In 2015 2.5 Quintillion bytes of data were created every day and 90% of all the data in the world has been created in the last two years alone. Nowadays, this data is not just business data only but also a collection of Netflix views, Amazon purchases, app activity and many more. However, that is still a lot of data and is a telling sign that organizations should use their data to make better decisions.
The power of data is that it can shed light on important business issues such as amount of active users and customer satisfaction (and how to improve it). Data analytics is the process of examining large and varied data sets to uncover hidden patterns, unknown correlations, market trends, customer preferences and other useful information that can help organizations make more-informed decisions.
From data to information
Data are raw, unorganized facts that need to be processed. Data can be something simple and seemingly random and useless until it is organized. When data is processed, organized, structured or presented in a given context so as to make it useful, it is called information.
Data analytics has become more important than ever. Simply put, we as a society are being asked to share data, and a tremendous amount of data is being gathered. The key here is that a huge amount of data is being collected in various domains as business interactions move to technology platforms: computers, mobile devices and the internet of things. We should consolidate the information, provide just-in-time information, analyze the information, provide visibility and even apply established knowledge-based rules to drive decisions. Hence, rather than make decisions on a limited amount of information or visibility, we provide enhanced analytical capability, the ability to drill down or roll up information, and apply expert-level rules to drive the right decisions.
But here’s the challenge: It’s an absolute myth that you can send an algorithm over raw data and have insights pop up. 80% of a typical data science project is sourcing cleaning and preparing the data, while the remaining 20% is actual data analysis in order to get the information we want.
Let’s take following simple example: A smart hospital bed is equipped with various sensors and interfaces: pressure sensors, accelerometers, moisture sensors, RFID-reader,… If a patient falls out of the bed we want to warn the nurses in order to deliver help to the patient. In order to get the information we’ll first have to prepare and clean the data: What sensors are relevant to the event? How do we have to interpret the sensor data? How do we filter out noise? We can then proceed to the analysis: How do we distinguish a patient falling of the bed versus voluntary getting out of the bed? How reliable is the system?
Even for this small example the task isn’t that straight forward.
Types of data analytics applications
At a high level, data analytics methodologies include exploratory data analysis (EDA), which aims to find patterns and relationships in data, and confirmatory data analysis (CDA), which applies statistical techniques to determine whether hypotheses about a data set are true or false.
Data analytics can also be separated into quantitative data analysis and qualitative data analysis. The former involves analysis of numerical data with quantifiable variables that can be compared or measured statistically. The qualitative approach is more interpretive — it focuses on understanding the content of non-numerical data like text, images, audio and video, including common phrases, themes and points of view.
More advanced types of data analytics include data mining, which involves sorting through large data sets to identify trends, patterns and relationships; predictive analytics, which seeks to predict customer behavior, equipment failures and other future events; and machine learning, an artificial intelligence technique that uses automated algorithms to churn through data sets more quickly than data scientists can do via conventional analytical modeling. Big data analytics applies data mining, predictive analytics and machine learning tools to sets of big data that often contain unstructured and semi-structured data. Text mining provides a means of analyzing documents, emails and other text-based content.
Data analytics initiatives support a wide variety of business uses. For example, banks and credit card companies analyze withdrawal and spending patterns to prevent fraud and identity theft. E-commerce companies and marketing services providers do clickstream analysis to identify website visitors who are more likely to buy a particular product or service based on navigation and page-viewing patterns. Mobile network operators examine customer data to forecast churn so they can take steps to prevent defections to business rivals; to boost customer relationship management efforts, they and other companies also engage in CRM analytics to segment customers for marketing campaigns and equip call center workers with up-to-date information about callers. Healthcare organizations mine patient data to evaluate the effectiveness of treatments for cancer and other diseases.
Smart Crowd Control
Famous for the $1 billion investment it took to get off the ground, Disney World’s proprietary MagicBand is a great example of IoT and big data working together. The MagicBand is a wearable, sensor-laden, wristband that vacationers use to do everything from check into their hotel room, buy their lunch, go through the turnstiles at the amusement parks, and reserve a spot for specific attractions. Wearers use the band to ‘check in’ at certain posts by tapping it against a receiver, and it tracks their movement via RFID, so Disney collects data on visitor movement throughout the park. Leveraging this data, Disney can accommodate more guests, properly staff rides and attractions, and better regulate inventory at highly-trafficked shops and restaurants.
Smart Convenience store
Jewelry store chain Alex and Ani have rolled out bluetooth sensors to their stores that can track traffic numbers in their stores and push specialized, or more customized, offers to users’ phones as they enter the store. The company has also partnered with beacon technology company Swirl to complete the deployment. The technology tracks customers movements within the store, similar to a heat map, so the company will be able to better organize and display its products to drive sales.
Headquartered in the Canadian province of British Columbia, BC Hydro is an electric utility providing power to nearly 2 million Canadian residents. In 2011, the company began upgrading its electricity meters to smart meters. Users can now track their energy use by the hour and see trends in their own usage data. Electricity theft has been greatly reduced and automatic outage detection alerts the company when the power is out in a certain area. Leveraging the data collected, BC Hydro can provide improved service at a lower cost.
Barcelona is no stranger to technological innovation. The city hosts the Mobile World Congress technology show every year, but is also becoming a center for innovation itself. The city offers smart parking meters that operate on city-wide wi-fi, giving residents real-time updates on where to park and allowing them to pay with their phone. Smart bus-stops provide passengers with real-time updates via touch-screen panels, and a city-wide sensor network informs workers and residents about temperature, air quality, noise level, and pedestrian traffic.To manage all of this, Barcelona built a big-data system on Microsoft Azure to process and analyze the myriad data points it was receiving. With the insights generated by the system, the city can offer better services such as public transportation, plan for events like the La Mercé Festival more efficiently, and better understand the impact of tourism.
Virgin Atlantic has embraced IoT with a slew of connected Boeing 787 aircraft and connected cargo devices. Each plane has multiple internet-connected parts generating a large volume of data.According to Virgin Atlantic IT director David Bulman, speaking to Computerworld UK, each connected flight can produce more than a half a terabyte of data. While the exact big data program isn’t fully realized, the data could be used to predict maintenance requirements or to improve flight and fuel efficiency.
Big data and the IoT are making waves in big agriculture, and one of the leaders in this area is equipment manufacturer John Deere. The John Deere Field Connect system monitors moisture levels and sends the data over a wireless connection for farmers to see. According to the website, the environmental sensors also measure “air and soil temperature, wind speed, humidity, solar radiation, rainfall and leaf wetness.” The data will help farmers discover when crops are reaching optimum moisture levels. Armed with this information, farmers can make timely irrigation decisions. Trend data can also show how much the change in seasons affects moisture retention.