Advanced analytics encompasses sophisticated techniques that help organizations analyze their data like never before to drive smarter business decisions. While traditional business intelligence focused on reporting and basic dashboards, advanced analytics leverages next-gen technologies like machine learning to enable deep data analysis and accurate forecasts.
In this beginner‘s guide to advanced analytics, you‘ll discover:
- What sets advanced analytics apart
- Key techniques and technologies
- Real-world applications and use cases
- Steps for getting up and running
So if you‘re looking to transform decision making with predictive insights from your data, you‘re in the right place. Let‘s dive in!
What is Advanced Analytics?
Advanced analytics utilizes modern techniques to extract valuable insights from data that traditional business intelligence alone cannot provide. It makes extensive use of emerging methods like machine learning, neural networks, predictive modeling, and optimization algorithms – leveraging both historical data and real-time data streams.
While traditional BI focuses mainly on descriptive statistics about past events, the key goal of advanced analytics is predictive and prescriptive analytics:
- Predictive analytics forecasts future outcomes based on existing patterns
- Prescriptive analytics recommends the best course of action to capitalize on those predictions
Some key capabilities enabled by advanced analytics include:
- Predictive modeling to estimate future trends
- Complex pattern/trend detection within large, varied datasets
- Sophisticated data visualization and exploration tools
- Automated analysis and modeling to quickly process new data
- "What if" scenario modeling for planning purposes
So in essence, advanced analytics empowers deeper data exploration paired with accurate forecasts to drive well-informed planning and decision automation based on data insights.
Why Do Businesses Need Advanced Analytics?
With data growing exponentially, businesses need ways to keep up with scaling analysis while making sense of a flood of information in varied formats and from disparate sources.
Some key reasons organizations are adopting advanced analytics:
More Accurate Predictions
Sophisticated machine learning algorithms can detect subtle patterns within complex data that humans or traditional analytics easily miss. This enables remarkably accurate forecasts to guide everything from supply chain to customer retention.
Faster Insights
Automated modeling, intelligent assistants, and streamlined analysis workflows empower faster insights from new data sources. This allows decisions to incorporate up-to-date information.
Smarter Decisions
Predictive insights from advanced analytics provide a data-driven view of future scenarios to optimize planning. Prescriptive guidance enables precise adjustments for substantially improved business outcomes.
Informed Strategy
By understanding emerging trends, market changes, and potential disruptions through data analysis, organizations can craft resilience into long-term plans and pivot faster when needed.
Competitive Edge
Companies that tap into advanced analytics position themselves to outperform peers by leveraging modern tech to capitalize on new opportunities and challenges.
Examples of Advanced Analytics Techniques
Advanced analytics brings together an array of sophisticated techniques – both established and emerging – that help turn raw data into valuable insights. These include:
Machine Learning
Machine learning algorithms automatically build analytical models and refine them based on new data without explicit programming. Techniques like natural language processing (NLP), neural networks and deep learning find complex patterns.
Predictive Modeling
Uses historical data to identify trends and patterns, then projects them into the future to estimate outcomes and risks for planning purposes. Enables "what-if" scenario modeling.
Data Mining
An umbrella term for looking at large datasets to identify useful correlations, associations, segments and anomalies that offer valuable insights.
Optimization
Mathematical techniques that provide guidance on the optimum course of action or configuration to maximize desired outcomes based on defined parameters and constraints.
Multivariate Statistics
Statistical models that analyze multiple variables or features of a dataset simultaneously to uncover complex relationships between them. Allows deeper analysis.
Sentiment Analysis
Uses NLP and text analysis to systematically identify, extract, and evaluate subjective information and the contextual tone within text to understand perceptions. Valuable for tracking brand sentiment.
Streaming Analytics
Enables rapid analysis of real-time, streaming data from IoT devices, website activity, sensors and more to support instant insights and decision automation.
Real-World Applications of Advanced Analytics
Advanced analytics delivers immense business value across industries. Just some examples:
Banking – Detect payment fraud in real time, model risk to guide investments, predict churn and optimize customer lifetime value
Insurance – Set dynamic premiums based on predictive risk models, streamline claims through automated assessment
Retail – Recommend products based on purchase data, optimize pricing, manage just-in-time inventory
Manufacturing – Predictively schedule production and maintenance, optimize supply chain and logistics
Healthcare – Personalize patient treatments with AI assistants, model resource needs, accelerate clinical trials
Sports – Determine game strategy, predict player injuries, analyze performance and fans to guide management decisions
The applications are nearly endless – advanced analytics can bring a competitive edge to any data-driven decision!
Getting Started with Advanced Analytics
Transitioning to a more mature, effective analytics strategy powered by cutting edge tech may feel daunting initially. Here are best practices to guide the journey:
Start Small – Walk before you run! Pilot a targeted predictive model or optimization algorithm in a low-risk area first. Let success drive expansion.
Focus on Business Priorities – Which decisions or processes will analytics improve most? Lead with the pain points!
Assemble the Right Team – Advance analytics success needs a blend of business and technical experts – data engineers, scientists, and decision makers.
Invest in Talent Development – An analytics-empowered culture and skill building helps sustain progress.
Review Data Prep Needs – Break data silos and pipeline all data needed for robust models – quality is crucial!
Iteratively Improve – Continually review programs against metrics, user feedback and business impact. Updates keep pace with changes.
Powerful Advanced Analytics Tools
Specialized big data platforms designed for sophisticated analytics now put immense power in the hands of end users with visual, self-service access:
- IBM Watson Studio
- SAS Visual Analytics
- SAP Analytics Cloud
- Databricks Unified Analytics
- AWS SageMaker
- Microsoft Azure Machine Learning
- Google Cloud AI Platform
Integrated notebooks also popularly support advanced analytics use cases for data access, modeling, visualization and collaboration:
- Jupyter
- Apache Zeppelin
- RStudio
The options keep expanding – no coding needed to leverage leading-edge tech!
Become an Analytics Leader
The proven performance gains delivered by advanced analytics will soon make sophisticated approaches table stakes. By starting now with targeted projects and cultivating data science experts internally, organizations can build the skills, infrastructure and vision to transform decision making through modern predictive insights.
The analytics leaders of tomorrow start putting the fundamentals in place today. Will you lead or follow? The power lies in your data!
