Your ROI is below expectations, and you’re seeking the answers in business intelligence If you already have a BI and analytics solution, you might wonder whether you need augmented analytics capabilities.

What was your company’s retail sales volume for the last quarter? How much revenue did the new product line make in the past year? What’s the customer churn rate? BI tools can answer questions like these in seconds.

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This article breaks down augmented analytics for you, with its applications and best practices to make it work.

Key Takeaways

• Augmented Analytics Definition
• Pain Points
• The Solution
• Additional Benefits
• Use Cases
• Best Practices
• Next Steps

Augmented Analytics Definition

Augmented analytics is a set of techniques that augment data exploration for generating and understanding results. These include artificial intelligence, machine learning (ML) and automation.

The Gartner team of Rita Sallam, Cindi Howson and Carlie Idoine coined the term in a research paper in 2017. Augmented capabilities help uncover hidden correlations between datasets and variables, opening up new possibilities to grow your business.

Pain Points

According to a Business Application Research Center survey, many companies still rely on guesswork and experience for business decisions.

• 58% of the respondents say their companies base at least half of their decisions on hunches and experience rather than hardcore metrics.
• Only 40% of the best-in-class companies make decisions based on guesswork, while 70% of those that lag in market performance do so.

Even with shared reports and dashboards, precious insight can get lost in translation. Despite user-friendly visualizations, not everyone can understand charts, graphs and maps. Scheduling meetings to interpret reports can be a time-suck, causing you to miss valuable opportunities.

Basing your decisions on hardcore numbers and figures will eliminate the uncertainty associated with guesswork.

It’s one thing to ask, “What was the retail sales volume for last quarter?” — many BI tools can answer this question in seconds.

But, performing diagnostic analysis, such as answering questions like “Why are sales declining in the eastern territory?” can be complicated. Such data queries require more processing power and machine learning — capabilities at the forefront of modern advancements in data and analytics.

Getting at the “why” gives you insight into the heart of your operations, pointing you toward causative factors and subsequent remedies.

Additionally, predictive analytics is a primary requirement when looking for analytics tools. Wouldn’t it be great to be aware of future market trends while designing strategy?

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The Solution

Performing data analysis becomes easier with machine learning and artificial intelligence. Using machine learning with natural language processing (NLP) at the frontend keeps the nitty-gritty of data wrangling and querying at the backend. The system performs mathematical calculations and statistical analyses behind the scenes, freeing you to figure out the next steps.

Machine Learning

Augmented technology speeds up response times by self-learning through recorded user searches and data queries. It means machines process a ton of data until they get good at completing tasks. It’s much like how you and I learn and become more proficient by gaining more experience in a field.

Machine learning builds iteratively better algorithms to process large, complex data volumes with a higher degree of accuracy. You don’t need guesswork — the software generates results based on accurate, contextual information. By integrating and indexing big data sets, the software ensures no data is left behind.

However, augmented analytics is more than machine learning. The capability to interpret everyday language takes this technology to the next level.

Natural Language Generation (NLG)

Mordor Intelligence predicts the global NLP market will grow to $48.46 billion by 2026, at a CAGR of 26.84%.

Augmented analytics software with NLG translates results into plain English language instead of showing the results as rows and columns of figures. There is no need to manually select and drag and drop the required metrics in a report or dashboard. Type in “Show me the latest sales figures” to view the desired metrics.

All your teams use the same language to analyze metrics, which can work wonders for shared insight and collaboration.

• Ask, “What will overall revenue look like next quarter?” and generate the results as a visualization or text explanation.
• Pose further queries to identify the variables that can make it happen at the end of the next quarter. You may need to boost sales in a region where you recently expanded your presence, and the new teams need a bit of hand-holding.
• By asking the right questions, sales and marketing professionals can analyze why a particular campaign did better in one customer segment than the others.

A sales performance dashboard in Tableau shows the Sum of Discount numbers by Customer.

Besides typing in text, you can use voice searches to get the desired information. This feature might not be available out-of-the-box and will likely cost extra. Ask vendors if their product includes voice-based data exploration.

Another significant aspect of augmented analytics software is ML-driven automation.

Automated Workflows

At the intersection of BI, AI, ML and RPA technologies is intelligent automation. RPA allows creating software bots to improve customer experience, employee productivity and efficiency across your organization. These ML-driven bots perform simple to complex repetitive workflows like data capturing with 100% accuracy.

They interact with existing human-machine interfaces by using technology that mimics human behavior. You can track job status and other performance metrics using augmented analytics software with RPA.

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Additional Benefits

Smart Data Management

Many vendors add value to data management tools by embedding augmented functionality. It boosts developer productivity, enables all users to perform advanced functions and enhances system performance.

ML algorithms and models:

• Automate repetitive, time-consuming workflows like mapping sources to targets, cataloging data or adding new sources.
• Optimize resources by automatically selecting query optimization strategies, table joins and data storage methods.
• Manage capacity by autoscaling as needed by adding instances on the spot and integrating node types in heterogeneous clusters.

