How Augmented Analytics Tools Will Change The Way We Work

September 28, 2019

So…What Is Augmented Analytics? Augmented analytics refers to the use of statistical
and linguistic technologies to improve data management performance. This includes everything from data analysis
to data sharing and business intelligence. The concept of augmented analytics is closely
linked to the idea of transforming big data into smaller, more usable, datasets. But if you’re wondering whether augmented
analytics can replace data scientists and data teams altogether, that’s definitely not
the case. Think of augmented analytics as playing an
assistive role — this technology doesn’t replace humans; it supports us and enhances
our interpretation capabilities. So which are the three Ingredients of Augmented
Analytics Tools Machine learning, Natural Language Processing
(NLP) and insight automation form the backbone of augmented analytics tools. In this section, we discuss how each of these
technologies works, and why they’re essential to business teams. Let’s start with Machine Learning Machine Learning is defined as “a field of artificial intelligence that is based on
algorithms that can learn from data without relying on rules-based programming”. In other words, the machines can optimize
their own performance through repeated usage, which can be supervised by humans, or not. For example, say you have a dataset comprising
photos of dogs and wolves, and you build a machine learning program to process the data
and categorize the photos. The program first processes the data to identify
patterns between the images; next, it builds an algorithm based on the patterns and uses
the patterns to identify the images. When you first run the algorithm, the program
might categorize some of the pictures inaccurately, so, for example, it could say that a small
dog is a wolf, by mistake, but the more you test the algorithm, the more it will become
precise. In the business world, machine learning has
a ton of implications, and can be used across virtually all departments and functions. In sales, for instance, you could build a
machine learning program to automatically qualify and score leads. To improve your marketing, you could build
a machine learning program to identify the exact point in time where each customer is
likely to churn, then retain your customers using highly-targeted messages. The second ingredient is Natural Language
Processing NLP is a branch of artificial intelligence
that helps computers understand, interpret and manipulate human language. NLP can also do the opposite, which is to
translate a machine’s key findings into phrases that humans can understand. In the business world, NLP empowers non-techie
managers and employees to get the most out of their data. Firstly, NLP makes the output of data analysis
a lot more straightforward. Instead of having to analyze the data in the
system and make sense of it, managers can simply wait for their data platform to convey
insights to them. NLP doesn’t just help tools transform intangible
algorithms into insights that are easy to comprehend; they also allow team members to
surface questions to their data platforms and get relevant answers. At Wonderflow we worked on this for some years
already. The last ingredient is Insight Automation Last but not least, augmented analytics tools deliver automated insights to teams, allowing
teams to assess their performance and brand health, identify opportunities for growth,
and benchmark their current standing against competitors. Today, many companies are still relying on
manual methods to analyze their data and uncover insights, but as we discuss later in this
video, this isn’t feasible anymore. Why is this the case? Companies who undertake data analysis manually
have to choose between cost and quantity; they either incur hundreds of thousands of
dollars in cost in order to churn out these analyses, or they narrow down their scope,
thereby delivering a not-quite-complete picture of the performance of the company. Do you want to know how to free 40% of your
time? I’ll explain it in the next video. Subscribe to the channel, hit the bell, and
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