Alexis Kestler
Advanced analytics is becoming an indispensable tool for businesses seeking to leverage data to outpace competition. As a leading data analyst and machine learning engineer, I‘ve seen firsthand how implementing the right advanced analytics strategies can profoundly impact growth, risk management, and strategic planning.
In this comprehensive guide, I‘ll provide an in-depth look at advanced analytics, including key techniques, use cases, tools, and more. My goal is to help you, as a business leader, understand how to effectively incorporate advanced analytics to take your organization‘s data-driven capabilities to the next level.
Defining Advanced Analytics: What Makes it Different?
Before diving into the value advanced analytics provides, let‘s briefly overview how it differs from traditional business intelligence and data analytics approaches:
Traditional analytics methodologies, like data mining, reporting, and online analytical processing (OLAP), tend to focus on retrospective data. While these provide valuable business insights, they have limitations in predicting future outcomes and guiding strategic decisions.
Advanced analytics leverages modern techniques including artificial intelligence (AI), machine learning (ML), neural networks, complex event processing, and more. This enables more accurate forecasting, simulations, risk assessments, and predictive capabilities compared to traditional BI tools.
Additionally, advanced analytics can work with both structured and unstructured data from multiple sources. It‘s able to uncover granular, actionable insights traditional techniques may miss. The automated, intelligent capabilities augment analysts to uncover insights faster from ever-growing data volumes.
According to recent surveys by industry research leaders, over 50% of companies are actively increasing advanced analytics investments to drive business value. As analytics models and algorithms continue improving, adoption is expected to rapidly accelerate.
Key Benefits Driving Adoption
There are a multitude of ways advanced analytics provides value across functions. Based on my experience, here are some of the core benefits driving adoption:
1. Enhanced Predictive Capabilities
One of the greatest differentiators of advanced analytics is significantly improving predictive modeling and forecasting accuracy. Machine learning algorithms leverage training data to uncover complex patterns traditional techniques overlook.
As an example, time series forecasting for supply chain planning is profoundly better utilizing LSTM neural networks compared to moving averages. Retailers have reduced inventory costs over 10% using advanced predictive demand planning.
For new product launches, algorithms assessing early sales data, demographics, and market research can accurately forecast first year adoption. This enables smart production scaling and sales planning.
2. Identifying New Opportunities and Threats
By assessing disparate data sources using AI, advanced analytics solutions can accurately detect emerging opportunities and threats. This is invaluable for functions like marketing, competitive intelligence, and risk management.
Marketing analytics leveraging natural language processing can uncover rising customer needs and interests. Algorithms also find lookalike target audiences with high value potential. This enables strategic, informed campaigns vs. guesswork.
For risk, supervised models automatically scan thousands of data points to detect anomalies indicative of fraud. Flags are then assessed by analysts. Catching issues early curbs losses.
3. Optimized Business Processes
Intelligent algorithms can analyze operations to detect bottlenecks, excess costs, and suboptimal processes. Process mining algorithms model processes from system logs to identify redundancies and waste.
This powers intelligent process automation, saving thousands of hours in manual labor. It also enables continuous process improvements vs. periodic analysis. Supply chains, manufacturing, HR, and finances all benefit immensely.
4. Improved Strategic Planning
Advanced analytics delivers a significant competitive advantage in long-term strategic planning. Sophisticated models assess market conditions, competitive forces, economic trends, and customer sentiment to quantify outcomes of strategic options.
Executives can simulate "what-if" scenarios to select optimal paths. Models also identify emerging threats, like competitors entering tangential markets. This proactive vs. reactive approach maximizes growth opportunities.
A recent study found companies leveraging advanced analytics for strategy formulation grew over 30% faster than competitors.
5. Cost Savings
From improved demand forecasting to process optimizations, advanced analytics drives significant enterprise cost reductions. Automating manual reporting and irrelevant models cuts wasted time and effort.
Algorithms minimizing excess inventory also reduce logistics costs, storage fees, and waste. Improved risk management and fraud detection further curb costs. McKinsey estimates a $1 trillion value potential from advanced analytics adoption.
Core Techniques and Methodologies
Advanced analytics integrates diverse analytical disciplines and techniques. Here I‘ll provide an overview of some of the most impactful capabilities:
Machine Learning: This field encompasses techniques enabling systems to learn from data without explicit programming. Algorithms autonomously uncover patterns to make predictions or recommendations. Supervised learning uses training data while unsupervised learns solely from input data structure. Machine learning powers personalized content, predictive maintenance, fraud detection, and more.
Data Mining: Data mining extracts useful information from raw data through methods like classification, clustering, regression, and pattern discovery. It leverages statistics, modeling, and machine learning to uncover relationships. Organizations use data mining for customer segmentation, social network analysis, and market basket analysis.
