Ever wish you had a magic crystal ball to predict positive business outcomes?
Well, we can't promise an actual crystal ball, but augmented analytics comes pretty close.
No idea what augmented analytics is?
It capitalizes on enabling technologies like AI and machine learning to enhance data analytics, thereby enabling you to make smarter, data-driven decisions.
But that's just the tip of the iceberg.
Keep reading to learn more about augmented analytics, best practices for implementation, and how it could help transform your organization to deliver business outcomes.
What is Augmented Analytics?
Augmented Analytics, or AA, is the use of enabling technologies like machine learning (ML), artificial intelligence (AI), and natural language processing (NLP) to enhance how people explore and analyze data.
The term was first coined by Gartner and described as the future of data analytics in a 2017 research report.
And by the look of things, it indeed is the future of analytics.
It enhances already powerful technologies (data analytics and business intelligence), making it one of the most valuable solutions any business can implement.
Find out how it works below.
How Does Augmented Analytics Work?
Want the simplest way to understand how augmented analytics works?
First, you must understand its two core components: machine learning and natural language processing.
Machine Learning (ML) is an AI subset focused on teaching machines to learn from data and generate meaningful predictive insights without human intervention.
Natural language processing, commonly known as NLP, is also an AI subset but focused on enabling technologies to process and respond to spoken and written words the same way as humans would.
For example, thanks to its natural language generation, your favorite gadget, Siri, can listen and respond to your song requests on the fly.
Now, augmented analytics uses machine learning algorithms to enhance and simplify complex data analytics processes like data preparation and uses natural language generation to generate meaningful yet conversational analytics.
Simply put, augmented analytics humanizes data analytics and related technologies through machine learning capabilities, making it possible for anyone, including non-tech savvy employees, to generate valuable insights from raw and unstructured data. This leads to faster data preparation and analytic processes.
Some of the augmented analytics capabilities the technology uses to enhance business intelligence and data analysis processes include:
Automated data profiling for hands-free data cleaning, enrichment, and manipulation
Auto-detection to eliminate repetitive tasks
Statistical operations help explain the why and how behind each data point
AI-driven recommendations spanning from data prep and analysis to sharing
Natural language processing allows all business users to run data queries and generate conversational analytics and informative data visualizations.
Why Do You Need Augmented Analytics?
Here are some benefits of augmented analytics, proving that implementing it is one of the best business decisions you'll make:
Augmented Data Preparation
Traditionally, data preparation involves gathering data from multiple sources, cleaning it, and integrating it with relevant organizational technologies. It's a heavily manual and incredibly time-consuming data analysis process.
Augmented analytics takes the manual work out of the equation by using machine learning and natural language generation to automate critical data preparation processes.
For instance, augmented analytics platforms automate the necessary but mundane data preparation steps such as joining schemas, allowing data analysts to focus on strategic tasks.
Machine-learning algorithms can also automatically integrate multiple data sources instead of traditional time-wasting data harmonization techniques. This leads to faster data preparation and speeds up the entire data analytics process.
The result?
A speedy data analysis process means insight generation occurs and is delivered to the right teams on time, leading to smart, data-driven enterprise planning and, ultimately, business success.
Smarter Enterprise Planning and Decision Making
Remember that magical crystal ball you wanted earlier? Well, augmented analytics is sort of one.
Why, you wonder?
To begin with, it's not biased. Unlike you or me, it has no assumptions that may influence results.
Instead, the technology uses ML algorithms to thoroughly analyze large raw data sets and find relevant data points.
This results in more profound, highly accurate, valuable insights and means no more missing important data points. If anything, you may find connections you may have otherwise missed using traditional approaches.
And with deeper, more accurate insight generation, you can now make smarter business decisions and predict outcomes. It's sort of a magical crystal ball because you can look to it when you need to predict the viability of a potential business plan.
Data Analysis Democratization
Approximately 2.5 quintillion bytes of data are generated every day. This is expected to grow even more as Artificial Intelligence and Internet-of-Things IoT technologies become more prevalent.
Every organization looking to stay agile and achieve enterprise intelligence through unified data must ensure that its users have access to the right data and know how to utilize it to its full advantage. A few years back, that would have meant huge training budgets, but thankfully, augmented analytics tools are changing things.
Today, users can leverage conversational analytics anyone can understand. For instance, predictive insights often include editable data visualizations like graphs and maps, meaning that users don't need to create them manually, unlike in the past.
The outcome?
They save time and become much better at working with data, which leads to quality insight generation and business reports.
Besides making data more approachable for the average employee, augmented analytics promotes organizational data literacy.
Thanks to Artificial Intelligence (AI) and Machine Learning, AA solutions can guide an average employee through the steps to take during a data analysis process and offer AI-driven recommendations on questions to ask or even data points to focus on.
Continuously doing this improves the employee's tech and data literacy which is an advantage for your business.
Reduces Operational Costs
Traditional data analytics relies entirely on data analysts and citizen data scientists. They have to manually handle data collection, preparation, analysis, and interpretation from delivery to use of downstream analytic solutions.
Consequently, your business must hire several data analysts to speed up business intelligence and analytics. This, however, only adds to the operational costs.
You can avoid these costs by adopting the right augmented analytics tools. They automate the process, meaning one person can perform steps previously dependent on a couple of heads. What’s more, an augmented analytics platform doesn’t just automate, it augments data, making exploration much faster.
As a result, you can reduce your data analytics team to just one or just enough data scientists to decrease operational costs without sacrificing data efficiency.
