Topic 4: Business Analytics
Business Analytics (BA), the moment when we fully capitalise on the intellectual and computational power that we now possess to change the entire analytics landscape. It consists of a range of techniques, notably descriptive analytics, predictive analytics and prescriptive analytics. One common problem students face while learning BA is the difficulty in distinguishing between the 3 methods, hence here is a simple way on how we understand it:
Descriptive Analytics: Describes what happened (No judgement), descriptive techniques are used to look for patterns, e.g. clustering. Uses data aggregation and data mining to provide insight into the past and answer: “What has happened?”
Predictive Analytics: Describes what WILL happen. Predictive analytics are a variety of statistical techniques from modelling, machine learning, and data mining that analyse current and historical facts to make predictions about future events. New data (customer) will be classified, patterns found in historical and transactional data are used. Analysts can use regression to hypothesise and identify variables that are correlated with each other and find the right combination of variables and best model. Hence, predictive analytics use statistical models and forecasts techniques to understand the future and answer: “What could happen?”
Prescriptive Analytics: Describes what will happen, when it will happen and why it will happen. It synthesises data, mathematical sciences, business rules and machine learning to make predictions and suggests options on how to take advantage of a future opportunity or mitigate the future, E.g. smart pricing allows to predict future demands, simulate expected sales given different pricing strategies and undergoes optimisation of the pricing strategy to maximise revenue. Hence, prescriptive analytics use optimisation and simulation algorithms to advice on possible outcomes and answer: “What should we do?”
List of Interesting Business Analytics Tech News
1. The world’s most valuable resource is no longer oil, but data
In this era, data is likened to oil as it is revolutionising the way people do business and compete with each other. The impact data has on this day and age is similar to oil in the last century. A huge amount of data is concentrated in the hands of a few tech giants, Facebook, Google and Amazon, just like how there were oil monopolies. The emergence of the data era, however, revolutionize the way people compete. Whenever the tech giants detect potential rivals, they will take actions to stop it or create barriers to entry.
2. The dangers of bias in machine learning
The article discusses the potential biases we as humans are introducing when we input data into the machines to train them for machine learning. We can resolve this issue by sanitising the data inputs we give, but it can lead to more problems instead.
3. 91 pc of organisations are slow to advance in data and analytics: a global survey
The article talks about different data and analytics maturity levels of organisations based on the result of Gartner Global Survey, where 91% of organisations have not yet reached a “transformational” level, despite this area being a top investment priority for CIOs in recent years. Moreover, the survey also talks about the most common barriers that prevent organisations from increasing their use of data and analytics.
4. Small brands and influencers are worried that Instagram is choking off their traffic — just like Facebook did with publishers
This article is on Instagram’s algorithm that is based on machine learning. Instead of displaying posts in a chronological manner, the machine learning-based algorithm displays posts in an order that the machine thinks users would be more interested in.
5. What could Amazon’s Approach to Healthcare Look Like?
Following Amazon’s announcement of its joint partnership with Berkshire Hathaway and JP Morgan to create a health mega-company for its employees, this article discusses the possibility of what Amazon could bring to the table regarding healthcare.
6. The Fundamentals of Deep Learning
This article is a comparison between machine learning and deep learning. Deep learning is a form of machine learning technique which offers higher levels of performance with more labelled data. The article discusses the applications of deep learning and a detailed breakdown of how neural networks work, specifically how Convolution Neural Networks work.
7. Big tech makes large gains at our expense
This article tells us how big companies make use of the data we input onto these so-called “free” platforms to generate a huge sum of profits. In some cases, these data can even put us at a disadvantage of being charged a higher price when consuming certain goods after our personal information was sold to the respective companies. Thus, this puts an invaluable price to the personal data we share or willing give in exchange of another good.
8. Rise of the racist robots – how AI is learning all our worst impulses
This article talks about the problem of human prejudice becoming further ingrained in our society due to the data we use to input into our computational tools, including AI.
9. 2 More Big Data V’s — Value And Veracity Link: http://www.digitalistmag.com/technologies/big-data/2014/01/23/2-more-big-data-vs-value-and-veracity-01242817
The article summarises the additional two Vs that Big Data should have in the coming age apart from the three standards of volume, velocity and variety. The two Vs include value and veracity.
10. How ‘Amazon Go’ works: The technology behind the online retailer’s groundbreaking new grocery store
Link (Video): https://www.youtube.com/watch?v=NrmMk1Myrxc&t=1s
Amazon developed and employed a “Just Walk Out Technology” in its new convenience store, which removes the need to interact with cashiers and relies on an app-based shopping experience instead. This technology utilises computer vision, deep learning algorithms and sensor fusion which automatically adds an item into a virtual shopping cart as soon as the customer picks it up. The store relies on a vast and sophisticated data-gathering which transform the shopping experience by eliminating waiting time.
