The Business Value of Big Data
•Download as PPTX, PDF•
2 likes•1,290 views
The potential of big data is well known, but many businesses are still quite some distance from harnessing it. In this session, we will look at some approaches to deriving business value from big data, with a number of case studies.
Recommended
More Related Content
What's hot
What's hot (20)
Similar to The Business Value of Big Data
Similar to The Business Value of Big Data (20)
More from Clark Boyd
More from Clark Boyd (20)
Recently uploaded
Recently uploaded (20)
The Business Value of Big Data
- 1. THE BUSINESS VALUE OF BIG DATA Clark Boyd, 27TH February 2018 @ClarkBoyd linkedin.com/in/clark-boyd-digital/
- 2. • The Explosion of Data • How Can Big Data Create Business Value? • Big Data Processes • Big Data Technology • Big Data Challenges • The Right People • Summary Agenda
- 3. Big Data Throughout History
- 8. How Can Big Data Create Business Value?
- 9. Point Defiance Zoo: Big Data for Predictive Analytics
- 12. 5 types of business case
- 13. The Components of a Big Data Strategy Data People Technology Process
- 14. T-Mobile: Big data for churn prevention Churn rate decreased by 50% within just one quarter
- 16. Data Value Templates Insight Targeting Personalization Context
- 19. Kaggle.com: Open data sources
- 21. Visualization & Analytics Computation Storage Warehousing Categories of big data technology
- 22. Examples of big data technology
- 23. IBM: Big Data for Staff Retention
- 30. The Data ‘Center of Excellence’
- 31. Some Questions to Consider • Who is responsible for data quality within my organization? • Where is our data stored? Does it have an expiry date? • How will we measure the impact of big data? • Where does our data come from? Do we have the right types of consent? • Are there any open source data repositories we could use for new analysis?
- 32. Summary: Big Data for Business • Businesses are starting to scratch the surface of big data’s potential – 67% of companies used big data technologies in 2017. • A significant shift is the move from retrospective analytics to predictive and prescriptive analytics. • There are more open source data repositories than many realize. Kaggle is a great place to start. • Big data can level the playing field. Businesses of all sizes, from a small zoo to a multinational bank, can start driving better business outcomes through data today.
Editor's Notes
- Sparta declared war on Athens. Some Athenians were trapped and need to climb over a wall created by Spartan-led forces Most of it was covered, but one section was not and the bricks could be seen.
- Lots of oil, but the combustion engine has not yet been invented We are now fully into the age of ‘big data’. In 2017, this is how much data was created by people every single minute. 4 million searches on Google, 46,000 Uber trips, 3 million GBs of internet data in the US alone. There are people, intentions, relationships behind every single one of these data points. The challenge is trying to make sense of it all, turning it into something genuinely insightful or useful. The most successful attempts have been driven by machine learning.
- Lots of oil, but the combustion engine has not yet been invented We are now fully into the age of ‘big data’. In 2017, this is how much data was created by people every single minute. 4 million searches on Google, 46,000 Uber trips, 3 million GBs of internet data in the US alone. There are people, intentions, relationships behind every single one of these data points. The challenge is trying to make sense of it all, turning it into something genuinely insightful or useful. The most successful attempts have been driven by machine learning.
- According to market research company, Dresner Advisory Services, 53 per cent of companies used Big Data Analytics in 2017, with telecoms and financial services industries leading the way at 87 per cent and 76 per cent, respectively.