Augmented Analytics Best Practices
Here are some augmented analytics best practices to ensure successful adoption and usage across the entire data chain in your organization:
Take Data Chain Value Users' Complaints into Account
Adopting augmented analytics in your business will impact entire business processes, including those where stakeholders and external partners might be involved.
Unfortunately, some of your employees and partners may have preconceived notions about AA that may negatively impact the rollout. For instance, if one business associate perceives AA as challenging to use, they may influence others, making adoption a nightmare.
So, determine what the direct and indirect team members say regarding AA before developing an adoption strategy. This will let you address these concerns before adoption, ensuring successful implementation.
Understand Your User Personas
A poor fit between an augmented analytics solution and the end user is among the leading reasons for failed or poor adoption.
You might invest in a premium augmented analytics solution, but if your data analysts find it too inflexible, they might have problems generating deeper insights.
Similarly, your target analytics solution might be the best, but even your best data scientist won't use it effectively if they think it's too complex.
So, research and understand your user persona profiles before adoption. You will find that you have:
The data consumers: the employees who'll use and interact with data visualizations and reports but rarely, if ever, modify them
The explorers: these are like data consumers but will occasionally modify reports and dashboards to suit a particular purpose
The data analysts
The data scientist
Understanding these user personas will help you select an augmented analytics software solution that best fits everyone in your business, streamlining adoption and ensuring continuous use.
Provide both Formal and Informal Coaching
Once you understand your team's personas and concerns regarding AA adoption, provide formal and informal coaching to address these issues. Training will also equip them with the data literacy they need to leverage AA.
So, organize a formal adoption training program where all potential business users are introduced to and comprehensively trained on how to use the solution you're about to implement.
But remember, augmented analytics technology is consistently changing and becoming better.
So, create avenues for continuous informal AA training in your business as well. For instance, regularly organize webinars and Zoom sessions where data analysts from your team can discuss their challenges, new augmented analytic best practices they've discovered, and emerging AA trends.
Make it as easy as possible for the data consumers (average employees) to access and get support from your organization's data explorers and data scientists. If possible, create an AA help desk where average and skilled AA users can reach out for assistance and informal coaching.
In a nutshell, make AA support accessible for all to create a data-literate workforce and facilitate the successful adoption and continuous use of augmented analytics solutions.
Prepare Your Business Data for Augmented Analytics
As noted, augmented analytics uses AI and machine learning to learn from historical data before extracting relevant and related data points. As a result, its efficiency and accuracy heavily rely on the information you feed into the algorithm and your intent.
That said, classify your data for easier analytical efforts using AA.
For instance, if you're looking to understand why sales are declining, you need to look at existing marketing data and sales reports. But if your goal is to predict outcomes from potential enterprise plans, transactional data is your best bet.
So, while training is ongoing, categorize your data types to make it possible for everyone, including the average team player, to leverage the power of augmented analytics without assistance.
Gradual AA Rollout
Most businesses wait to implement augmented analytics because they don't have enough data or believe what they have isn't enough for successful data visualization. Others embed AA systems into all business processes, making it overwhelming for their teams.
You don't want to be in either category because the results are always unsuccessful and, in turn, disappointing and costly.
To ensure successful implementation, you need to roll out AA gradually. Instead of dropping the bombshell on the entire organization, beta test your AA solution with one group before widespread adoption.
For example, test its features with a target group's leaders to determine what they like and don't like. This way, you can work with the AA solution vendor to improve the features they don't like before deploying the software to the rest of the group.
Gradual AA Rollout
Most businesses wait to implement augmented analytics because they don't have enough data or believe what they have isn't enough for successful data visualization. Others embed AA systems into all business processes, making it overwhelming for their teams.
You don't want to be in either category because the results are always unsuccessful and, in turn, disappointing and costly.
To ensure successful implementation, you need to roll out AA gradually. Instead of dropping the bombshell on the entire organization, beta test your AA solution with one group before widespread adoption.
For example, test its features with a target group's leaders to determine what they like and don't like. This way, you can work with the AA solution vendor to improve the features they don't like before deploying the software to the rest of the group.
While at it, keep in mind your data doesn't have to be perfect or available in large volumes to implement predictive analytics. It just has to be aligned with your key performance indicators to get started with AA.
Set up effective communication systems to foster collaboration
Augmented analytics systems don't work alone. They're integrated with other software and embedded into workflows and business processes.
In that light, you need to eliminate data silos and encourage organizational collaboration to ensure everyone successfully adopts, actively uses, and has access to these new technologies.
You’ll achieve this by setting up downward and upward communication channels that allow everyone, from the data scientists creating predictive analytics models to the data consumer, to easily collaborate on AA-influenced projects.
Implementing Augmented Analytics
Augmented analytics is the magic crystal ball every business needs to scale in this highly competitive and ever-changing business world. And judging from the rising value of the global AA market due to the increasing adoption of augmented analytics tools, it seems businesses globally have already grasped the fact.
You, too, need to join the bandwagon.
You are on the right track by familiarizing yourself with what augmented analytics is and how it relates to technologies like machine learning, the benefits it brings to the table, and best practices for implementation with our guide above.
However, you need to do more than familiarize yourself. It would help if you adopted the right solutions as soon as possible to ensure accelerated insights and improve decision-making by leveraging augmented analytics.
Don't know where to start? Or do you need more help aligning these technologies with your business use case? We can help. Contact us to learn how or request a live demo here.