How Augmented Analytics Tools Will Change The Way We Work

There are plenty of business intelligence platforms in the market, and as of late, we’ve seen more and more augmented analytics tools popping up. That aside, augmented analytics has also been lauded by several leading research firms and companies. Gartner, for instance, says that this particular approach is the “next wave of disruption in the data and analytics market”. 

If you’re not 100% sure what augmented analytics entail, you’re in the right place. In this guide, we walk you through all you need to know about augmented analytics, including:

• What is augmented analytics
• The three ingredients of augmented analytics tools
• How augmented analytics tools can help you free up 40% of data scientists’ time 
• How to use augmented analytics tools to empower your teams

What Is Augmented Analytics?

Simply put, 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.

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 work, and why they’re essential to business teams. 

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, a machine can optimize their own performance through repeated usage.

For example, say you have a dataset comprising of 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, but the more you test the algorithm, the more its accuracy improves. 

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.

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 or tool to convey insights to them, such as: “Sales in Country A have increased by 20%”.

NLP doesn’t just help tools transform intangible algorithms into insights that are easily comprehensible; they also allow team members to surface questions to their data platforms and get relevant answers. For example, tools such as Wonderflow apply natural language to their search functions, meaning that an employee can ask a question such as “How much did sales increase from one year ago?” and receive an answer to their question.

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 (as compared to that of their competitors’).

Today, many companies are still relying on manual methods to analyze their data and uncover insights, but as we discuss later in this article, this simply isn’t feasible. 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 how their company is currently faring. 

Freeing 40% of Data Scientists’ Time 

Sceptical about how much time augmented analytics tools can save your company? Gartner predicts that over 40% of data science tasks will be automated by 2020, resulting in increased productivity and broader usage of data and analytics by citizen data scientists.

More specifically, Gartner says that citizen data scientists can “bridge the gap between mainstream self-service analytics by business users and the advanced analytics techniques of data scientists”. These scientists are now able to perform sophisticated analysis that would previously have required more expertise; this enables them to deliver advanced analytics even if they don’t have specialized skill sets that data scientists do.

To be clear, these automations won’t just benefit data scientists and data teams. Gartner defines a citizen data scientist as a person who creates models that use advanced diagnostic analytics or predictive and prescriptive capabilities, but whose primary job function is outside the field of statistics and analytics. If you’re, say, a sales manager who’s tasked with putting together reports for board meetings, you can also skyrocket your productivity with augmented analytics tools.

Image source: Gartner

Interestingly, Gartner also predicts that citizen data scientists will surpass data scientists in the amount of advanced analysis produced by 2019. According to Gartner, businesses today are increasingly being powered and impacted by analyses produced by citizen data scientists. This doesn’t just create a more data-driven and analytics-driven environment, it also allows data scientists to shift their focus onto more complex analysis.

As Joao Tapadinhas, research director at Gartner, puts it: “Most organizations don’t have enough data scientists consistently available throughout the business, but they do have plenty of skilled information analysts that could become citizen data scientists.” Tapadinhas goes on to explain that citizen data scientists who are equipped with the proper tools have the potential to perform intricate diagnostic analysis and create models that leverage predictive or prescriptive analytics. 

How Augmented Analytics Tools Will Empower Your Teams 

How, exactly, can you use augmented analytics tools to empower your teams? Here are a few use cases of augmented analytics that can help to boost your team’s productivity: 

1. Automated data cleaning and compilation
2. Identifying patterns and trends in key metrics
3. Tracking actions and strategies to identify effective approaches

Automated data cleaning and compilation

Most companies used to compile their data on a monthly or quarterly basis, and you can see why this would be a problem. By the time data is available for management to access, it’s typically a few months old, and this makes it hard for management to refine their decision-making process using said data. 

These days, however, technologies such as SQL databases and centralized CRM systems allow companies to access their data in real-time. The problem, though, is that real-time data is still expensive and time-consuming to acquire and compile. Think about it: most companies have several sources of data, including their advertising platforms, their website and other assets, and their point of sale systems.

As you may imagine, the data collected via these platforms are stored separately in different formats. If you’re content to look at your data in silos, that’s perfectly fine. But in many cases, managers find that they need to combine their data in order to track their key metrics or KPIs. 

Here’s an example: to extract information about your Return on Investment (ROI) or Return on Ad Spend (ROAS), you’ll have to pull data from two different sources — cost data from advertising platforms, and revenue data from your point of sales. To do this, developers and data scientists need to work together to develop a measurement framework that can connect the data and integrate them into a single data sheet.

Now, that’s where augmented analytics come in. Augmented analytics tools basically make the integration process a lot simpler — more specifically, they allow teams to seamlessly integrate their data with a few clicks of a button. This doesn’t require any technical expertise or coding knowledge whatsoever.

Identifying patterns and trends in key metrics

Once companies clean and compile their data into a single source, the next step is to identify patterns and trends that they can use to unlock actionable insights. Often, the more data you have on hand, the harder it is to do this.

For example, say your compiled data comprises of 10 key metrics and 20 dimensions. (This is pretty standard for a small-sized company, but if you’re running a larger organization or an enterprise business, you might be dealing with anywhere from 10-30 metrics and up to 100 dimensions).

Now, to slice and dice just ONE metric across 20 different dimensions would mean that your data scientists will have to run 20 different analyses. When we combine different dimensions to conduct multi-dimensional analyses, the number of analyses that are needed increases exponentially.

Unless you’re employing a huge team of, say, a hundred data scientists, it’s simply impossible for them to run enough analyses for you to cover all your bases. To work around this, many companies rely on analytics heuristics, which basically means that they only conduct analyses for scenarios that scientists think are important. 

Now, it doesn’t matter if you’re employing some of the most brilliant scientists in the world — if you’re using the heuristic approach, this will make it hard for you to look at the big picture, plain and simple. At the end of the day, you’re relying purely on the intuition of your scientists with heuristics, and regardless of how capable or experienced your scientists are, they won’t be able to predict with 100% accuracy what’s important or less important to analyze.

Don’t want to lose out on important insights? Instead of working your data scientists to the bone, use an augmented analytics tool to conduct all the analyses you need. These platforms let you rapidly analyze all your data across all possible dimensional combinations in record time, and hone in on the key factors that are helping your company grow (or doing the opposite).

Tracking actions and strategies to identify effective approaches

After you’ve identified patterns and trends in your key metrics and come up with actionable insights based on these trends, the next step is to execute your insights and strategies and track your performance.

Again, tracking proves to be a huge headache for most companies. Many managers are too caught up in fire-fighting and attending to their day-to-day tasks; they simply don’t have the time to sit down to track and review their strategies or evaluate whether these strategies are working. 

On top of that, managers also find working with data and tracking new strategies confusing. For instance, if you implement a new strategy, and you see a quick change in your data (say, a spike in conversions in just three days), does that mean you can confidently say that your new strategy is working? Most managers take this to be the case, and stop tracking their data prematurely — but the right approach is to wait until a large enough sample size has been collected, instead of stopping a certain campaign or experiment early. 

Now, with augmented analytics tools, managers no longer need to make (often confusing) calls about how to go about tracking their strategies. Augmented analytics tools help to automatically track actions taken within a business, and these tools also crunch all the numbers to deliver scientifically backed evaluations of whether a particular action or strategy is working.

Let me know what you think!

Cheers, Bram.