11. How data and machine learning are ‘part of Uber’s DNA
The article shares how Uber has a unique approach to machine learning. Traditionally PhDs, data scientists are hired to work on this issue, but Uber create machine learning as an internal service instead. Every team in the company can use this service as it has a graphical user interface. It highlights how machine learning work in something like Uber Eats and also how it adds value in areas where people initially didn’t think of.
12. SIA taps digital technology to understand customers’ needs, improve company operations
Despite dropping in rank for the best airline in the world, Singapore Airlines launched its newest Digital Innovation Blueprint where it will be working with its partners to better understand customers’ travel patterns by using data analytics. Besides, SIA is also working with A*Star to develop a way of predicting when to fix or repair critical parts of aircraft, to improve business efficiency.
13. The Amazing Ways Coca-Cola Uses Artificial Intelligence And Big Data To Drive Success
Being the world’s largest beverage company, it generates mountains of data and thus, relies on solid data-driven strategy to make business decisions at a strategic level. Coca-Cola has developed something similar to their virtual assistant AI bots such as Alexa and Siri which now resides in vending machines allowing greater personalisation and adaptation depending on locations and trends.
14. Machine Learning and Artificial Intelligence Trends in 2018
The article summarises the common forecasts made by the industry for 2018 and cites what experts say about the future of Machine Learning and AI. The article also summarises the key benefits of AI-driven marketing and the top technology priorities of 2018, one being the growth of conversational platforms, which are user interfaces that engage users with a company.
15. Business Analytics in Oil and Gas
This article talks about the applications of Data Analytics in the Oil and Gas Industry. It highlights the benefits of using data analytics in this industry such as the improvement of productivity in unconventionals, conventionals and midstream operations in the industry. This article also mentions the difficulties commonly encountered by companies when they use Data Analytics in their operations.
16. Justice taps big data in investigations
The article describes how data analytics can be used to help Justice Department crackdown on criminals like financial fraudsters and online dark web drug dealers. Such data analysis has successfully helped capture criminals in multiple cases.
17. Millions Of Refugees Could Benefit From Big Data — But We’re Not Using It
Today, 65 million people live as refugees, and while many countries are opening their borders to help them, they struggle to handle the massive influx. However, using predictive analysis on big migration data can be the key to saving these refugees. Sophisticated predictive analysis can help identify where refugees are likely to head next, and governments might reroute refugees to different countries or allocate resources and manpower to areas with the expected influx.
18. Four Ways Big Data Will Revolutionize Education
This article talks about how big data and data analytics can potentially help students attain better education. It allows better results, creation of mass-customized programs, improvement in learning experience real-time, and also better group projects.
19. Role Of Big Data Analytics In Gaming Industry
According to an industry estimate, servers of the gaming giant Electronic Arts alone accumulates around 40 terabytes worth of user-generated data every single day, thanks to the huge registered user base of around 275 million players worldwide. Zynga, the game development company that brought the famous game known as ‘FarmVille’. This prompted the company to bring those characters from the background to the foreground of the game screen in subsequent updates to the game.
20. What can football teach us about Data Analysis?
One of the main reasons football has become so competitive and hard for teams to stay champions is due to data analysis. Data analysis gives insight into everything that happens on the pitch, proving with statistics what are the effective things the team do and things that they need to cut out from their game. Moreover, data analytics has become the bread and butter of all the teams as it gives an edge to teams even before games as it helps with preparation against opposition by analyzing what are the opponents’ weak points are.
21. Data analytics have made the NBA unrecognizable
Link (Video): https://www.youtube.com/watch?v=nxcbuV0_WEM
Basketball is a very complex sport to analyze. Beginning in early 2009, the NBA began using a video system that tracked the movement of every player on the court, and the ball, for 25 times a second. Nearly every team in the NBA now has data analysts on their staff who work with coaches and player evaluators to maximize individual athlete’s talents and identify undervalued players.
22. Big data case study: How UPS is using analytics to improve performance
The article talked about the various business analytics tools used by UPS in logistic functions such as Network Planning tool and also to continue the development in existing tools such as On-road Integrated Optimization and Navigation(ORION). In addition, it discusses how UPS plan to overcome potential challenges in its utilisation of Big data and data analytics.