- According to Deutsche Bank research, the use of big data has improved the performance of businesses by an average of 26%
- A survey by SAP in late 2016 found that over 70% of small business leaders felt that they were still only in the “early stages” of deriving insights from their data. One zoo in Tacoma, Washington bucked that trend by partnering with the National Weather Service to identify the factors that caused attendance figures to rise and fall so unpredictably. This created issues for management, who would always staff the park to cater for a large audience, but often ended up overspending on salaries due to underwhelming attendance. Intuitively, we could assume that attendance is higher on warm, dry days, but lower when it is cold or wet. However, by incorporating the National Weather Service’s data into IBM’s AI-driven Watson platform, the zoo was able to pinpoint exactly which conditions caused more people to make a visit. This knowledge was then used to model future visitor patterns, using historical attendance figures and projected weather statistics. The project was a huge success and is now a central part of the zoo’s business planning. Point Defiance can predict attendance figures with greater than 95% accuracy, allowing managers to staff the park appropriately. This has no negative impact on how visitors experience the park (perhaps even the opposite), and creates some vital business efficiencies. The applications of this methodology reach far wider than just attendance figures, of course. Port Defiance can monitor how visitors interact with the zoo, helping to provide a better customer experience. Plans are also in place to use AI-driven predictive analytics to monitor health data and diagnose issues with the park’s animals to provide pre-emptive treatment.
- ML is everywhere and we see a lot of the same companies here that we saw at the beginning. The ones with most data to process are the ones most likely to use ML to harness its potential.
- Customer data of T-Mobile USA includes the time and lengths of call, internet usage or peak times for direct messaging. T-Mobile USA takes advantage of this data to prevent customer churn. An example of this is billing analysis, where the product usage is calculated. If the frequency of calls to contacts who are using a new providers are increasing this could imply that friends or family are switching providers, and the customer might possibly do so as well. By identifying these customers T- Mobile USA achieved to decrease their churn rate by 50% in just one quarter
- We could spend all day on ML, but let’s take a quick look at remarketing before getting on the examples. Most of you will be familiar with RM, so I won’t labor the point. For those that aren’t familiar, the concept is really quite simple. Remarketing, also known as retargeting, can dramatically increase your conversion rates and ROI. This is because past site visitors who are already familiar with your brand are much more likely to become customers or complete other valuable actions on your site.
- The travel industry is notoriously competitive, with volatile peaks and troughs in demand and many low-margin routes. This can leave travelers in the dark, unsure of the best time to book. Sometimes it’s better to book ahead, at other times it’s better to wait until closer to the date of departure. This makes it a field ripe for the power of AI-driven predictive analytics, a fact that has seen the travel app Hopper grow dramatically in popularity since 2015. Hopper stays one step ahead by predicting future pricing patterns and alerting travelers of the cheapest times to buy flights to their preferred destinations. It does this by watching billions of prices every day and, based on historical data for each route, anticipating how the trend will develop. Users can then set up notifications to remind them to book when these price drops come to pass. Although not the only such company to provide this service, Hopper reports a 95% accuracy rate with its predictions and claims to save customers an average of over $50 per flight. The screenshot below shows how this process functions. Accompanied by a cuddly, bespectacled bunny, I select the New York to Honolulu flight route for that richly-deserved vacation.
- We could spend all day on ML, but let’s take a quick look at remarketing before getting on the examples. Most of you will be familiar with RM, so I won’t labor the point. For those that aren’t familiar, the concept is really quite simple. Remarketing, also known as retargeting, can dramatically increase your conversion rates and ROI. This is because past site visitors who are already familiar with your brand are much more likely to become customers or complete other valuable actions on your site.
- There are over 15,000 marketing technologies now, compared with 150 10 years ago
- The fundamental attraction of predictive analytics is the potential to deliver better outcomes against organizational goals. These are often overtly profit-based, but predictive analytics can also help identify staff retention issues and suggest solutions. By uploading a structured data file, Watson can spot the common contributing factors in staff attrition. This then feeds into the generation of a ‘quality score’ for each employee, based on their projected likelihood of leaving the company soon. Where this really comes into its own is in its ability to respond to natural language requests from users. In a similar fashion to Google’s new Analytics feature, which will fetch data in response to user questions, Watson can respond to specific queries and build data visualizations based on the user’s preferences. This is a great example of a platform that moves quickly from exploratory and diagnostic analysis, into the realm of predictive analytics. Any business owner or manager can make use of these tools to identify with precision what exactly causes staff to leave, but they can also see what lies behind those factors and put in place preventative measures to appease any potential departures. Given the cost of recruiting new staff versus retaining current high-performers, this leads directly to decreased operational costs.