Guide to Augmented Analytics: Everything You Need to Know

Vast amounts of business data are generated every day, but that doesn’t mean the information is immediately accessible or valuable. Raw data often exists in different forms and across numerous platforms, systems, and sources. It must first be centralized, cleaned, and transformed to become useful to your company.

In the past, only teams of engineers, scientists, and analysts could turn raw data into actionable information. This kept executives, managers, and other team members out of the loop. Modern technology, including machine learning and AI, now assists in this data analysis process, making it more efficient and giving employees with less technical experience increased access to critical business information. 

Learn how augmented analytics can help speed up your data analytics process, ensure greater accuracy, and empower your entire organization to make data-informed decisions.

What is Augmented Analytics?

Augmented analytics uses technology like AI, machine learning, and natural language processing to help process, prepare, and present data. Why is this important? Modern business data is complex—coming from multiple sources such as websites, search engines, business transactions, social media sites, marketing campaigns, and customer relationship management (CRM) systems, to name just a few. 

Your business data may be structured or unstructured, which can make it difficult to combine, interpret, or easily understand without an experienced data or IT team. Traditional analytics steps such as data preparation, processing, and analysis are left to those with the required technical skills of data scientists, analytics engineers, and data analysts. 

In contrast, augmented analytics uses technology to assist or completely automate these steps, allowing more business users access to data to discover impactful patterns, trends, and other insights through self-service analytics tools. Now, companies of all sizes can use data analytics to inform and drive business decisions more efficiently, regardless of their industry or the tech skills of their workforce.

How Does Augmented Analytics Work?

To learn how augmented analytics fits within your data lifecycle, you need to understand both the initial definition of augmented analytics and the technology that powers its processes. 

Augmented analytics uses artificial intelligence (AI) to enhance analytics techniques and processes; it doesn’t replace the human element in analytics work. It allows for faster, more effective data analysis but still relies on a user’s guidance, context, and behavioral cues to work. 

While augmented analytics does provide insights and recommendations, the actual decision-making is left to business users.

Augmented analytics tools include components like:

• Artificial intelligence: AI can quickly analyze large, complex data sets and automate analytics tasks such as data transformation. Augmented analytics integrates AI throughout each step of the process, streamlining data collection, cleaning, transforming, and analysis so you can develop accurate, meaningful insights more efficiently.
• Machine learning (ML): This is a type of AI that uses existing data and algorithms to analyze data, spot patterns or deviations, and generate predictions without computer programming. ML continuously learns and improves with access to more data and can handle structured and unstructured data on its own. It can help detect data abnormalities and irregularities that may indicate fraud and perform predictive maintenance to optimize your infrastructure or equipment.
• Natural language technology: Augmented analytics depends on natural language processing (NLP) to turn human language into a form computers can understand and use. It also uses natural language generation (NLG) to translate computer code into plain language. This technology lets users engage with an analytics tool through regular, conversational language and questions instead of computer code.
• Automation: AI and ML technologies automate many routine analytics tasks, including collecting, cleaning, and combining data across various sources. This not only speeds up the process but also makes detecting errors and abnormalities in your data easier. With automation features, non-technical users can explore and understand valuable business data and generate reports on their findings.
• Data visualization: AI-driven visualization tools like dashboards lets users see and explore their data more easily. Interactive charts and graphs can update in real time and make it easier to interpret and share the data’s story with others.

What Are the Benefits of Augmented Analytics?

The most effective augmented analytics tools use AI, machine learning, and natural language technology to remove barriers to data analysis. This makes the analytics process faster, more accurate, and more rewarding. Your business can see and use data in new ways to evaluate performance, optimize operations, or drive growth.

Increased Efficiency

Augmented analytics accelerates the analytics process and helps you develop insights more efficiently in several ways. First, it reduces or eliminates the tedious, time-consuming work of preparing and transforming data. Second, ML technology uses your cues to discover and surface data or trends relevant to your role or responsibilities, saving you time and effort combing through extensive data sets. Lastly, automation and data visualizations help you stay on top of fast-moving data and develop or change strategies to optimize business operations or meet your goals.

Greater Data Accessibility

Businesses collect massive amounts of data from everyday operations that contain key information about customer behavior, sales, marketing campaigns, and other business performance factors. Previously, only data scientists and analysts had the required skills to access and gain value from this data. 

Augmented analytics opens this data up to everyone, making it easier for sales or marketing teams, managers, executives, and other stakeholders to access, interpret, and make data-driven decisions. This, in turn, helps your business build a data-driven culture so that everyone in your organization can develop and meet goals that grow your company.

Reduced Bias and Errors

Most businesses have so much data that it’s nearly impossible for human analysts to handle the data on their own without problems. When non-experts collect, clean, or transform data, they’re prone to human error or mishandling it, leading to inaccurate or unreliable analytics findings. 

Augmented analytics lessens these problems and ensures you see a comprehensive view of your data, surfacing information you may not locate yourself. This is critical for developing accurate analyses, and it combats confirmation bias among users.

Deeper Insights

Augmented analytics tools are often intuitive and make data approachable to more users. They can be tailored to specific workflows, integrated with other platforms, and connected with additional software to provide a complete picture of your data. These tools give you deeper insights and, when paired with ML and natural language tech, can help suggest questions or identify patterns beyond what traditional analytics can offer.

