as data and data users become more mobile, in turn increasing the opportunity for breach.

In addition to meeting the demand for more mobile access (and security), companies must also check to make sure that they will be able to easily manage and administer their augmented analytics platform, ideally from any location. How easy is it to track usage of the platform's tools, and are they compatible with other third-party solutions? The right augmented analytics platform will become part of your daily operations, while a platform that is difficult to use could end up becoming shelfware.

In a marketplace defined by data, the choice of an augmented analytics solution could make the difference between success or failure. Putting in the time to research, compare and understand the different capabilities offered by competitive platforms — and how they match up against a particular company's needs — will help your business to feel confident in its investment of time, money and resources.

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