How Much ML is Needed for Data Analysis?

Last Updated : 25 Jan, 2024

Data analysis has become a critical component of decision-making across industries. With the exponential growth of data, businesses are increasingly looking for valuable insights to stay competitive. Here’s where machine learning comes in. Machine learning provides advanced analytical capabilities to uncover patterns, make predictions, and optimize processes based on historical data. However, an essential question organizations face is: how much machine learning capability is needed for impactful data analysis?

In this article we will explore the considerations around finding the right balance of machine learning for optimizing data analysis.

Table of Content

Why Machine Learning is Important for Data Analysis ?
Considerations for Applying Machine Learning
Best Practices for Applying ML in Data Analysis
Analyzing The Extent of ML in Data Analysis
Everyday Use Cases for Applying ML in Data Analysis
The Crucial Role of the Human in the Loop
Emerging Opportunities for Advanced ML
Conclusion

Why Machine Learning is Important for Data Analysis ?

Before deciding to incorporate machine learning models, it is critical to understand what types of analyses are possible using basic analytic approaches. Aggregation, visualization, and reporting are used in descriptive analytics to provide insights into what happened. However, it does not help predict future outcomes. Diagnostic analysis digs deeper to understand the root causes behind metrics.

While it answers the “why” question, it still does not forecast the future. Prescriptive analysis recommends data-driven actions but cannot adapt as new data comes in. While these basic analytics provide value, they have vital limitations when handling complexity, unpredictability, and identifying unknown relationships. This is where machine learning closes the gaps. The proper machine learning solutions can detect hidden patterns, make probabilistic predictions, and continuously optimize to provide the most accurate insights. However, system mastering requires significant record volumes, engineering attempts, and computing resources to paint correctly. Finding the premiere stability is vital.

Considerations for Applying Machine Learning

Here are five key considerations when evaluating the extent of machine learning to apply for data analysis within an organization:

Identify Critical Business Decisions: The first step is identifying high-value business decisions that can benefit from advanced analytics. These include predicting customer churn, forecasting regional demand, optimizing marketing spend, or personalizing recommendations. The business priorities and use cases should drive the data analysis approach rather than a general desire to incorporate machine learning.
Assess Data Quality and Volume: Machine learning algorithms perform better with large volumes of high-quality, relevant data. Before training ML models, assess if your data depth is adequate. For example, predicting rare events like fraud requires collecting niche datasets. A rules-based approach may suffice if data volume is low rather than struggling to train ML models.
Map Analytics Maturity: Every organization has a different analytics maturity level. Before advanced ML initiatives, those still doing basic reporting may first focus on getting the proper data pipelines, descriptive analytics, data visualization, and business intelligence capabilities. Being realistic about existing infrastructure, skills, and culture will inform the analytics expansion roadmap.
Develop In-House ML Expertise: While it is tempting to skip to fancy machine learning capabilities, having the right in-house skills is essential first. Recruiting data scientists and ML engineers and supporting their continual training is vital, even if they eventually rely on cloud services or ML automation platforms for deployment. Without expertise, organizations will not be equipped to frame problems, preprocess data correctly, interpret model outputs, and ensure model fairness.
Leverage Cloud and Automation Options: Organizations can accelerate the integration of machine learning without intensive coding and infrastructure investments thanks to mature cloud platforms and ML automation tools like Azure ML Studio, DataRobot, and H2O Driverless AI. Pre-built and automated machine learning solutions have democratized access to advanced ML capabilities across industries. However, they still require foundational data understanding to train, validate the output, and provide adequate guardrails properly.

Best Practices for Applying ML in Data Analysis

While you take a cloister look at the above considerations, there are also certain best practices that guide you at the time of applying ML in data analysis. Let’s dive deep and look at these best practices in detail:

