Business Analytics in the Era of Artificial Intelligence and Machine Learning

Peter Sondergaard, the head of global research at Gartner famously remarked – “Information is the oil of the 21st century, and analytics is the combustion engine”. 

In today’s world where exabytes of data are stored by businesses every single day, this holds. Business analytics has come a long way from its humble beginnings in the late 19th century. Most businesses today are making use of analytics engines before making any decision – whether it is related to purchasing resources, optimizing supply chains, or even making marketing campaigns.

Business analytics refers to the process of exploring an organization’s data to uncover patterns, trends, and insights that drive strategic decisions. It encompasses various techniques such as statistical analysis, predictive modeling, and data mining to extract actionable insights from structured and unstructured data sources. These insights help organizations identify opportunities for improvement, make informed decisions, optimize business processes, and gain a competitive advantage in today’s rapidly evolving business landscape.

Machine learning (ML) on the other hand is the science of algorithms that help machines learn and improve from data analysis without explicitly programming them. It is considered to be a discipline of artificial intelligence (AI) that provides machines with the ability to automatically learn from data and past experiences while identifying patterns to make predictions with minimal human intervention. Given the large volumes of data available to businesses, this is becoming an increasingly popular solution available to businesses these days.

While Machine Learning (ML) and Business Analytics are both methodologies used to extract actionable insights from data to aid decision making and have significant predictive power, they still differ from each other in numerous ways.  Business analytics typically uses predefined rules and algorithms to analyze structured data and generate insights, while ML involves training algorithms on data to learn patterns and make predictions without being explicitly programmed and can also work with unstructured and more complex kinds of data.  Moreover, Business Analytics uses simple statistical techniques to understand past performance, identify trends, and optimize current processes to improve efficiency and effectiveness.  ML, on the other hand, is geared towards building predictive models that can automate decision-making processes and optimize outcomes based on data.

 

Machine Learning in Analytics Projects

Now while ML offers valuable capabilities for data-driven decision-making and automation, it is essential to recognize that it is not a silver bullet for all analytical problems. ML should be approached as a tool within a broader toolkit of methodologies. Its adoption should be guided by careful consideration of the problem context, data quality, model interpretability, ethical considerations, and ongoing maintenance requirements. Machine Learning can be really expensive in terms of storage, compute, training time and so many other resources and we need to be careful while working with them. 

There are innumerable cases of companies going bankrupt because of a lack of business analytics.  A recent example of this happening is the case of Quibi. Quibi was a short-form mobile video platform launched in April 2020, to provide high-quality content optimized for viewing on mobile devices. Quibi’s subscription-based revenue model relied heavily on attracting a large user base willing to pay for premium content. 

However, the company’s failure to analyze market trends, consumer behavior, and competitive dynamics led to a flawed monetization strategy. Quibi struggled to differentiate itself in a crowded market dominated by established players like Netflix, Hulu, and YouTube, ultimately failing to generate sufficient revenue to sustain its operations. Despite significant investment and high-profile backing from Hollywood executives and celebrities, Quibi shut down just six months after its launch, filing for bankruptcy in October 2020.

So how do we ensure that our Machine Learning and AI projects are going to be worth the effort required to build them? With the help of effective business analytics of course! Business Analytics is simpler, easier to understand and cheaper than ML. It can help in assessing the potential business impact of ML projects. 

By analyzing historical performance metrics, market trends, and customer behaviors, businesses estimate the expected return on investment (ROI) and prioritize ML projects based on their potential value to the organization. Other than these – Business Analytics also plays a crucial role in guiding machine learning projects by providing insights into not just the impact but also the relevance and feasibility of potential projects. 

 

Analytics Guides Machine Learning Efforts

A ‘prime’ example of a business leveraging business analytics and machine learning harmoniously to optimize its supply chain operations seamlessly is Amazon. By analyzing historical sales data, inventory levels, and demand forecasts through Business Analytics, Amazon accurately predicts customer demand, optimizes inventory levels, and reduces fulfillment costs. Amazon’s fulfillment centers use machine learning algorithms to improve warehouse operations and logistics. 

By analyzing order fulfillment patterns, inventory turnover rates, and transportation logistics and generating analytics, Amazon optimizes warehouse layout, streamlines picking and packing processes, and reduces delivery times to meet customer expectations. Amazon employs effective business analytics and machine learning to mitigate supply chain risks, such as stockouts, delivery delays, and inventory shortages. 