More Accurate Predictions

AI and ML features of augmented analytics can predict future outcomes by using patterns in your current and historical data. Each user interaction with data improves ML through increased context and intent of your business data. This means augmented analytics provides more accurate and relevant insights over time, helping to further increase trust and company-wide adoption of analytics. 

What Are the Challenges of Augmented Analytics?

While augmented analytics offers many advantages, your business may face challenges when introducing or using these tools. Key problems to be aware of are:

1. AI and ML Misconceptions

Many people are still unfamiliar with AI and machine learning, and some fear being replaced by the technology. It’s critical to educate your company’s workforce about how you will use ML and AI to amplify their work and make their roles easier. Explaining augmented analytics’ benefits and how your company will implement them is key to building trust and eliminating misconceptions.

2. Finding Balance and AI Limitations

Others may believe that AI and ML can handle tasks well beyond the technology’s limitations and have unrealistic expectations that need resetting. It’s important for users to understand that augmented analytics can automate many mundane tasks, but the tools still rely on humans for context and decision-making. It also requires your business to know where technology or people excel best in the analytics process, have a strategy that supports its use, and allow for feedback and flexibility to make adjustments over time to find the right balance.

3. Data Quality

Augmented analytics can’t transform low-quality raw data into accurate, high-quality visualizations and insights. Good data quality is the foundation for an effective augmented analytics system. It requires you to invest in proper data management and data governance practices to ensure you’re working with clean, accurate data. 

4. Data Literacy and Analytics Skills

Users often need to understand the fundamentals of data and have the right technical skills to benefit from augmented analytics tools. You may need to offer employees a basic data literacy program to learn data concepts that support them in exploring, understanding, and analyzing your data. 

While augmented analytics tools don’t require users to learn more technical skills such as coding, you’ll still need to ensure your organization understands the tool’s features and how to get the most out of using it. Training sessions can help bridge any skills gap in your workforce.

5. Data Bias

Bias can exist in machines if you have incomplete data sets or aren’t providing enough context. To avoid this, make sure all data is available and free of errors, run tests to see if you’re getting relevant and valuable results, and update models and context as you go to address changes. Having transparency around machine-based calculations and analysis can also help you identify and resolve bias.

Examples & Use Cases of Augmented Analytics

Augmented analytics can help your company find ways to boost overall performance, make everyday operations more efficient, and improve your relationship with customers—regardless of your industry or role. Explore how your business can develop more data-driven decisions using the augmented analytics examples below:

• Sales teams: Explore your entire sales pipeline to find ways to increase customer engagement, track sales quotas by team or individuals over time, or identify seasonal trends in your sales numbers.

• Marketers: View your campaigns at a granular level or compare key performance indicators between multiple campaigns to see what’s generating the best results. Use augmented analytics to track and boost ad views, clicks, or conversions.

• Executives: Quickly generate quarterly reports or interactive dashboards using augmented analytics tools to monitor big-picture performance and share findings during board meetings. 

• IT departments: Monitor system and server performance, track downtime, and use ML and predictive analytics to maintain equipment and prepare for possible cyber attacks.

• Fraud prevention: Augmented analytics enable your business to identify patterns or trends you may not be able to see on your own. For example, these tools can help spot or prevent fraudulent activity. 

• Supply chain management: Better track your inventory, shipping and delivery rates, and learn where problems or delays occur so you can take action to improve performance and customer satisfaction.
• Hotels and hospitality: Monitor performance of hotels and other hospitality facilities over time, optimize your services, offer more personalized experiences, and create more opportunities for upsells.

These are just a few ways to empower your business with augmented analytics. This technology makes data analysis more approachable and useful so you can take the right actions to move your company forward.

Augmented Analytics Best Practices

Set up your augmented analytics initiatives for success by following these best practices. 

1. Start With the Right Tools for a Strong Foundation

Augmented analytics tools aren’t all the same. You’ll need to research and find a tool that aligns with your company’s goals, offers the features you need, and stays within your budget. You may want to meet with internal stakeholders to determine what data security, governance, and functions are essential for your business and if a self-service analytics tool is your best option. 

2. Prove Your Success, Then Scale

Select one case or project aligned with existing KPIs and a small, specific scope to get started. Once your first augmented analytics project succeeds, you can implement it with larger projects throughout your business. Other departments and managers are more likely to buy into the process once you have solid results. 

3. Educate and Collaborate to Build Trust

Educating your organization about what augmented analytics can and can’t do—as well as how your company will use and benefit from it—can help demystify the process and build trust. Creating transparency around your strategies, teaching data literacy and analytics skills, and collaborating with different departments to tweak results are essential for success.

4. Create a Data-Driven Culture to Encourage Adoption

Adding augmented analytics into existing workflows requires patience and adaptability. Have experienced data teams help your departments learn how to find value and apply context. With support, their results will be even more relevant and successful. Creating a positive experience will also help develop a more data-driven work culture and encourage more teams to adopt analytics.

Get More Out of Your Data

Data is critical for improving many aspects of your business, such as customer relationships, project or campaign profitability, sales team productivity, and overall performance. Augmented analytics makes your data more accessible to the entire organization so that everyone can benefit from it, regardless of their role or technical experience. 

Domo’s augmented analytics tools help you get fast, accurate information using AI to assist with collecting and preparing data. Then, you can explore your data easily with machine learning and natural language processing through visualizations, dashboards, and other data solutions.