Start with a Relevant ML Proof-of-Concept: Rather than overhauling entire analytics processes with ML, identify a well-defined business problem and run an 8-12 week proof-of-concept project to test capabilities. Focus the project on a single use case with a committed business team rather than a technical exercise. Learnings from this project can inform your broader ML adoption.
Build Hybrid Analytic Approaches: The most valuable insights often come from blending different analytic techniques, e.g., using machine learning for predictions while applying business rules to eliminate specific recommendation scenarios. As there is rarely a single source of truth, aim to leverage both ML and non-ML-based techniques.
Apply Guardrails to ML Outputs: Monitoring machine learning models to catch unexpected errors or bias issues stemming from insufficient data is essential. Establish human review processes, test ML outputs versus benchmarks, document model limitations, build cross-checks with other models, implement fairness constraints, and continually enhance the model’s accuracy.
Focus on “Good Enough” Over Perfection: There is no perfect machine learning model, as new data introduces entropy over time. Rather than over-optimizing for theoretical accuracy or wasting resources on incremental gains, focus on reaching the model effectiveness needed to address the business problem. Then, monitor and update as necessary.
Build Trust & Understanding: Adoption will be negatively impacted regardless of accuracy levels if business leaders do not adequately understand ML outputs or lack trust in the technology. Fostering stakeholder education, openly discussing risks, and encouraging feedback loops are critical for driving engagement. Leadership buy-in stems from their comfort with deploying analytics.
Embed ML Engineering: To properly monitor models and ensure optimal integration with applications, have machine learning engineers partner closely with software engineering teams rather than operating in a silo. DevOps approaches applying version control, collaboration rituals, and continuous delivery pipelines lead to better ML applications.
Enrich Data Over Time: Each model is equal to the data used to train it. Even with advanced machine learning, more data can be needed to improve model accuracy. Develop a data governance strategy for capturing, connecting, cleansing, and enhancing varied datasets over time to drive improved insights.

Analyzing The Extent of ML in Data Analysis

Determining the proper extent to apply machine learning techniques for data analysis depends significantly on the business context, data prerequisites, and in-house ML capabilities within an organization. As a rule of thumb, focus ML on addressing clearly defined analytical gaps with high business value. Be realistic about existing analytics maturity. And blend ML with both existing and new data approaches to drive impact.

While advanced machine learning promises many benefits, it requires thoughtful adoption. Organizations can optimize how much ML capability is embedded across their data analysis processes by following best practices and focusing machine learning applications on critical areas with adequate data and expertise. In many cases, simple and flexible solutions blended with other techniques prevail over complex or theoretical machine learning models. As analytics needs to evolve, so will the integration of ML to stay competitive.

Everyday Use Cases for Applying ML in Data Analysis

To further understand the appropriate level of machine learning that can be beneficial based on the analytical task, it helps to explore some everyday use cases within business functions:

Marketing

Predictive lead scoring – High ML: Identify high-value leads most likely to convert based on dozens to hundreds of attribute combinations. Requires significant historical conversion data.
Campaign propensity modeling – Medium ML: Estimate response rate to marketing campaigns through supervised learning techniques. It may need more historical responses.
Message personalization – Low/Medium ML: Tailor messaging across channels using segmentation models with a hybrid rules-based approach.
Market basket analysis – Low ML: Understand co-purchase trends with essential association rule mining. Useful for cross-sell.

Sales

Territory assignments – Low ML: Set sales territory boundaries based on geographic concentrations of customers using clustering algorithms.
Demand forecasting – High ML: Predict revenue by market based on historical performance, economic trends, and other signals. Requires advanced time series ML techniques.
Churn analysis – Medium ML: Use classification techniques to determine customers most likely to cancel services. BeneficialIt is helpful, but many causes require operational fixes.

Finance

Anomaly detection for fraud – High ML: Identify abnormal transactions not fitting expected patterns in complex high-volume data—advanced unsupervised ML capability required.
Cash flow prediction – Medium ML: Forecast short and long-term cash positions. Time series ML is helpful, but causal understanding is also important.
Credit risk assessment – Low/Medium ML: Supplement policy rules with ML to classify subprime applicants. Limited historical default data requires simple models.

HR

Employee attrition modeling – Medium ML: Predict workers likely to leave based on tenure, performance, and engagement indicators. Focus on small data techniques.
Job profile recommendation – Low ML: Suggest open jobs to the talent pool based on skills match and stated preferences—mainly rules-based matching.
Learning personalization – Low ML: Recommend training content using collaborative filtering—beneficial but small and sparse dataset.

