Augmented Analytics

Modern augmented analytics (also known as AI analytics) is powered by a cloud-based, end-to-end data integration and analytics platform. This platform helps you manage complex data across its lifecycle as well as perform the necessary types of analytics. Artificial intelligence enhances each aspect of your data analysis by automating processes, enabling advanced techniques, and delivering insights and recommended actions.

Let’s walk through the diagram above.

Data Collection and Preparation: AI streamlines the data collection and preparation process through automated gathering, cleaning, and integration of information from diverse sources. AI algorithms possess the capability to scrutinize big data sets for potential errors or inconsistencies and to recognize specific attributes within data such as personal information. They can also propose appropriate data refinement techniques, ultimately enhancing the effectiveness and precision of data preparation.

Data Exploration and Visualization: Sophisticated algorithms for analysis and visualization bolster the processes of data discovery, mining, and representation. AI methods, including clustering and anomaly detection, assist in revealing concealed patterns and anomalies within the data. Visualization tools driven by AI provide interactive and user-friendly visual depictions, allowing you to more efficiently explore intricate data sets and gain actionable insights.

Machine Learning and Statistical Analysis: Machine Learning (ML) is a subset of AI that automates the construction of predictive models. It enables models to learn from historical data, recognize patterns, and make predictions or decisions with minimal human intervention. ML models serve as the foundation for a wide range of AI-powered data analytics applications, including recommendation systems and search-based analytics in natural language processing. The best augmented analytics tools incorporate AutoML capabilities, facilitating the creation of customized ML models without the need for extensive training. Explainable AI (XAI) refers to models and systems that can provide clear and understandable explanations for their decisions or outputs. Additionally, AI algorithms like neural networks support advanced techniques like deep learning, enhancing the precision and complexity of modeling for diverse use cases.

Natural Language Processing (NLP) and Natural Language Generation (NLG): NLP serves a multifaceted role in data analytics. It resolves language ambiguities, structures data, and facilitates seamless communication between computers and users for tasks such as reading, speech recognition, and sentiment analysis. This enables immediate and coherent responses through NLG. Furthermore, NLP supports research endeavors by extracting crucial facts, relationships, and summaries from vast amounts of text-based data, while also conducting sentiment analysis to discern the emotional tone. NLG complements these capabilities by empowering analytics tools to offer easily understandable responses and generate reports in your language. This automation of routine analyses not only saves time and resources but also aids compliance teams in identifying vital information from structured data, providing context, explanations, and potential next steps. Advanced NLG tools, augmented with machine learning capabilities, can furnish in-depth answers to complex queries.

Predictive Analytics: AI empowers the development of highly precise and robust prediction models. Through the application of AI algorithms, these models can efficiently analyze extensive and varied datasets, adeptly capturing intricate, nonlinear relationships, and effectively managing complex features. Techniques like ensemble learning and deep neural networks, inherent to AI, further enhance the accuracy and resilience of these predictive insights.

Prescriptive Analytics: Prescriptive analytics involves employing sophisticated methods and tools to scrutinize data and content, offering specific recommendations for the most advantageous course of action or strategic approach going forward. It can also facilitate automated decisions in other apps such as CRM or ERP software. In essence, it addresses the fundamental question: "What is the best course of action?"

The most effective augmented analytics tools combine the best aspects of machine intelligence and human curiosity to help users get faster insights, consider data from new angles, increase productivity, and help users of all skill levels make better business decisions. So if you’re evaluating data science vs data analytics, keep augmented analytics in mind.

1. Delivers value faster: When data science and artificial intelligence are leveraged, the result is faster data preparation, more effective data analysis and visualization, accelerated insights, and higher productivity. On the data prep side, algorithms can help automate manual processes, such as automatically recommending associations between different data sources, as well as profiling and transforming data. When it comes to visualization, a quick drag-and-drop auto-generates the right types of graphs, maps, KPI objects, and other visualizations based on the data you choose.

