A New Era of Data Insights: How Augmented Analytics is Transforming Sales

Imagine this- Your data reveals a 20% increase in revenue QoQ, prompting calls for a celebration. But wait, what caused the surge? Has the demand for your product increased, did your marketing team do a great job or were you able to make your processes more efficient? Here is where you turn to augmented analytics, your trusty sidekick in the world of data explosion.

In today’s dynamic and highly competitive landscape, it’s essential to have an in-depth knowledge of all facets of your business. However, with data pouring in from every direction, it's easy for crucial information to play hide-and-seek in the chaos. With augmented analytics, you get a trusty sidekick that sorts through the clutter and ensures nothing vital slips through the cracks. Augmented analytics not only organises your data but also offers smart recommendations and timely alerts that align perfectly with your company goals. As companies recognise this, the global augmented analytics market is set to grow to $173.82 billion by 2032.

In this article, we will take a closer look at what augmented analytics is and how it can steer your business towards becoming a data-driven powerhouse.

What is Augmented Analytics?

Augmented analytics is a combination of several technologies and methodologies that make data analysis more accessible and impactful for users without extensive data expertise. It makes use of Artificial Intelligence (AI) and Machine Learning (ML) to automate and enhance data analysis. It helps you dive into data within analytics and business intelligence platforms, and also simplifies complex data information, making it easier to understand.

Augmented Analytics is especially helpful in the world of sales. By turning raw data into actionable insights, all within the CRM platform, it helps sales teams easily understand customer behaviour and spot market trends. With these insights, you can quickly identify opportunities, fine-tune strategies, and personalise customer interactions.

For instance, Salesforce's Einstein Analytics employs machine learning algorithms to predict which leads are more likely to convert into customers. This enables sales teams to prioritise their outreach efforts, ultimately leading to more efficient and targeted sales strategies.

The Role of Machine Learning in Augmented Analytics

Machine learning (ML) is the backbone of several augmented analytics features. Using ML’s data learning models, augmented analytics can continuously learn from new data and offer intelligent insights. This capability transforms augmented analytics into a proactive partner, enabling it to consistently improve its insights and predictions. With data volumes and complexity expected to grow in the coming months, ML’s role - in organising and analysing data within augmented analytics - becomes even more crucial.

Salesforce’s State of Data and Analytics Report reveals that over two-thirds of analytics and IT leaders expect data volumes to increase 22% on average over the next year. In such a scenario, ML algorithms can tirelessly sift through mountains of data and enable teams, especially sales teams, to make lightning-fast decisions based on solid data rather than intuition.

Let’s take a closer look at how ML algorithms contribute to automated analysis.

1. Pattern recognition

These algorithms spot intricate patterns and trends in massive datasets. By analysing past data, they uncover correlations, predict upcoming trends, and craft predictive models that shout, "Hey, here's where the action is!”        

2. Augmented data preparation

ML doesn't just stop at data organisation; it excels at data prep too. It spots schemas, catalogues data, and even suggests how to enrich data points. It then translates these into natural language queries or visualisations, letting users speed up the analysis and execution process.

3. Enhanced personalisation

Within business intelligence (BI) platforms, machine learning algorithms learn the ins and outs of your industry semantics and user preferences, creating a personalised experience that feels tailor-made for your team.

4. Anomaly detection

These algorithms can also spot anomalies and outliers in datasets, enabling them to flag potential issues or golden market opportunities. It's like having a data superhero with a sixth sense!

Augmented Analytics vs. Automation

While the alliteration is catchy, it’s crucial to understand the difference between the two. Automation, an important part of machine learning, tackles the mundane stuff – data entry, workflow automation, basic data processing etc. It doesn't whisper any secrets about your data; it just ensures the stage is set.

On the other hand, augmented analytics is all about enhancing human intelligence through AI and ML. It's not satisfied with just automating tasks; it wants to empower you with tools that unveil hidden insights, making your decision-making sharper and more accurate.


Augmented Analytics



Enhances human intelligence through AI and ML.

Streamlines repetitive business processes to boost efficiency.


Tools for data interpretation and actionable insights.

Task automation and efficiency improvement.


Provides advanced BI tools.

Handles mundane tasks like data entry.


Guides users to uncover hidden insights, deeper analysis and predict market trends.

Automates data entry process to reduce manual efforts in this process.

However, automation and augmented analytics need to work together most of the time. For instance, automated data cleaning or preprocessing is a critical part of the augmented analytics process, ensuring that the data used for analysis is accurate and relevant.

Let’s look at another example. Suppose you want to hire sales professionals for a new location. While automation can weed out the irrelevant profiles initially, augmented analytics can spotlight regional superstars you might otherwise miss. As a result, you can hire a dream team perfectly suited to the local market.