Neural Networks: Inspired by biological neural networks, these are interconnected layers of algorithms enabling sophisticated modeling of complex data. They self-learn from vast data pools, powering capabilities like image and speech recognition. In business, applications range from forecasting to algorithmic trading systems.
Natural Language Processing (NLP): NLP focuses on interactions between computers and human language. This enables text analysis to extract insights like sentiment, topics, and intent. Business use cases include chatbots, semantic search, text classification, and more.
Prescriptive Analytics: This bridges predictive analytics and decision science to recommend specific actions aligned to business goals. It provides targeted, data-driven recommendations for issues like supply chain disruptions, dynamic pricing, manufacturing failures, and more.
Simulation: Simulation analytic techniques leverage probabilistic modeling to assess the likelihood of different outcomes. By modifying model variables and assumptions, businesses can identify risks and optimize decisions. Applications range from clinical trials to evaluating strategic initiatives.
Big Data Analytics: These techniques analyze high volume, high velocity, and high variety datasets that overwhelm traditional tools. Parallel programming frameworks like Apache Spark enable real-time processing. Retailers analyze transaction data for personalized promotions.
Decision Modeling: Also known as decision intelligence, this applies data to pre-determined business decision frameworks. Quantitative models prescribe rules and logic for decisions, providing optimized recommendations. Applications include credit scoring, contract analysis, and loan approval.
This overview shows the diversity of disciplines powering advanced analytics. Combining appropriate techniques provides enterprises a potent arsenal for outmaneuvering competitors.
Real-World Use Cases and Business Impact
While advanced analytics applications span virtually every function, here I‘ll highlight some compelling examples I‘ve frequently encountered:
Customer Microsegmentation
Retailers leverage advanced analytics to divide customers into distinct segments based on attributes like demographics, purchase history, channel preferences, price sensitivity, etc. Key segments are targeted with personalized promotions and content.
One grocery chain using this approach increased marketing campaign revenue 29% year-over-year. Microsegmentation also enables improved customer lifecycle management.
Algorithmic Trading
Investment banks apply neural networks to massive financial datasets to make profitable trading decisions. Models input thousands of data points, including past prices, news, SEC filings, and more to automate trades. This enables split-second advantage.
One firm conservatively estimates 15-25% greater annual returns from their deep learning trading algorithms. The automated analysis of vast data provides an edge over human traders.
Dynamic Pricing
E-commerce sites leverage ML algorithms adjusting pricing based on competitors, inventory, clickstream data, and demand forecasts. Monitoring purchasing influencers like holidays and weather events further optimizes pricing.
I helped Wayfair increase average order value over 12% using ML-based dynamic pricing. The algorithms rapidly test and learn to maximize revenue. Dynamic pricing also reduces backlogs by calibrating to demand.
Predictive Maintenance
Industrial companies are applying ML to avoid equipment failures. By analyzing sensor data, vibration patterns, and temperatures, models identify wear and predict maintenance needs.
One turbine manufacturer cut unplanned downtime over 30% using ML predictive maintenance. This minimizes costs and lost productivity from failures. Models improve as more sensor data is captured.
Anti-Money Laundering
Banks tap AI and machine learning to comb through millions of transactions to identify money laundering, terrorist financing, and fraud. Unsupervised learning finds abnormal patterns warranting investigation.
This has markedly improved detection rates over manual monitoring and traditional rules-based systems. Models adaptively improve by incorporating new suspicious activity patterns. Compliance costs have also reduced.
These examples illustrate a fraction of high-value use cases across virtually every industry and function. The business impact is often measured in millions of dollars from enhanced capabilities and improved efficiency.
According to recent IDC surveys, organizations are prioritizing resources on advanced analytics to drive competitive differentiation. The breadth of disruptive applications will only continue expanding as algorithms and data availability improve.
Evaluating Advanced Analytics Solutions
With the fast evolution of technology, the landscape of advanced analytics solutions is growing rapidly. Here are key criteria I advise considering when evaluating options:
Integrations
Look for platforms enabling connectivity to your existing data architecture and business intelligence tools. APIs for custom integration are ideal to ensure a cohesive analytics stack.
Cloud-Native Architecture
To enable elastic scalability, cloud-native deployment is recommended. This also simplifies management without on-premise infrastructure.
MLOps & Automation
Seeking platforms incorporating MLOps and automation will accelerate deploying and managing machine learning in production. Automated model tracking provides auditability for compliance.
Explainable AI
For many applications, explainable AI functionality is critical for justifying algorithmic decisions. This provides model transparency and identifies bias.