23. oBike harnesses Big Data to support the development of Smart City in Taiwan
By harnessing big data, oBike is now able to help the Taiwan government identify intelligent bike parking spots in the city. Apart from allowing commuters to locate more than 33,000 public bike parking spots via their mobile app, oBike also analysed OD data and presented commuters’ usage patterns in elaborate heat maps, in order to determine riding patterns, and evaluate the efficiency of the city’s transport system.
24. Why Every Business Should Care About Machine Learning Link: http://www.digitalistmag.com/cio-knowledge/2017/09/28/why-every-business-should-care-about-machine-learning-05381249
Many corporations are investing billions of dollars in machine learning and it is a crucial part of helping to keep the business competitive. Machine learning helps to improve business processes, creating a more intelligent enterprise system and making workspaces even more collaborative.
25. Big data blues: The dangers of data mining
Link (Video): https://www.youtube.com/watch?v=F7pYHN9iC9I (2min 28s)
Data mining has led to many unfortunate cases, despite its ability to help companies achieve greater business efficiencies and customise new products. Overzealous data mining can easily backfire as the threat of invasion of privacy is at stake. The link offers 13 commandments for data scientists to fulfil their job as well as preserve customer privacy.
26. Samsung’s ‘Connected Space’
Samsung is planning to introduce a connected pop-up store to collect and analyze data in real time, through the use of Samsung Nexshop, protocol cameras, tablets and interactive displays to understand customer behaviour. The whole idea would be following a Retail as a Service approach and these pop-ups can be rented by SMBs to utilize data analytics through IoT to provide retailers with data and insight on key factors.
27. Business Intelligence and Analytics Trends in 2018
Due to the rapid growth of Machine Learning (ML) and Deep Learning (DL), the Business Intelligence (BI) and Analytics Trends in 2018 are full of changes. Smart Data Discovery powered by ML will be the next game changer for the businesses of all sizes and shapes. In today’s business world, given the criticality of immediate and accurate decision making, all business users would want to have the capability of independently visualising and analysing data for an improved result. Therefore, one of the main trends is that Augmented Data Preparation will gain popularity among businesses as it allows business users to perform data-testing tasks without the assistance of IT staffs.
28. Context Is King: The Rise of Contextual Advertising in 2018
Link (Video): https://www.youtube.com/watch?v=Y9Y4Efyxmk4&t=4s
One of the most significant challenges for both publishers and brands today is capturing the attention of users without disrupting their online experience. While most consumers ignore ads, there’s an effective method that can help catch their attention: contextual advertising. At its core, contextual advertising is an efficient way to deliver advertisements that are directly correlated with the content the consumer is enjoying. Contextual advertising is more personalised than traditional display since the ads are directly related to the content the consumer is engaging with.
29. The Big Read: In the business of Big Data, Singapore has built a cutting edge.
This article talks about how Big Data is bringing in big bucks and benefits for a highly-wired nation like Singapore, showing that Big Data can pave bright prospects for Singapore’s future economy and so, the Singapore Government should embrace Big Data. The big three telcos in Singapore have been providing data analytic services to their clients, and traditionally, its benefits are often seen in the private sector.
30. GDPR? What does it imply for consumers and businesses
Link (Video): https://www.youtube.com/watch?v=1xy_afgALSI
With the GDPR soon to take place on 25 May 2018, this will radically change the consumer data handling of not just companies originating from EU but those operating in them as well. With this change comes, vital consequences for both consumers and businesses that are important for firms to be aware of, to ensure that they stay within the guidelines as well as keep their data safe and relevant for business analytics.
31. Machine learning’s role in Big Data
The Kepler space telescope launched in 2009 to explore possible new planets has since collected over 14 billion data points – too much for humans to analyse. However, machine learning has made the difference where smart algorithms process a large amount of data eliminating false signals and identified two new planets in the process.
32. What Are Instagram Business Tools and Why You Need Them Right Now
Instagram now allows businesses to switch their profile accounts into business accounts. When they switch to this account, they can get insights on who views their profiles, see the demographic, gender and age of their followers and see how many likes or comments are gained in their posts.
33. The Use of Big Data Analytics in Algorithmic Trading
The article talked about how traders make use of big data analytics in the buying and selling of stocks. It also emphasis on the importance and how heavily algorithmic trading are used to predict the future stock trend and in calculating the risks of investment. It also explained why retail traders are at a disadvantaged due to this algorithmic trading technology.
34. Big-data analytics startup create petabyte-scale analytics solution with ultrafast query results
“Big data analytics that used to take days or weeks to process may now require only a few mouse clicks to get query results in seconds”. The new solution provides super-fast analysis results, known as sub-second query latency, on truly massive datasets. It simplifies analytics by providing self-service, seamless interoperability with popular business intelligence tools, with no programming required. Eventually, this short learning curve further boosts productivity and saves on the cost of specialised labour.
