Aswini Thota is an Analytics and AI leader who solves organizational and business problems by leveraging data. He always believed in the power of data and was amazed at what insights we can grasp from it. He is currently working as a Principal Data Scientist at Bose Corporation, and in the past has served several data leadership roles at Fidelity Investments.
The ability to position data as a competitive business asset is one of the key reasons some companies enjoy the longstanding trust of their customers. Data-driven organizations strive to leverage historical information to help businesses make critical business decisions instead of relying on intuitions. Data-Driven Decision-Making motivates organizations to use facts, measures, and data to influence crucial business decisions that align with their goals, objectives, and projects.
While the belief that data-driven decisions yield the best business results stayed constant over the years, the methods used to make these decisions evolved rapidly. Until recently, the most popular way to understand information is descriptive analytics.
Descriptive analytics involves evaluating data to answer questions, detect trends, and extract insights. To analyze data, business leaders employ business intelligence tools and spreadsheets, among other means, to understand historical data in context. These can aid in examining data from various perspectives and the creation of visualizations that bring the story to life.
The last decade has witnessed outstanding innovations in data storage and analytics. With the invention of big data storage frameworks such as Hadoop, organizations realized that they could first store the data in its original fidelity and think about using them later. Data computation has also become cheaper. Inexpensive data storage and efficient computation have motivated organizations to add newer analytics frameworks to their decision-making process. The frameworks that allow businesses to be more predictive and prescriptive.
The frameworks that allow organizations to store and retrieve big data, develop machine learning-based predictive and prescriptive analytics, and present insights to make business decisions are collectively known as data science.
In its inception, data science was perceived as a research function where PhDs typically spent weeks and months conducting groundbreaking research. Fast forward a few years, it’s now hard to find a progressive organization that’s not using AI in its mainstream products.
According to a recent McKinsey poll, 56% of businesses use AI in at least one business function. Today’s organizations are using AI to make critical business decisions; below are a few examples of how AI is used in core enterprise functions:
Marketing organizations play a critical role in conceptualizing promotions, drafting the right message, and creating ads to reach the right customer through the right channel. Companies may use AI-powered tools to understand their customers better, develop more engaging content, and run targeted marketing campaigns to achieve success. AI can give precise insights and suggest innovative marketing techniques based on customer data that can instantly impact revenue.
AI-enabled tools can monitor media outreach and provide insights into marketing initiatives, highlighting what drives engagement, traffic, and income. As a result, businesses can develop efficient marketing strategies that entice their customers.
HR departments are taking advantage of AI/ML technology improvements, even though HR is naturally perceived as a “human” position that requires a personal touch.
AI is now being used by businesses to assist HR personnel in making better decisions based on data-driven insights and intelligent automation.
ML technology may be used to interpret job content and job seeker intent in a way that goes well beyond keyword-based methods throughout the recruitment process. This assists candidates by finding suitable employment more quickly while also helping businesses attract and convert higher-quality prospects.
The opportunity to embed AI into the sales process improves the company revenue and enhances the customer experience. Advanced analytics-based solutions can look at the customer attributes along with previous sales data to predict the propensity to buy.
Click-stream data combined with CRM information can provide personalized recommendations to customers based on their intention.
The world is going through severe supply chain shortages in every industry. From lumbar shortages to semiconductors, the global supply chain shortages can be felt in most commodities and assets we try to buy.
Organizations are trying to do more with less, and planning existing resources will decide the winners and losers. A sub-discipline of AI, time-series forecasting can look at historical sales figures and predict future demand.
This AI-driven approach toward demand forecasting will help organizations produce and distribute their products judiciously.
A common trait among most successful organizations is that they put their customer in the center. This means providing legendary customer service and leaving them with a world-class customer experience. Natural language processing (NLP), a sub-field of AI, is helping organizations to develop products such as chatbots that are capable of conversing with their customer.
This allows customers to find and retrieve answers to their everyday questions at their convenience. It also enables organizations to re-distribute their customer service agents to more critical areas.
Data is now incorporated into every industry, and it is the new oil that’s powering the innovation engine. AI is intertwined with all sectors and functions in the current digital economy. AI grew from a research-oriented function to a business unit, driving change across other vital functions.
Data is a huge competitive advantage and source of growth for organizations worldwide. Data science can disclose hidden insights into a company’s daily operations, allowing it to develop more efficient and productive operating procedures, price risks, and predict market patterns.
With every successful AI project, companies increasingly understand the practical benefits of AI and how it can boost day-to-day operations.
Compared to other functions, Data Science is still in its infancy. The long-term success of AI and data science depends on their ability to generate fair and unbiased predictions. Though data science is considered a critical differentiator, many business leaders opine that the models lack transparency and are not always explainable. The data scientists should balance the prediction accuracy and model explain ability.