What is Augmented Analytics?

Augmented analytics uses artificial intelligence (AI), such as machine learning and natural language processing, to automate data preparation, insight discovery, and sharing, enabling faster and more accurate business intelligence.

Right, so what are the real benefits of augmented analytics? 

Augmented analytics unlocks insights at lightning speed. This empowers everyone in your team—not just data scientists—to contribute meaningfully to data-driven strategies. Automating data analysis saves time, allowing your top talent to concentrate on strategy and new ideas.

The Evolution of Augmented Business Analytics

Before the 1990s, business analytics relied on manual data collection and analysis methods. Analysts had to gather data from paper records or disparate computer systems. This painstaking process involved using calculators or early spreadsheet software, which limited the depth and scope of the analysis. 

For example, these early tools struggled to handle large datasets, often crashing or slowing down significantly. Additionally, they lacked the capability to perform complex statistical analyses or generate sophisticated visualizations, hindering the ability to uncover deep insights.

The Rise of Big Data and Its Challenges

Big data started in the early 2000s. This was driven by:

• The boom of digitization

• The popularity of internet-connected devices

• The growth of online interactions

Consequently, many businesses found themselves data-rich but insight-poor. 

How Augmented Analytics Address Modern Data Challenges

Today's modern data analytics began to take shape around 2010-2015 during the rise of cloud computing, powerful data processing capabilities, and sophisticated analytics software. Eventually, developers integrated machine learning and AI, laying the groundwork for augmented analytics.

Core Elements of Augmented Analytics

Most major business intelligence (BI) and analytics vendors incorporate these four elements of augmented analytics into their platforms: 

1. Machine Learning

Machine learning models (MLM) identify patterns, anomalies, and trends in large datasets invisible to human analysts. ML algorithms continuously learn and improve from the data they process, enabling more accurate predictions and recommendations. This empowers businesses to make data-driven decisions with confidence and precision.

2. Natural Language Processing 

In augmented analytics, natural language processing (NPL) bridges the gap between human language and machine understanding. Natural language generation (NGL) allows users to interact with data using everyday language. In other words, you can make complex queries without specialized coding skills. Explaining data insights in plain language also makes analytics more accessible to an organization’s non-technical users.

3. Automated Data Preparation 

Automated data preparations streamline the collection, cleaning, and organizing of data for analysis. AI is often used to find and fix problems with data quality. It also helps combine different data sources. Finally, AI prepares datasets for analysis.

Automated data processing can reduce data entry time by up to 70%, freeing roughly 23 days in the year for other business activities.

4. Automated Insight Generation

Once data is prepared, augmented analytics platforms detect significant patterns, relationships, and anomalies within datasets, presenting them to users in an easily digestible format.  AI-powered insight generation empowers you to make swifter, more informed decisions, driving business value and competitive advantage. 

Real-World Applications of Augmented Analytics

This is just a sampling of how augmented analytics is reshaping industries:

• Finance: Enhance risk management, fraud detection, and investment strategies by analyzing real-time financial data.

• Retail: Forecast demand, personalize marketing efforts, and streamline inventory management for improved customer satisfaction.

• Manufacturing: Drive efficiency through predictive maintenance, quality control optimization, and data-driven production line improvements.

• Entertainment: Analyze viewer preferences, create personalized content recommendations, and inform content creation decisions.

• Transportation: Optimize routes, predict maintenance needs, and improve overall efficiency of logistics operations.

• Healthcare: Improve patient outcomes through predictive diagnostics, personalized treatment plans, and optimized hospital resource allocation.

• Government: Increase operational efficiency through data-driven decision-making and citizen engagement analytics.

• Energy: Optimize energy consumption, predict equipment failures, and improve grid management for a more sustainable future.

• Education: Personalize learning experiences, identify at-risk students, and optimize resource allocation for improved educational outcomes.

Augmented Analytics Challenges and Considerations

From data security to system integration and user adoption, these factors are crucial if you’re aiming to leverage AI-driven platforms to analyze data:

Addressing Data Privacy and Security Concerns

Your organization must establish strict data handling, storage, and access protocols to maintain trust and avoid potential breaches. Ensure your augmented analytics tool complies with your region's specific data regulations, such as GDPR in Europe or CCPA in California, to avoid legal issues and protect user privacy.

Integrating With Existing Systems

When selecting your augmented analytics platform, consider its compatibility with your current tech stack and scalability. You want a solution that can grow with your business and adapt to future technological advancements. Such foresight saves you future headaches and guarantees your investment pays off in the long term.

User Adoption and Training

Introducing AI and machine-learning tools often meets resistance from employees accustomed to traditional methods. Well-designed training programs are key to overcoming this hurdle. Instead of just teaching how to use new tools, focus on why they're valuable. Foster a culture of continuous learning by providing ongoing training opportunities, such as workshops, webinars, or online courses. 

Create incentives for learning, such as certifications or badges, and recognize and reward employees who actively embrace new technologies and share their knowledge with others. Encourage open dialogue about the changes and celebrate early successes to build momentum and enthusiasm for augmented analytics.

MicroStrategy ONE Platform: Your Augmented Analytics Solution

Augmented analytics transforms raw data into a strategic asset, giving your business a decisive edge. Automating complex analysis uncovers nuanced patterns and correlations human analysts might miss. The ripple effects are tangible:

• Streamlined operations that adapt in real-time
• Customer experiences tailored with precision
• A market position fortified by data-driven agility

The Benefits of MicroStrategy ONE

The analytics and BI platform MicroStrategy ONE offers intuitive data visualization, natural language querying capabilities, and predictive analytics tools. We empower your employees to make informed decisions quickly and confidently.

Accelerate their time to action, reduce resource waste on complex dashboard creation, and ensure that predictive insights serve everyone's needs. Unlock the full potential of your data and drive business success with MicroStrategy ONE. Contact us today to find out more.