Who is Augmented Analytics For?

One of the key advantages of augmented analytics is that it can be applied at even the organisation level, improving the overall efficiency of the company. There are various roles that can benefit from augmented analytics tools. Within the domain of sales, these are:

  1. Sales representatives: Augmented analytics provides sales reps with deeper insights into customer behaviours, sales trends, and predictive analytics. With such streamlined data, they can easily identify opportunities and personalise customer interactions, improving customer experience by leaps and bounds.
  2. Sales executives: Sales managers and executives directly involved in decision-making benefit from the detailed reports and insights that analytics provides. They can oversee the overall sales performance, set realistic targets for their teams, and form comprehensive growth strategies based on accurate insights.
  3. CRM administrators: While augmented analytics is primarily targeted towards people with limited exposure to data, it can also help CRM administrators and analysts. BI tools powered by AI enable them to optimise data workflows, conduct in-depth analyses, and create customised dashboards for all stakeholders.

Similarly, other departments can leverage augmented analytics for their unique purposes. Finance and operations can analyse datasets for insights that can help in forecasting, optimising supply chains, assessing risks, and boosting operational efficiency. Marketing professionals can better segment customer profiles and understand their preferences and behaviours, enabling them to create targeted campaigns with personalised messaging.

Augmented analytics is not limited to any industry as well. Some industries that can benefit from it include:

  • Retail and e-commerce: Using augmented analytics, retailers can understand customer behaviour and forecast demand for specific products or services during peak season.
  • Healthcare and Life Sciences: Helps in analysing patient data, facilitate drug research, and boost treatment effectiveness and operational improvements within healthcare organisations.
  • Manufacturing: Helps predict machine failure, minimise downtime, cut costs, optimise production lines with real-time data and boost efficiency and yield.
  • IT: Helps identify customer churn risks before they happen, personalise experiences, streamline IT operations with data-driven insights, reduce manual work and improve service quality.

What Are the Benefits of Augmented Analytics?

Augmented analytics empowers organisations to make informed decisions by leveraging data. The powerful capabilities of analytics enable a proactive, forward-looking approach, enabling the development and enhancement of products, services, and business models. Let’s take a look at the benefits of augmented analytics in detail:

Agility: Increasing speed to insight

Augmented analytics significantly reduces the time required to derive actionable insights from large data sets from multiple sources. By employing ML algorithms and automation, it swiftly processes vast datasets, providing timely insights for decision-making. No wonder it is expected that by 2025, over 40% of data science tasks will be automated to expedite insights generation.

Accuracy: Providing a complete picture

Machine learning algorithms don't just interpret data; they unveil patterns and anomalies that often go unnoticed. As a result, organisations can reduce instances of error. For example, applying augmented analytics to your customer interaction data might help you identify subtle patterns in purchasing behaviour that ultimately help reduce errors in targeting.

Efficiency: Automating operational tasks

Automating routine and repetitive operational tasks frees up humans for more strategic analysis, allowing them to make data-driven decisions. Imagine not having to spend time sorting through a pile of paperwork and instead, focusing on creating a smarter plan to target a new group of interested leads.

Confidence: Powerful analysis in context

Augmented analytics delivers insights with context, enabling actionable decision-making by providing a deeper understanding of data relationships and implications. For example, analysing sales data alone might show customer churn as a percentage or trend. However, by incorporating context-rich data like website browsing history, social media engagement, and support ticket interactions, sales teams can understand the reason for this- whether it is dissatisfaction with pricing, lack of product features, or poor customer service.

Augmented analytics features

By enabling technologies that seamlessly combine automation, machine learning, and intuitive interfaces, augmented analytics empowers users to quickly extract actionable insights from complex datasets. Here are some features of augmented analytics that aid a more insightful decision-making process.

Automatic data identification

Self-improving machine learning algorithms can automatically identify and classify schemas, profile and catalogue data, data lineage, and metadata. Leveraging advanced data sorting techniques, ML algorithms can categorise and label data based on attributes such as type, format, and relevance. This entire process ensures efficient data discovery and categorisation, which is crucial for efficient data analytics.

Statistical techniques

Augmented analytics relies on various statistical techniques such as regression analysis, clustering, and time-series forecasting. Such techniques can not only provide accurate data trends but also explain the reason behind them, making it easier to understand for users who do not have expertise in writing code or calculations.

Smart data prep

Intelligent data preparation tools streamline data analysis. Tasks like data cleaning, normalisation, and transformation can be easily automated, which ensures minimal manual labour during data clean-up. For example, imagine you are tracking errors in your company's operations. To catch these accurately, you need neat and well-organised data. Here, smart data preparation tools act like super organisers, enabling the detection methods to work better and spot issues more precisely.