35. How Machines Learn
Link (Video): https://www.youtube.com/watch?v=R9OHn5ZF4Uo
The video describes the science behind how programs are ‘learning’ and how they become so incredibly powerful.
36. DATA AT WORK: 3 REAL-WORLD PROBLEMS SOLVED BY DATA SCIENCE
With 90% of the world’s data was only created in the past few years, we can anticipate there to be a lot of growth in the data field. With access to such vast amounts of data and equipped with the ability to interpret and make sense of it, it has helped us to solve three real-world problems- (i) Optimal pricing for businesses, (ii) revolutionising sports analytics, and (iii) increasing the efficiency of non-profit organisations.
37. SQL Text Analysis with Donald Trump’s Tweets
Donald Trump’s usage of Twitter was a big talking point during the 2016 U.S. presidential election. Using SQL (using Periscope Data as a platform) to conduct text analysis, the article shows how Donald Trump’s entire tweet history can be analysed to find out information like keyword frequency, the tone and sentiment of his tweets (whether positive or negative), and the sentiment by the time of day. By relating individual words to sentiment scores, a quick read on the emotional state of Donald Trump can be obtained. It was found that Trump generally trended downwards, exhibiting increasing negativity over the course of his Presidential campaign.
38. When You Fall In Love, This is What Facebook Sees
The article summarises data findings from Facebook’s semantic analysis of relationships, by their team of data scientists. Statistical evidence hints at budding relationships before the relationship starts. 100 days before a relationship starts, there is a slow but steady increase in the number of timeline posts between the couple, of which decreases significantly when things start to become official. The posts also become significantly happier throughout the relationship.
39. Disney Uses Big Data, IoT And Machine Learning To Boost Customer Experience
Link (Video): https://www.youtube.com/watch?v=eYsYITUUa4o
Data is changing the entertainment behemoth of Disney by providing more immersive, more seamless, and more personal experiences for every guest. There are also some intriguing developments with using data that will excite Disney movie fans. The MagicBand developed by Disney World over the years, act as hotel keys, credit cards, tickets, FastPasses and more. With a simple swipe of the band across sensors located throughout the park, the giant system knows where you are, what you’re doing and what you need.
40. This Entrepreneur Uses Cutting Edge Data Analytics To Predict The Future Of Financial Markets
This article talks about how an entrepreneur using the power of data analytics specifically predictive analytics predicts the financial markets. Throughout the article, much was talked about the capacity of a supercomputer to deliver information to our fingertips. This is similar to Big Data 3.0 and Internet of Things where we get data that we do not know we need. And all these information are delivered to us via smart assistance.
41. Moneyball for Movies: Data Science and AI in Hollywood
The Chief Analytics Officer of Legendary Entertainment shares the data, predictive analytics, AI and tools that bring box office success to films like Jurassic World. They applied data analytics to provide inputs like when considering what films to produce, the castings and the release dates. This gives opportunity by adding efficiency to the marketing of the films and allows them to make important marketing decisions that can be costly.
42. Five Ways Data Analytics Will Shape Business, Sports And Politics In 2016
This article talks about how Business, Sports and Politics will differ with the help of data analytics. With the help of analytics, it can help to predict even the winner of the next election, the next game of how businesses even set up. It is an exciting news to hear because with data analysts, not only could it predict just factual data to help businesses grow, some people analyse people. They help to determine their stamina, their running rate and with the data they come up with a plan to help maximise their physical conditions to help them with the game.
43. ClearStory Data Raises Series C Financing to Transform Front-Office Business Analytics Across Global 2000 Companies
ClearStory Data, the company, transforming Enterprise-Scale Business Analytics in the front-office so that companies can leverage all their data assets for material business impact, today announced it has successfully closed a Series C funding round of nearly $15 Million. The round features DAG Ventures, existing investors including Kleiner Perkins, as well as a private investor.
44. Cargill’s iQShrimp helps farmers manage risk, make better decisions
Shrimp farmers can now use predictive and analytics software provided by Cargill to give them real-time visibility into their farm operations. iQShrimp is a first-generation offering driven by iQuatic™, Cargill’s digital platform for aquaculture. The iQShrimp software captures data from shrimp ponds through mobile devices, sensors and automated feeders to record data about shrimp size, water quality, feeding patterns, and health and weather conditions. The system then combines production and environmental information into a “live operations dashboard” to provide insights and recommendations, such as feeding management strategies and optimal harvest dates.