By proactively identifying potential disruptions and bottlenecks in the supply chain, Amazon implements contingency plans, adjusts inventory levels, and optimizes transportation routes to ensure uninterrupted operations and customer satisfaction through ML algorithms.

From the above example we can see that Business Analytics aids in identifying patterns, trends, and correlations within datasets, informing the selection of machine learning algorithms and models. It assesses data quality and reliability, ensuring that machine-learning solutions align with business objectives. Businesses leverage analytics and machine learning for strategic decision-making, optimizing operations, and managing risks effectively. Analytics techniques such as predictive modeling and simulation can be used to forecast the expected outcomes of ML initiatives and assess their feasibility and profitability.

 

Analytics in ML Projects 

Let us now take the example of Netflix – primarily an ML company which uses a combination of business analytics and machine learning algorithms to make strategic decisions about content acquisition and production. By analyzing viewer preferences, viewing history, and engagement metrics, Netflix identifies trends and patterns in audience behavior and makes use of them in decisions about which original series or licensed content to invest in. Once that is done, ML algorithms optimize the user experience by suggesting personalized content based on individual preferences leading to increased user engagement and retention. 

By monitoring viewer feedback and engagement metrics continuously, Netflix also assesses the performance of its content library via analytics and adjusts its content acquisition and production strategies accordingly. Business Analytics helps Netflix mitigate the risk of investing in content that may not resonate with its audience, improving the platform’s competitiveness and profitability. 

Even Netflix’s Personalized Content Recommendation System regularly goes through several rounds of business analytics scrutiny and quality assessments to ensure that the trends are in line with the expectations of experts before being deployed into production helping it become one of the best Customer Experience providers in the world. 

 

Machine Learning Takes Analytics to the next level

Finally, let’s take the case of Capital One. Capital One uses analytics and machine learning to assess credit risk and make informed lending decisions. By analyzing applicant data, credit history, and macroeconomic indicators, Capital One predicts the likelihood of default and determines appropriate lending terms to maximize profitability while minimizing credit losses. Capital One’s risk management systems then use machine learning algorithms to automate credit scoring and underwriting processes. 

By analyzing large volumes of applicant data and historical loan performance, Capital One streamlines loan approval processes, reduces manual review efforts, and expedites loan disbursement to customers. Capital One employs advanced analytics and machine learning to detect and prevent fraudulent activities in real time. By monitoring transaction data, user behavior, and spending patterns, Capital One can identify suspicious activities, flag potentially fraudulent transactions, and take immediate action to mitigate financial losses and protect customer assets.

What seems like a simple and traditional problem of banking analytics becomes supercharged and powerful as an autonomous applicant screening and fraud verification system with the help of machine learning algorithms. The fact that the algorithms can identify fraudulent behavior and anomalies by themselves makes it even more impressive and proves that ML can take analytics to the next level with the right effort and under the right guidance.

 

Conclusion 

Integrating business analytics and machine learning into organizational processes, as we have seen here, increases the value of the project exponentially.   But it also presents numerous challenges and considerations. It requires a multidisciplinary expertise encompassing statistics, data science, machine learning, and domain knowledge, which can be difficult for organizations to assemble and retain. Moreover, machine learning relies heavily on high-quality data for training accurate models and making reliable predictions, necessitating investments in data collection, infrastructure, and storage capabilities. Data quality issues, especially regulatory, privacy, and governance related issues, further complicate matters. Monitoring and tracking key performance indicators (KPIs) are essential for evaluating project effectiveness, but failure to do so can lead to missed opportunities or unexpected outcomes.

Continuous testing and validation of machine learning models are crucial to ensure accuracy, reliability, and relevance, with past failures like Microsoft’s AI chatbot Tay and Amazon’s biased recruiting tool serving as cautionary examples. By implementing rigorous testing protocols, thorough data cleaning and validation efforts, and well thought governance mechanisms, organizations can mitigate risks and maximize the effectiveness of their initiatives. 

Additionally, conducting internal testing, or “dogfooding,” allows organizations to assess model performance in real-world scenarios and make necessary adjustments. Overall, maintaining trust, transparency, and integrity in data-driven initiatives is paramount for achieving sustainable success in today’s competitive business landscape.

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