Insights uncovered during data processing can be used to provide AI-driven recommendations to the users. For instance, in retail, these systems might recommend product bundles that people frequently buy together, assisting businesses in devising effective sales tactics.

Natural language interactions

With augmented analytics, insights from data processing are either narrated in natural language or through visualisations. Natural language interactions significantly simplify complex queries and analyses, making data more accessible to non-technical users and helping users focus on what is important and actionable. For example, a sales executive could ask for quarterly team performance insights and receive precise reports without diving into query languages.

Use cases of augmented analytics

Forget dusty spreadsheets and cryptic dashboards! With its Artificial Intelligence and Machine Learning capabilities, augmented analytics empowers people in diverse roles to unlock game-changing possibilities.

Examples by role

  • Sales representatives: Sales reps can leverage augmented analytics to identify patterns in customer interactions, helping them tailor pitches and offers based on individual preferences, ultimately improving conversion rates.
  • Marketing managers: Marketing managers can use augmented analytics to analyse campaign performance, identify successful channels, and optimise strategies in real-time for maximum impact. HR professionals: Through a comprehensive analysis of resumes, skills and cultural fit, HR professionals can streamline the hiring process by identifying top candidates efficiently and improve the overall recruitment process.
  • Financial analysts: Financial analysts can benefit from augmented analytics to quickly identify trends in financial data, assess risk factors, and make informed investment decisions, ultimately enhancing the accuracy and efficiency of financial forecasting.
  • Customer support teams: It can help customer support teams understand customer behaviour, predict service inquiries, and provide proactive support, resulting in enhanced customer satisfaction and a more efficient support process.
  • Operations managers: Operations managers can employ augmented analytics to identify bottlenecks, predict demand fluctuations, and improve overall efficiency in production and logistics.
  • Executive leadership: Augmented analytics can aid strategic decision-making by providing comprehensive insights into various aspects of the business. These include market trends, customer behaviour, and operational efficiency, and can aid in long-term planning and growth strategies.
  • Data analysts: Augmented analytics helps data analysts with accelerated data preparation, real-time analysis, and reduced bias, enabling them to derive deeper, more accurate insights. The end result? More informed and effective business decisions.

Examples by industry

  • Healthcare: In healthcare, augmented analytics dashboards help in tracking patient recovery and predicting treatment outcomes. With the help of analytics, institutions can also analyse disease outbreaks, deliver personalised medicine, and improve resource utilisation.
  • IT: Within the IT industry, augmented analytics can help in identifying patterns and drivers behind spikes in system and server usage. As a result, IT teams can predict and prepare for outages in advance, reducing downtime.
  • Retail: Retailers can leverage augmented analytics for demand forecasting, inventory optimisation, customer behaviour analysis, and dynamic pricing, driving revenue growth and customer satisfaction.
  • Manufacturing: AI-based features, such as predictive maintenance, quality control, production optimisation, and supply chain management can help reduce downtime and enhance efficiency.
  • Travel and hospitality: In the hospitality industry, augmented analytics helps predict customer trends throughout the year, allowing hotels and other lodging facilities to prepare for peak and off seasons. Organisations like Airbnb also use augmented analytics to create personalised offers to upsell and cross-sell to customers.
  • Financial services: Here, augmented analytics help by automating data processing, identifying patterns, and enhancing predictive modelling for more accurate risk assessments, fraud detection, and investment decisions.
  • Public sector: In the public sector, it assists in data-driven policymaking, optimising resource allocation, and improving citizen services through insights derived from comprehensive data analysis. This ultimately leads to more informed and efficient governance.

Other common use cases

  • Legal: Across industries, augmented analytics assists in risk assessment, fraud detection, and compliance monitoring, mitigating risks and ensuring regulatory adherence when dealing with multiple stakeholders.
  • Talent acquisition: Lately, there has been a rise in HR professionals using augmented analytics to understand and resolve talent shortcomings, identify ideal candidates, and plan periodic activities.

Challenges of using augmented analytics

To leverage the full potential of augmented analytics, you must address the challenges that come with the implementation of advanced technologies. Let’s dive into the misconceptions surrounding AI and ML, the limitations of augmented analytics and the importance of data literacy and ethical considerations.

Human vs. machine dynamics

One of the most common misconceptions surrounding AI and augmented analytics is that it will replace human jobs entirely. This creates scepticism and hinders the widespread adoption of the technology. To address this misconception, impress upon your employees how augmented analytics actually complements human intelligence, and collaboration is essential for optimal results. Explain that while augmented analytics makes analysing data easier, human judgement and interpretation are still vital to figure out the relevance and implications of the insights.

