Ted Talk Discussion

Prompt 1: Big Data Is Better Data by Kenneth Cukier

From the coursebook, big data helps an organization collect massive data and identify new opportunities. With advanced analytics, organizations use big data to gain value, including efficient operations, innovative business moves, happy customers, and higher profits. Besides, every organization makes complex decisions as to progress. Big data aids in making those decisions with confidence, using in-depth analysis of the information you feed it with, such as what is known about the industry or customer. The most significant value of big data is its accuracy. Big data analytics offers a complete synopsis of everything you have learned as you develop the organization. According to Kenneth Cukier, big data means more data. It is an essential tool through which society is going to advance. With more data, we can see more similar things we are looking at and see new things. Kenneth Cukier (2014) explains that more data allows us to see things better and differently.

In Cukier’s case, big data helps us see that America’s preferred pie is not the apple, contrary to what is known, explaining the irony that more data let us see smaller ideas. Cukier defines machine learning as a branch of AI (artificial intelligence) that can learn and adapt without following explicit instructions and drawing inference from a data pattern using statistical and algorithm models (Kalali et al., 2019). Machine learning, according to Cukier, is the idea that computers can get smarter than human ability by throwing data at our computers and letting them figure out the issue itself. It implies creating machines that surpass our ability in tasks that we teach them. Examples of machine learning include self-driving cars, which feed many data around the vehicle it lets it figure out, including the traffic lights, when it stops, and so forth (Gupta et al., 2021). Other examples of machine learning include search engines such as Google and Amazon personalization algorithms. Big data is here to stay. According to Cukier (2014), it will continue to improve today’s models and allow more advancement in research.

Prompt 2: Predictive Analytics Delivers on the Promise of Big Data by Eric Siegel

Organizations use predictive analysis to establish customer reactions/responses or buying and promote cross-sell opportunities. With an effective predictive model, a business can attract, keep, and grow its profitable customer base. It is also used to improve the organization’s operations, including forecasting inventories and managing resources. According to Eric, predictive analytics empowers organizations not just to predict the future but to influence the outcomes by producing a prediction of each individual, which influences the organization’s action and treatment of each individual. The data effect is the idea that every data often has a story, and there is a valuable thing to learn from the story. For example, a credit card often used at a drinking place sends a signal that the user is more likely to miss payments regularly, and a credit card used at the dentist sends information that the owner is more organized and less likely to miss payments regularly (Papadimitriou, 2022). Hence, the data effect implies that data is always predictive (Eric Siegel 2016).

From the marketing perspective, my understanding of predictive analytics entails using present and historical data alongside statistical techniques, machine learning, and predictive modeling to predict consumer behaviors in the future (Tyagi, 2021). We can use Amazon’s landing page as an example of predictive analytics in marketing. The page shows personalized items for each customer while the company collects data based on customers’ past buying behavior and predicts what they are likely to buy in the future, leading to personalized recommendations. It does not ends there, amazon will also analyze products customers clicked on most in the past, what they expressed interest in, and more customers’ preferred suppliers to make more precise recommendations. The outcome is increased customer satisfaction and more sales in the future (Eric Siegel 2016). Hence, I agree with Eric Siegel’s presentation and argument that predictive analytics empowers organizations to predict the future and influence the outcomes.

Prompt 3: Why Smart Statistics are the Key to Fighting Crime by Anne Milgram

Anne Milgram’s predictive tool in the video is money-balling criminal justice, using statistics and smart data to aid decision-making. When she became New Jersey’s attorney general, Milgram quickly realized that many of the decisions within the criminal justice system were based on gut instinct and were mainly subjective. In a longing to apply the law in a more data-driven and objective way, Milgram assembled a team of researchers, statisticians, and data scientists to develop a universal risk assessment tool. It can offer judges and other key decision-makers within the criminal justice a data-driven methodology to enhance better decision-making using a Moneyball model. The Moneyball model has been used by Oakland A’s to pick the best players to help them win the game (Boylan, 2011). Moneyball analytics relies primarily on smart data and rigorous statistical analysis to produce the best outcomes. Every judge can use the tool as it relies on a universal data set (Anne Milgram 2013).

The three things the tool is used to predict include: a) whether someone is likely to commit a crime if they are released; b) whether someone is likely to commit a violent crime if they are released; c) whether one is likely to come back to court if they are released. The use of smart statistics helps the criminal justice system, including the judges, make objective decisions based on risk data-driven risk assessment, making it possible to detain high-risk criminals and release only low-risk criminals (Završnik, 2020). With smart statistics, judges can assess the New Criminal Activity Score, the likelihood of committing a new crime, and the potential risk of committing a violent crime. I agree with Anne since the lack of accurate data is a problem in the criminal justice system. The majority of decisions rely on instinct (Anne Milgram 2013). With data and analytical tools, judges and other decision-makers can make objective decisions and detain the right people.

Prompt 4:  Using “Big Data” To Read Business Signals by Hank Weghorst

Hank Weghorst uses the Avention tool to track databases of business signals of 25 million world’s most important corporations, ranging from multinational companies down to the corner grocery store, and keep this data so it can be shared with companies doing business to business (b2b) transactions or business to customer (b2c) transactions. Users can slice and dice that data and put them together how they like (Qadir et al., 2016). If you type a concept into the system, it goes to the databases of the 25 million companies and searches for the organizations that use the concept and the context it is used in real-time (Hank Weghorst 2014).

Business signals are reasons to reach out to potential customers. Ways to read business signals as described by Hank include comparing how that particular company performs in a particular area relative to other firms globally. For example, if one wants to know how businesses score in specific areas such as suitability, they pull the list of companies during the business in, say the United States or Brazil that are environmentally friendly. The system weighs the companies according as they come and go out of the cycle to include those that have shown interest or have recent evidence of sustainability involvement. Users can also look at the actual signals, tons of them in the system, to develop a sense of how to build a DNA pattern they would like to match against. Data cold war occurs when consumers become more informed about the industry, firsts, products, and services due to the increased availability of information (Sheth, 2020). As a consumer, I would use data cold war to select the business to buy from based on their values as being environmentally friendly (Hank Weghorst 2014). Yes, I agree with Hank that people make an informed decision about a business based on the availability of information. Information is vital, and it is only through data-driven decision-making that we make objective decisions.

Prompt 5: Making Data Mean More through Storytelling by Ben Willington

The visual representation used in Ben’s talk included topographical maps of different locations within New York City, showing cycling injuries within the city. The zones where people were getting cycling accidents are marked red and a repainted spot on the road to show impact (Reiter Law Firm, 2019). Other visual representations that could have fit the presentation include graphs showing the patterns of people catching caps. Ben’s talk describes the four steps to data storytelling: connecting with people’s experiences. Relate your story to everyday things people have experienced, such as brushing teeth. People relate with their experiences. The second step is to find the most critical insights that appeal to the audience. The third step is to craft storylines and design the data stories (Ben Willington 2015). Use visually represented data to help the audience locate the patterns in the data and map data to visual attributes (Knaflic 2015).  Ben’s data story makes sense and presents a real problem, a cycling accident within New York, which even the audience can relate to in real life. He has done a great job developing the story.