Effective Big Data Software Will Win the IoT Age
From choosing the right language (Julia, R, Python, etc.) to expanding your imagination on the use cases, dealing with IoT's data is the key to its success.
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If you effectively sift Big Data generated by the Internet of Things, then precision marketing, powerful CRM, productive customer service, and, ultimately, more profitable sales will follow.
The “if” part of that statement is implied, but it's the tough part of the equation. Let’s expound.
Big Data Analysis Software: The Vital Link
If you develop software that mines Big Data for actionable information pertinent to your marketing goals, then you will be able to customize outreach to consumers, which leads to a healthier bottom line.
We’re going to expand far beyond the marketing world in a minute, but it serves as a useful example of the premise of this post. The IoT sends an enormous amount of data (Big Data) to the cloud, where it can be accessed by marketers. That’s step one, but a step that by itself won’t yield a single marketing benefit. The essential second step is analyzing the data properly, so it can be employed in effective marketing, sales, and CRM (more about IoT here). And marketers must have software tools for that purpose because the sheer volume of data is too immense to sort manually — as if it were information garnered from pre-digital age customer surveys.
If you have software development skills, then you possess powerful tools in the age of the IoT. First, think about how many “things” are in the IoT ecosystem:
Personal computers and laptops
Work computers and laptops
School computers and laptops
Smartphones and their apps
Personal, public and work vehicles of all types
Home appliances such as refrigerators and clothes washers
Medical equipment including monitors, devices and implants
Security and door access control systems
Fobs for a wide range of purposes
Fitness and training equipment
The list could continue indefinitely. It's already long, and it's being added to daily. And we mean that literally.
Now, secondly, consider the enormous amounts of data being generated by those smart, connected things every time one of them is used.
Finally, the data must be collected, stored, sorted, and turned into actionable steps that connect with the target audience. This is where software developers are the essential link — digital translators of data into action steps that will change the world.
The potential is enormous for marketing and sales. We know that. But the opportunities are just as real for the fields of health and medicine, education, workplace safety, personal and public security, transportation safety, environmental conservation, manufacturing, energy consumption, sport, politics, and every other field in which data can be collected and analyzed for trends, connections, preferences, and probabilities that can be acted upon to achieve the desired outcome.
If the potential sounds immense to you, you’re getting the picture!
The IoT and Big Data
Here is a brief overview of how we got to this point.
There was a time not so long ago when accessing the Internet meant making a conscious
decision and taking a definitive action, such as logging in. That method is fading, quickly being overtaken by an ecosystem in which smartphones and their apps, computers, automobiles, refrigerators, smart home hubs, thermostats, medical implants, security systems, surveillance cameras, fobs, wearables, clothes washers, watches, and a growing number of other objects are always connected.
The IoT was envisioned by innovators like Andy Hobsbawm, the CMO of smart device IoT platform Evrything. He wondered, “Why couldn’t the physical world be online and referenceable, searchable, mashable just like other forms of digital information? We [he and Evrything co-founder Niall Murphy] both felt strongly that the web will inevitably include billions of objects sharing dynamic information about themselves in real-time.”
The IoT raced past the billion point and is expected to reach 50 billion “things” by 2020, a figure predicted by Ericsson in 2010 and echoed repeatedly and even
increased by some.
Making Sense of Big Data: The Life-changing Power of Analytics
The number of ways data can be used is mind-boggling. Consider several examples, and you’ll be able to configure as many as you like:
Your car’s sensor indicates that your engine oil is dirty, and you get a text with the notification and a coupon code for a nearby oil-change center.
You enter a QR code into a restaurant’s scanner by passing your phone in front of it, and a custom menu is presented to you that reflects your preferences and requirements (vegetarian entrees, Italian cuisine, French wines, free-trade coffees or gluten-free desserts, for example).
Student performance data is analyzed and adjustments in education made based on how well students learn at what times of the day and how eating, rest, exercise and instruction techniques or media impact performance
A holistic analysis of medical data from thousands of patients is analyzed to determine the most effective combinations of treatments, drugs, therapies, and diet for each medical condition
Workplace safety data is analyzed to determine how work techniques, equipment, length of work shift, training, lighting, and other factors can be adjusted to create a work environment in which accidents rates fall and productivity and employee satisfaction rises
The mass of data is simply too big to mine without putting analytics software to the task. The software tracks, accumulates, categorizes, applies algorithms, employs data science, and performs a multitude of additional tasks to help the marketer decide that:
Customer A fits the demographic of customers that respond when Goods B and Services C are offered to her on Devices D and E
Customer X fits the psychographic profile of a person likely to purchase product Y for herself and product Z as a gift
Turning Data Into Good Decisions
The first step for developers is to become proficient in data programming languages with, “feature sets [that] make them well suited to handling large and complicated data sets.” This is according to Bernard Marr, a Big Data consultant to industry and governments. In a recent piece for Data Informed, Marr discussed three popular Big Data programming languages — Python, R, and Julia — along with their strengths and weaknesses.
Secondly, the language chosen must be used to create software tailored to its industry and purpose. For example, the Data Sciences department of the US National Institutes of Health has created the Targeted Software Development awards to fund software tools and methods development to tackle data management, transformation, and analysis challenges in areas of high need to the biomedical research community.” In 2015, awards were made in the areas of data compression, data provenance, data visualization, and data wrangling. In 2016, awards were made in the areas of data privacy, data repurposing, and applying metadata.
The best route to creating effective software might be to work backwards from the end in mind. Here’s one example to demonstrate what this might look like in just one field.
Let’s say that the goal is to structure a school day that optimizes students’ learning potential. First, determine the factors that might be relevant. Some, like the first in the list below, are obvious; the other information is available by an analysis of the data, if it is being properly collected. Here are a few of the questions a developer might want to know in to determine the relevant data:
Time the school day starts and ends (known, obviously)
What subjects do students perform best in at what times of the day?
How does performance change with factors such as class size, class period length, the longer students go without eating or what they eat?
How do factors such as exercise, playing classical music or offering healthy snacks at intervals affect learning student attitude and outcomes?
What teaching styles yield the best results?
Next, develop software and install it on all devices used by students for testing and other classwork. There, it would glean information to create and organize data sets and analyze the data to show the relevance of each factor on outcomes.
From there, it would be easy for educators to make changes in the school schedule, format, and teaching methods for better results.
Becoming a World Changer in the IoT Age
If you’re new to the idea of developing Big Data software, one way to get very familiar with it is to be trained and certified in one of the leading software products currently in use. By using the software, you’ll come to understand exactly how it does what it does. From there, you’ll add what you’ve learned to your existing software development skills to create targeted Big Data analysis software for the industry you’re passionate about. The process isn’t easy, or everyone would be doing it, right? However, it is possible, and the potential for success that will reward you personally and professionally is absolutely off the charts.
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