Understanding boundaries of augmented analytics

Augmented analytics can still encounter scalability issues, especially when dealing with complex datasets. For example, augmented analytics systems might find it hard to understand the bigger picture and details of the business environment. They use patterns and connections in the data they have, but they may not fully understand external factors, how the industry works, or specific details about the business. Additionally, some users may have inflated expectations of AI tools and expect solutions beyond what is possible.

Data literacy and analytics proficiency

The success of augmented analytics relies heavily on users' data literacy and analytics proficiency. Recommendations are only useful if people understand the underlying concepts. In other words, machines can identify correlation and analyse data, but domain expertise is required to identify causation. Bridging the skills gap by providing adequate training and resources is essential for users to interpret insights effectively and make informed decisions.

Data governance, management, and curation

Since analytics is data-dependent, the quality of its results is directly dependent on the quality and reliability of the data fed into the system.

86% of analytics and IT leaders agree that AI's outputs are only as good as its data inputs.

Contaminated data, missing values, and inconsistencies can confuse the AI model and lead to inaccurate or misleading insights. Combining data from various sources like databases, sensors, and social media can also be a challenge. For example, your organisation might be a treasure trove of information, but it's locked away in isolated silos. Sales has its customer data, finance its financial records, and operations its own set of metrics. This fragmentation makes it hard for the AI to see the bigger picture and generate holistic insights.

Ethical Use of AI

Automated decision-making platforms often suffer from issues like algorithmic bias and data privacy concerns. Implementing ethical guidelines, transparency in AI processes, and continuous monitoring can help you overcome these ethical dilemmas.

Augmented analytics best practices

To get the most value out of augmented analytics, you must follow certain best practices such as establishing a strong foundation, educating users, and nurturing a collaborative community.

Start with a Solid Foundation

Before adopting augmented analytics solutions, it is crucial to establish a strong foundation. Departments need to understand how to ensure data quality and reliability, establish robust governance frameworks, and have a scalable infrastructure that supports advanced analytics capabilities.

Improving data quality is the top data priority of analytics and IT leaders.

Prove success before scaling investments

If you are moving away from traditional BI tools, it is better to start small. Demonstrate ROI and proof of success with department-level implementations, pilot projects or use cases. Once you have ample evidence that this is indeed improving things, consider a company-wide rollout.

Demystify and educate to build trust

If you want to truly get the most out of your BI tools, get your workforce to trust the technology. Host workshops and training sessions, sharing stories of how it helped others. Instead of using jargon and explaining the overall benefit, try picking up specific departments/roles and explaining the benefits they get. This makes it more relatable. It's like teaching someone a cool Excel shortcut – once they get it, they'll wonder how they lived without it.

Nurture success with collaboration and community

Ensure that domain experts and decision-makers encourage the adoption of these tools among the workforce. Encouraging cross-functional collaboration, creating forums for knowledge sharing, and recognising and rewarding contributions can drive engagement and innovation within the community.

Augmented Analytics and Salesforce

A leader in CRM solutions, Salesforce incorporates AI-powered features within its Einstein suite of data tools, harnessing augmented analytics to empower users with actionable insights. Here are some of the notable AI-powered features:

Einstein Analytics (source)

Salesforce Einstein Analytics infuses AI into business intelligence, providing predictive and prescriptive insights. It employs machine learning to analyse CRM data, delivering recommendations for sales, marketing, and service teams. It offers predictive lead scoring, next-best-action recommendations, and automated insights generation within Salesforce.

Einstein Discovery (source)

Einstein Discovery uses machine learning to explore patterns within data, automatically detecting trends, correlations, and outliers in large datasets. It is integrated within the Salesforce environment to allow users to operationalise data analysis, and get predictions and improvements.

Einstein Prediction Builder (source)

Einstein Prediction Builder leverages AI to predict what happens next in your business and helps users create custom AI models without coding. This feature integrates AI-powered predictions into Salesforce workflows. This allows users to predict outcomes, such as the success rate of deal closure or customer churn, within their CRM environment.

Einstein Bots (source)

Salesforce also leverages machine learning to present multimodal AI-powered insights in a more accessible way. Salesforce Einstein Bots leverage natural language processing (NLP) and machine learning to create AI-powered chatbots. These bots handle routine customer queries, perform tasks, and provide personalised recommendations, enhancing customer support and engagement.

Einstein Search (source)

Einstein Search supercharges the search bar using AI. Within the CRM, it predicts and displays the most relevant records and information based on user behaviour and patterns.

These AI-powered features embedded within Salesforce's suite of data tools leverage augmented analytics to empower users with predictive insights, automated recommendations, and streamlined processes. The result? Enhanced efficiency and informed decision-making – all within the CRM platform.



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