2. Uncovers hidden insights: With earlier generation visualization tools, users would have needed an idea or a hypothesis around the kinds of insights they wanted to uncover. But with augmented analytics, the algorithms do the work, providing contextual suggestions that uncover insights users never thought of in advance. By surfacing important data relationships, correlations, and outliers, data science and artificial intelligence helps users build confidence as they’re guided through the process of making their own discoveries.

3. Increases trust: Every time a user interacts with data, they provide clues to machine learning or AI algorithms about business context and intent. Over time, the machine provides more relevant and accurate suggestions and interactions based on these clues, increasing user trust in data. And, because with augmented analytics people play a role in the analytical process, rather than just accepting insights that come from a black box, that trust grows even stronger, facilitating buy-in and wider adoption of analytics in the organization.

4. Increases data literacy: As businesses continue to collect massive amounts of data, it’s important that everyone, not just data analysts or data scientists, has the opportunity to gain value from that data. AI analytics can promote data literacy by automatically surfacing insights, making recommendations, and empowering all users to interact with their data. Because users can interact conversationally using natural language, and visualize data with very little effort, creating a data literate workforce becomes far more attainable.

In this example, AI analytics capabilities found in a modern business intelligence tool helps a sales leader gain insights such as the sales and cost by product in a specific category. Natural language processing makes it easier to understand the sales revenue and cost of sale by product.

Augmented analytics takes into account your intent and behaviors to generate insights that are context-aware and highly relevant. As shown above, you can ask a question in a conversational way and NLP understands the meaning and then generates relevant insights, including visualizations. You can then refine your query and explore your data in ways you hadn’t thought of before, to help you gain deeper insights and make better decisions.

Here are some of the biggest barriers your organization may face in adopting augmented analytics.

1. Accuracy: Ensure the insights your tools generate are accurate and trustworthy. This means using comprehensive data that is free of errors, updating models as your data changes, and providing transparency into how calculations were made.

2. Relevance: Users don’t have time to filter out irrelevant information. Test out your interfaces to ensure that searches yield relevant results. If not, users will stop using the analytics software because it won’t provide value.

3. Data Bias: Bias can be present in machines as well as people. Data bias is typically caused by AI having incomplete data sets and lack of context. Be sure you have context-aware capabilities that can analyze all of your data and provide more objective results.

4. Performance and scalability: Depending on your platform and capabilities, data analytics using AI could take a lot of computing power. Also, keep in mind that the volume of data significantly affects response time.

First, let's start with some definitions. Machine Learning (ML) is a type of AI which automates predictive model building by allowing software to learn from historical data, identify patterns, and make predictions and decisions with little to no human guidance. AutoML (automated machine learning) refers to the self-service tools and processes which make it easier to build, train, deploy and serve custom ML models.

Data analytics and machine learning

ML models are the basis for most AI analytics capabilities. They’re the source for insight recommendations and natural language, search-based analytics. They’re also the engine for cleaning and prepping the data, reducing errors and saving countless hours of manual work in curating tables, text, images and videos.

The best BI and data analytics platforms go a step further by integrating a robust AutoML capability into their solution. This allows you to build custom ML models and then explore interactive visualizations and dashboards with predictive insights based on the models you develop.

Human intuition is still required

Most business problems can’t be solved purely by algorithms or machine learning. They require human interaction and perspective. Augmented data analytics is a combination of human curiosity and artificial intelligence, making BI and data analytics accessible to more people by allowing them to ask questions and automatically generate insights from their data, through simple conversational and visual interactions. Ultimately, this brings deeper insights and data-driven decision-making which improves your company’s performance.

Set your initiatives up for success with these best practices.

• Start small and align KPIs: Your data doesn’t have to be perfect to get started with data science and artificial intelligence. Instead, choose a use case that is aligned with your KPIs and metrics and has high business value. Once you see success, celebrate it and move on to larger projects.

• Collaborate to build trust: One of the biggest sources of mistrust in AI is lack of transparency. Avoid the black box by inviting business users from across the organization to be a part of your analytics initiatives and workflows so they can build trust through transparency.

• Encourage a data-driven culture: As more people in your organization begin to use analytics, you should make sure they have the strategies and training they need to become data literate and get the most from your company’s valuable data assets.