45. Singapore Prison Service spells out ‘prisons without guards’ concept
Prison officers in Singapore will increasingly be less occupied and can perform more ‘higher order job’ with the introduction ‘prisons without guards’ concept. This services will be tapping on data analytics to grasp the rehabilitative needs and progress of inmates, to minimise their risk of re-offending. It is also compatible with mobile devices, allowing officers to access inmates’ data even when they are away from their work terminals. This technology helps SPS to save time, cost and have higher efficiency at work.
46. US scandal shows big data manipulation likely in next Thai election: analysts
Bhume Bhumiratana, a researcher and educator in cyber-security, said that it was highly likely that the next general election in Thailand would see the use of big data analytics in profiling voters. He warned users that their personal information was already being collected by various apps that seek their permission for access to their data. Voters profiling will be very impactful in influencing the election and it is unethical to do so without first seeking permission of the users.
47. The Evolution of Business Intelligence
This article talks about how business intelligence existed since the 1990s but highlights the flaws it had, even though it was useful in its day. Then came the second generation in the 2000s, where the integration of the Web 2.0 allowed more interactive interfaces so that more users could understand the data, as well as choose different parts of the huge amount of data to focus on and analyze. However, most importantly, it talks about how the current technology, which has not made phenomenal advancements in the past years, can adapt its output to produce more useful information for users.
48. How Big Data Is Used To Address Water Crisis In India
This article talks about IBM’s Big Data Solution. Based on their analytics, they can provide smarter water management and better control over the resources for water boards thus controlling the wastage of water. The command centre monitors the water flow in the city and provides a data of the functioning of all the meters. This helps to save water and address the water crisis. Water wastage/leakage is also a very serious problem in India.
49. How Business Analytics is Disrupting the Sports Industry
There are two types of analytics within the sports industry – sports analytics (the science of learning from data to improve sports performance) and business analytics. Even though on-field performance drives revenue, teams are now growing more concerned about how business analytics is disrupting the sports industry.
50. Snapchat rolls out analytics tool to win back the influencers it has lost
Snapchat has always been terrible at analytics, only showing daily Story view counts that content creators had to screenshot just before they disappear every 24 hours to prove their worth to sponsors. But starting today, tens of thousands of creators who make official Stories or have large followings will start seeing a slew of view count and demographic analytics on their Snapchat profile. The new analytics could help creators show their total reach to secure sponsored content and product placement deals, refine their posts to match their audience, and compare their following to that on other platforms
51. Can social media data be used to predict threats or identify fake news?
When acquiring large, high-quality data, it is costly and time-consuming. If social media data can be used instead, it can help to overcome the obstacle. Nevertheless, research is still being carried out to find out whether crowd-sourced data from social media can be used to not only detect threats but also to prevent catastrophic events from happening in the future. The challenge with using data that comes from a group is that people or algorithms can be unreliable.
52. Cambridge Analytica
The heart of Cambridge Analytica’s power is an enormous information warehouse — as many as 5,000 data points on each of more than 230 million Americans, according to recent reporting, a fact the company proudly confirms on its website. Its promise of elections driven by data ultimately implies a vision of government steered not by people but by algorithms, and by an expanding data-mining culture operating without restrictions.
53. Google develops data analytics service in Brazil
This article talks about Google expanding its data analytics service in Brazil, adding at least five business areas to existing platforms, such as sales and agency. This will contribute to generating useful insights about users, using customisation and analysis of data results.
54. Big Data Predicts Terrorist Attacks with More Than 90% Accuracy
This article talks about how Big Data can be used to build profiles of potential terrorists and locate areas with large numbers of people who match these. One of the main tools being used against ISIS is social media analytics. ISIS is renowned for their social media prowess, with the use of Twitter and Facebook central components of a recruitment drive that has seen them become the most popular organisation for fighters coming from foreign countries.
55. 5 Ways Walmart Uses Big Data to Help Customers
Walmart is an American-based hypermarket which markets itself in the industry as the supermarket with the lowest prices, as they aim to help people save money, yet still being able to purchase their necessities. In fact, Walmart has been actively making use of data analysis to create a business system which consistently offers the lowest prices to their customers. Also, big data helps to personalise the shopping experience for customers at Walmart as it can help identify preferences of the users and create a smoother shopping experience for them.
56. Artificial intelligence reads privacy policies, so you don’t have to
57. Hospitals and Analytical Tools
Analytic tools are being employed to identify the risk tolerance baseline to adopt appropriate and optimal risk management in hospitals and health systems. Big data is used to provide public health systems and public hospitals especially with a strategic view of risk tolerance. By using this technology-enabled approach to do so, customisation is allowed to adjust for the different area of concerns that are critical for a certain health/medical organisation such as different financial metrics to determine the success or failure of finances in the hospital.