How to Implement Augmented Analytics for Data-Driven Decision-Making

Augmented analytics offers a streamlined approach to data analytics, enabling data scientists to produce valuable, relevant findings for business leaders automatically. It can supplement almost every stage of insight generation, outperforming other existing technologies. How can this tool be used to support data-driven decision-making?

What is Augmented Analytics?

Augmented analytics leverages machine learning (ML) and natural language processing (NLP) technologies to automate many of the analysis processes data scientists typically handle – tasks such as data preparation, analysis, insight generation and output interpretation. Its purpose is to streamline responsibilities and help inform business decisions more quickly.

This tool is a type of business intelligence (BI) platform that prioritizes accessibility, automation and simplification. Augmented analytics differs from traditional applications in that it isn’t static and doesn’t take weeks to generate models and purpose-made reports. Of course, it also integrates several subsets of artificial intelligence (AI) in novel ways.

Considering its rapid processing speeds and automation capabilities, AI’s integration into the field is inevitable. Although augmented analytics is relatively new, it will likely catch on soon. Already, the International Data Corporation estimates sales of AI-related software, hardware and services will increase by almost 30% in 2024 alone, totaling $166 billion.

How Augmented Analytics Outperforms Other Approaches

Traditional data management and analytics processes are largely manual. Even with the help of modern technologies, extracting intelligible, actionable insights from numerous spreadsheets or massive amounts of unstructured information is time-consuming and complex. Poor data quality magnifies these issues.

In the United States, poor data quality is a multi-trillion-dollar problem. Around 54% of companies agree that data quality and completeness are the leading information management challenges, with only 16% categorizing their data quality as “very good.” Manually extracting valuable insights is a difficult task.

Even if an organization has high-quality data, the data analysts and data scientists can only work so fast. By the time they complete preparation and analysis, the findings may be stagnant or outdated. Further, because they work with samples, it is difficult to comprehensively analyze trends, limiting the potential value of results.

Although estimates differ, organizations generally spend 10%-30% of their revenue on managing data quality issues. In addition to direct financial losses, they also experience indirect losses such as reputational damage and poor decision-making. Other nonmonetary impacts are often short-term operational issues that affect task delegation and administrative actions.

Because augmented analytics tools leverage AI, they can detect and resolve those issues by automatically eliminating duplicates, recommending transformations and filling in missing values. These tools work quickly – faster than a human –exponentially accelerating quality assurance and analytical tasks.

In addition to outperforming its human counterparts, augmented analytics also outdoes traditional BI. Those platforms exclusively seek to inform specific business decisions, meaning their purpose-made reports often overlook larger trends. On the other hand, an AI-driven tool can effortlessly examine an entire population.

Research shows that traditional BI analytics take much longer than augmented analysis. For instance, in model tuning, while BI required three minutes for every model to generate results, AI generated all models in under a minute. Substantial differences add up, facilitating successful analysis processes.

Advantages of Utilizing Augmented Analytics

Augmented analytics democratizes analysis by making results accessible – even to nontechnical staff – enabling enhanced interdepartmental collaboration and information sharing. Because disparate storage systems can affect decision-making, the ability of augmented analytics to minimize data silos can improve business outcomes.

Skill scarcity is another primary implementation driver. According to one survey, 6 in 10 respondents agree that data science and analytics roles are the hardest to fill. In light of staffing issues, many decision-makers use automation platforms to augment their workforce.

Augmented analytics platforms provide several operational and productivity benefits.