How can you manage data better with machine learning?

• Data Cataloging: Augmented analytics software can automatically catalog and categorize data sources, datasets, tables and even individual columns and fields with ML algorithms and models. You can tag a single data element by domain, compliance risk, source, lineage, quality and more. It enhances search results, letting you identify cross-category correlations.
• Metadata Management: Newer data sources like IoT devices lack metadata, so you need additional help logging it into your repository. Augmented analytics tools with ML can parse data and create its associated metadata. They prompt you with automated recommendations or log the metadata into a warehouse.
• Data Quality Management: Augmented data management involves training algorithms to check the data quality, including detecting anomalies and outliers. Other automated techniques include data cleansing and classification and time-series forecasting. Some tools can even address these issues automatically based on previously created ML models or pre-defined business rules.

Greater Employee Engagement

NLP and machine learning techniques bring you closer to your metrics. Tracking KPIs gives your employees a greater sense of involvement and goal ownership. Adding common language narratives to data stories helps you cover the “last mile of analytics” — understanding the data results.

Reduced Bias

Augmented insight is free of bias that can creep into manually generated results. Even with the most experienced data scientists, the human perspective can corrupt the analytics results. As a result, businesses might not have accurate information, which leaves money on the table.

Financial institutions bank on predictions that involve assumptions — estimated economic growth, expected interest rates and more. Leaning on manual forecasting can be risky for customers and the business.

ML algorithms and automated workflows function with minimal human interference, so they aren’t predisposed to the same bias.

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Use Cases

ExpertMarketResearch predicts the global augmented analytics tools market to grow to $25 billion by 2026, at an impressive CAGR of 30.8%.

Many BI and analytics tools have these capabilities to help you analyze data quicker. Einstein Analytics, SAP Analytics Cloud, SAS Viya, Sisense, TIBCO Spotfire, Tableau and Thoughtspot have augmented analytics capabilities.

There are several augmented analytics examples with successful implementations across many industries, and machine learning and automated workflows are ubiquitous parts of it.

• AI-driven analytics helps physicians provide time-critical care by digging up electronic health records (EHR) information faster. Pharmaceutical companies use NLP and automated workflows to speed up clinical testing for developing new medicines.
• Financial lenders can assess customer risk before approving loans and minimize the risk of payment defaults. Augmented systems pull up historical information on defaulters and compare it with the applicant’s age, credit score and financial profile to assess their creditworthiness.
• Consumer companies manage marketing, sales and customer service activities better by obtaining product sales, buyer behavior, churn rate and customer satisfaction metrics.
• Supply chain and inventory management get a boost with instant natural language insight keeping you updated about in-house, out-for-delivery and delivered orders.
• Augmented analytics tools streamline HR and payroll management by allowing you to pull employee records through text or voice-based searches. The software offers search recommendations based on your browsing history.

With their ease of use, enterprise-level adoption of augmented analytics software should be straightforward. But, it can take some time, especially with a steep learning curve. Often, employees quickly move away when facing a challenge using new tech.

Following some tried and tested methods might encourage optimum software usage.

Best Practices

Set an example by using the technology often and using it well. Advertise your successes, however small. Tangible results can take time, even years, so be patient.

Get Executive Buy-in

People are more invested in the product when they have a clear picture of what it can do for them. Build a business case to get your top stakeholders on board. Highlight your company’s pain points and how augmented analytics can address them.

Present actual use cases, and showcase realistic goals by breaking them into smaller, achievable milestones. Besides the top brass, involve teams on the floor by showcasing how it enables self-service analytics.

Automate Your Workflows

Lean on automated workflows to enable quick insight and freedom from repetitive tasks. Machine learning streamlines data pipeline automation through easy drag-and-drop user actions. Investing time and effort in automating processes at the onset will save you precious time in the long run and make onboarding smooth for new team members.

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Feed Your System

Machine learning systems feed on information to learn and improve. The ML algorithms learn from your search history to gather and present relevant insight to you.

Big data is the ideal input for such systems to scale faster. CRM, ERP, social media, IoT and point-of-sale metrics help your system’s algorithms learn. It lets them cluster similar items together by identifying meaningful data relationships not apparent in manual analysis.

Celebrate All Successes

Over-communicate. Broadcast successful augmented analytics projects. Display KPI dashboards prominently in the office so people can track relevant metrics. Highlight successes in team meetings and encourage discussions on potential improvement areas.

Deploying an augmented analytics tool demands a conscious shift in your organization’s data culture. Your journey doesn’t end with purchasing the right augmented analytics software. Instead, it begins after that.

You might need to address constant concerns about how the technology could impact employee roles and jobs. Having all the facts ahead of deployment can help ease your team’s onboarding concerns.

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Wrapping Up

Before deciding on a tool, assess your company’s needs by creating a requirements checklist. Or get a head start with our free requirements template. Knowing what features you need at the outset will save you a lot of time. And when it comes to picking a tool, you can be confident you end up with one that works for you, not against you.

Are you using an augmented analytics tool? How has it helped your business? Let us know in the comments below!