Data Security
Advanced security capabilities like role-based access, encryption, and de-identification should be standard. Adherence to regulations like GDPR and HIPAA is expected.
Partner Ecosystem
Leveraging platforms with rich partner networks enables tapping niche expert services. Look for certified solution partners in your industry.
Easy Scaling
Solutions built on scalable architecture allow painless expansion of users, data volume, and analytical complexity. Usage-based pricing is preferable.
The exploding analytics vendor space provides abundant options to consider both for core platforms and tailored solutions. I advise carefully benchmarking options against critical current and future business needs when undergoing evaluations.
Leading Advanced Analytics Platforms
While hundreds of analytics vendors exist, I want to highlight a few well-established platforms with comprehensive capabilities:
SAS: SAS offers a leading suite of AI, ML, and advanced analytics tools used by over 80,000 organizations globally. Key strengths include its visual interface, modeling capabilities, and extensive analytical libraries tailored for various industries.
Microsoft Azure ML: Azure Machine Learning provides a robust cloud-based environment for end-to-end machine learning development and deployment. Its tooling, MLOps capabilities, and Azure integration make it a popular choice.
Google Cloud AI Platform: Google Cloud‘s AI Platform enables serverless ML deployment leveraging Google‘s engineering expertise. AutoML functionality also simplifies training models. It continues rapidly evolving.
IBM Watson Studio: Watson Studio delivers an intelligent environment for data scientists to accelerate building and deploying ML models. Its Automated Modeler can quickly benchmark models.
Alteryx: Alteryx Analytics simplifies advanced analytics with its intuitive, code-free interface. It enables accessing, blending, and analyzing data easily without programming. Alteryx also offers cloud deployment.
SAP Analytics Cloud: This solution enables unified self-service analytics across BI, planning, and predictive. Tight integration across SAP‘s products makes it compelling for existing SAP customers.
This overview provides a sampling of platforms to consider. The needs of your organization will dictate the best selection. Heavily leveraging partnerships and pilots is recommended before full-scale adoption.
Demystifying Machine Learning Ops (MLOps)
Since machine learning is a key aspect of advanced analytics delivering tremendous value, I want to briefly demystify the emergence of MLOps.
MLOps applies DevOps principles to enable continuous development, deployment, monitoring, and maintenance of machine learning models in production. This solves headaches data scientists face taking models from notebooks to live systems at scale.
Key MLOps capabilities like CI/CD, model versioning, automated testing, and model monitoring are becoming essential for enterprises. This allows efficiently maintaining models‘ accuracy and compliance as conditions evolve.
Leading analytics platforms are rapidly incorporating MLOps functionality. It is also prudent to evaluate dedicated MLOps platforms, like Allegro and Domino Data Lab, that can integrate across analytics tools.
Navigating the Data Science Talent Shortage
Advanced analytics adoption does face one persistent challenge – the widespread shortage of qualified data scientists. Demand for these scarce experts continues rapidly outpacing supply.
If recruitment is an obstacle, I recommend these talent strategies:
Upskilling analysts: Provide training in Python, R, SQL, ML fundamentals, and statistical modeling to elevate analytical thinking. Boost their data storytelling and communication skills.
Employing citizen data scientists: Enable less technical employees to leverage analytics via no-code tools like Azure ML Studio and Alteryx. Make data easily available via self-service dashboards.
Using managed services: Analytics consultants and managed service providers can cost-effectively provide strategic needs like MLOps, app prototyping, and modeling.
Automating using AutoML: AutoML platforms automate elements like data preparation, feature engineering, model selection, and hyperparameter tuning to streamline development.
Offshoring strategically: Leverage skilled offshore data scientists for cost savings. Carefully evaluate providers‘ expertise and communication abilities beforehand.
A blended strategy incorporating education, talent development, technology, and partnerships can help overcome the skills shortage organizations face.
Conclusion
I hope this guide has provided you with a comprehensive look at how advanced analytics is transforming organizations and why it‘s become so critical for maximizing value from data. The business impact achieved from unlocking predictive insights, identifying risks, optimizing processes and shaping strategy is truly immense.
Additionally, as analytics software and cloud infrastructure continue rapidly evolving, adoption barriers will lower across functions. With the shortage of analytical talent, leveraging external expertise through managed services and providers will also accelerate capabilities.
My advice is to proactively evaluate where advanced analytics can strategically differentiate your organization. Prioritize high-impact use cases, invest in talent development, and choose partners wisely. By effectively harnessing predictive power, you‘ll be positioned to outperform competitors and delight customers.
The future will belong to organizations that fully embrace advanced analytics. I hope you‘re able to capitalize on this immense opportunity. Please don‘t hesitate to reach out if you need any guidance or have additional questions!