• Accelerated Decision-Making. Speed is among the top benefits of augmented analytics. AI’s rapid processing and automation capabilities can handle repetitive data preparation, management and analysis responsibilities, lightening workloads. It can also rapidly perceive patterns and anomalies, accelerating an organization’s decision-making process.
• Comprehensive Analysis Results. If you’re like most data scientists, you work with samples because the sheer volume of information would make tasks otherwise impossible. Unlike humans, an ML model can analyze entire populations, enabling a comprehensive analysis. Consequently, it increases authenticity and precision.
• Greater Precision and Pertinency. Increased accuracy is another substantial advantage of utilizing augmented analytics. ML models are immune to human error and – when properly developed – are more accurate than their human counterparts. Greater precision substantially heightens the pertinency of findings, enabling more effective, informed business decisions.
• Explainable Analysis Findings. Have you ever struggled to interpret or explain analysis results? Because AI leverages NLP, it can decipher and clarify the findings. Additionally, it can translate complex technical jargon into easy-to-understand language for nontechnical stakeholders or colleagues. Some platforms can visualize information, turning text into charts, graphs and tables.
• AI-Generated Recommendations. Crucially, these tools can offer guidance because of their NLP capabilities. For example, they can decide whether to select a cluster or time series analysis based on the data set. Instead of entering code, you can type a question in plain text and receive a human-like conversational response.
• Challenges of Utilizing Augmented Analytics. Augmented analytics platforms are prone to analytical bias because algorithms cannot understand context. Although this challenge isn’t unique to these tools, its automation capabilities may cause excessive oversight, resulting in errors going unnoticed – and influencing decision-making. The integrity and cleanliness of data sources should be prioritized.  
• Overreliance is another potential issue. After all, AI is meant to augment, not replace, human expertise. Although it can automate much of your workday and may even outperform humans in some regards, solely relying on AI can gradually diminish efficacy, adversely affecting insight generation.

How to Implement AI in Decision-Making

Implementing augmented analytics for data-driven decision-making is a multistep process.

1. Select an Analytics Platform

Numerous out-of-the-box tools exist, so choose carefully. Does it support your data models? How does it display analysis findings? Can it connect to your storage systems? Although most support structured information, many are unable to process unstructured data. Consider whether the platform aligns with your business-specific needs.

2. Determine the Extent of Automation

The extent of automation determines how implementation affects everyday responsibilities. Priorities will shift if you allow AI to supplement or take over most tasks. Crucially, remember that augmentation is not synonymous with automation – this tool does not eliminate the need for the expertise of a data scientist or analyst.

3. Ensure Operational Compatibility

Ensure that your data sets, platform and business goals are compatible before proceeding with implementation. For example, you must have the resources to complete preprocessing before feeding newly collected data to the tool. Considerations like these may seem minor but are crucial for a seamless adoption.

4. Stagger Platform Implementation

If you are in a decision-making role, strongly consider staggering implementation to resolve any unexpected pain points. Determine whether data collection, preparation, transformation or insight generation takes priority. Then, measure its impact to determine where you need to improve before progressing.

5. Evaluate the Impact of Implementation

If a pre-implementation baseline is established, you can evaluate precisely how the platform affects productivity and time to completion over time. This enables determining how the tool influences business outcomes using quantifiable metrics to involve stakeholders and receive buy-in from board members.

Implementation Obstacles to Consider

Although thorough preparation, careful platform selection and staggered implementation should minimize obstacles, it’s difficult to completely eliminate them. Despite best efforts, you cannot plan for every contingency. Unforeseen issues are a standard part of adopting and deploying any new technology.

Data scientists may fear automation technology will replace humans. Although this hesitancy is normal, it may develop into attrition over time as worry builds to disapproval. Naturally, this apprehension can significantly hinder implementation. At the very least, it can affect the platform’s effectiveness.

To combat this, consider scheduling a meeting with team members and supervisors to discuss how automation will affect job security and duties. Cover postadoption hours, responsibilities and expectations. Directly addressing this issue can suppress any lingering hesitancy during critical stages.

Board buy-in is another common implementation obstacle faced by technical departments because the cost of modern technologies makes adoption a risky business decision. To overcome this obstacle, consider highlighting others’ success stories with up-to-date, relevant data.

Make your findings easily understandable to nontechnical professionals to increase the odds of swaying their opinions. Visualization can help you communicate large data sets quickly, which is crucial in fast-paced settings. Additionally, it makes trends stand out, which is a beneficial factor when attempting to get your point across.

Augmented Analytics Empowers Decision-Makers

Standard analytics can produce valuable results driving data-driven decision-making, but manual preprocessing, analysis and evaluation are time-consuming and inefficient. Augmented analytics is fast, precise and user-friendly, accelerating processes to empower decision-makers.

Consider exploring your platform’s capabilities to maximize its potential. For example, an embedded analytics feature may enable information visualization on easily accessible displays, enhancing interdepartmental collaboration. How you leverage augmented analytics tools post-implementation determines its impact on decision-making and its return on investment.