IT Ops

Infrastructure optimization – Medium ML: Tune resource allocation across technology assets based on usage analytics. Reinforcement learning helps.
Issue remediation – Low ML: Provide suggestions for solving system incidents based on matching error codes.
Event correlation detection – High ML: Analyze a multitude of infrastructure events across apps and networks to identify failure correlations. Requires advanced ML.

The need for machine learning varies greatly depending on the function and use case complexity. Many impactful business decisions can improve with basic descriptive and diagnostic analytics – an important reminder not to over-index on ML. Cross-functional data analysis also plays a crucial role in contextualizing signals from different domains.

The Crucial Role of the Human in the Loop

While machine learning provides advanced analytical capabilities, human oversight remains critical in ensuring appropriate and ethical usage for crucial decisions. Some best practices include:

Human-in-Loop Model Training : Having subject matter experts work alongside data scientists during the model development can improve the resulting insights. Humans can provide relevant business, process, and domain expertise to help prepare data, select useful features, interpret results, and identify potential bias issues that algorithms miss.

Human-in-Loop Output Validation: Well-trained ML models can still produce inconsistent or misleading outputs on never-before-seen data. Experts reviewing samples of model predictions not only safeguard accuracy but also prevent unintended consequences from automation. Organizations can code these validations and feedback loops to enhance ML model intelligence over time automatically.

Human-in-Loop Exception Handling: Where decisions involve risk factors like legal exposure or health impacts, organizations implement human reviews for outlier scenarios that fall outside a model’s reliable operating parameters – for example, extensive loan approvals, this helps minimize risks from incorrect predictions.

Human Judgment in Deployment: For decisions involving morality, ethics, and social norms, organizations maintain human judgment in determining how ML model outputs get deployed rather than simply enabling automated actions. This helps uphold corporate values and fairness standards when dealing with people’s well-being.

With growing version complexity, the human interpretability of outputs will become more complex. However, ongoing human involvement in machine-mastering approaches remains critical for agreeing with, protecting, and responsible innovation – specifically for decisions impacting human beings’s lives materially or socially. Overall, the human within the loop will continue to play a crucial balancing function in complementing algorithmic intelligence with human information.

Emerging Opportunities for Advanced ML

While current machine learning adoption for data analysis may meet a majority of needs, there remain cutting-edge techniques that show high future potential as data and analytics maturity increases further:

Reinforcement Learning: Great for optimizing decisions in complex, uncertain environments based on maximizing “reward” signals over time. Useful for personalized recommendations. Promising for predicting fast-changing time series but less structured data requirements currently pose adoption challenges.
Generative AI: Powerful deep learning techniques that can generate highly realistic synthetic data for training other ML models when real-world data is insufficient – beneficial for new products or rare events. However, output explainability remains a crucial limitation.
Causality Networks: Causal machine learning models uncover authentic cause-effect relationships from observational data – helping answer what would happen under different decisions by removing spurious correlations. It is very promising for healthcare, finance, and policy decisions but is still an emerging capability.
Neuro-symbolic AI: Combines the reasoning power of computer programming with the flexibility of neural networks for advanced explainability and reliability. Allows rule validation against black box ML models and vice versa. They are gaining adoption for critical use cases like drug discovery. As tools like Azure Cognitive Services, TensorFlow, Pytorch, and Spark MLlib drive democratization, more groups can experiment with advanced techniques. AutoML and MLOps are also streamlining development pipelines.

However, rigorous evaluation of test cases and die-off-based transition is advised before refreshing entire production systems. With a strategic roadmap leveraging proven early techniques and experimenting with emerging ones, organizations can build enduring machine-learning competencies over time.

Conclusion

While machine learning unlocks many advanced analytical capabilities, the business context should drive its application rather than a general desire to use ML. Once adequate data, infrastructure, and internal skills are available, ML solutions can complement existing approaches to unlock new insights, leading to significant competitive advantages.

However, the human element remains indispensable for providing oversight, guardrails, and ethical usage of machine learning – especially for consequential decisions impacting people’s well-being. Further breakthroughs in reinforcement learning, generative AI, and causality networks promise to expand ML capabilities even more. However, a pragmatic focus on developing cross-functional competencies with the right foundations first is critical to harnessing the full potential of machine learning in data analysis now and in the future.