OnPoint For Individuals

24/7 Data - What Is the Deal?

Posted by Marie L. Clark on Feb 1, 2018 6:11:00 AM

data 24-7 eye.jpeg

Data is everywhere. Data is powerful. Data is power.

Wired states that: “Data has influenced a wide range of industries… quantitative analysts on Wall Street, polling in politics, market research…” And that is just the tip of the iceberg. Seriously, data is ever-present everywhere, and it isn’t going anywhere anytime soon.

Data has raw value as it flows into, through and from Google, Facebook, Amazon, utilities companies, healthcare organizations, credit bureaus and pretty much anyplace with a server and some information about some facet of your life. You’re basically “on the record” 24/7 in our intricately connected modern world. Yeah, let that sink in. Add to that…
“Everything we do in the digital realm – from surfing the Web to sending an e-mail to conducting a credit card transaction to, yes, making a phone call – creates a data trail. And if that trail exists, chances are someone is using it – or will be soon enough.”- Douglas Rushkoff, Author of Throwing Rocks at the Google Bus.

We humans love data. Even more than the data itself, we love algorithms. Yes, we really do love algorithms, whether or not we comprehend all that they do to bring us information, images and interactions that we crave…just think about your Facebook page or Twitter feed—that take that our collective data and give us “practical applications.” We’ll swing back around to those practical applications a little later.

2001 monolith.jpegThink about the opening scene in 2001: A Space Odyssey, known as “The Dawn of Man.” In this scene, the Paleolithic Era people one day discover a tall, black, rectangular object—what we know as “The Monolith”—that radiates knowledge to empower our ancestors to pick up tools to forge their own fate in the world.

And here we stand, facing AI and an increasing variety of robots. Our tools are different now, falling under the heading of data literacy, which will allow us to understand the massive tons of data all around us. Data literacy also gives us the language to communicate about—and to some degree with—data, algorithms, AI and robots.

And with algorithms, data science, data analytics and data literacy, we can synthesize that information into meaningful morsels for the benefit of ourselves and society. From simple processes and machines to mass automation and huge strides in robotics, algorithms give meaning to data .

But is that meaning inherently good for society? Is it good for you?

The Unintended Consequences of Data: Leaving Ourselves Vulnerable to the Whims of Algorithms and Robots

"The system is perfect until it comes after you."  -Minority Report


Are we overlooking existing and potential dangerous ramifications by blindly relying on data-based results from devices like our smartphones, tablets, fitness trackers, smartmeters? If we are leaving our fate in the hands of algorithms, is that really wise?

So, as we share our endless streams of data, flowing from and to every conceivable—and inconceivable—direction, we may wonder, “What could possibly go wrong?” Isn’t that one of those “famous last words” type of phrases?

Even though we don’t necessarily hear people uttering those precise words, it seems like we may be forfeiting our rights for the sake of convenience, pleasure and perhaps a certain degree of ignorance. It is still such a new age—this “data age” we find ourselves in—that a certain degree of ignorance makes sense. The world has monumentally shifted while we were checking our email, so we all need a minute to catch up. But catch up, we must because technology does not have ethics, and we need to ensure its accountability.

The Many Misadventures of Algorithms

It is important that we understand some of the ways that algorithms sometimes “get it wrong.” There are many accounts of algorithms that have gone afield—or have the all too real potential to veer off course—of their desired goals, and following are just a very few:

Criminal “Risk Scores.” The criminal justice system is increasingly using algorithms as a means of predicting the likelihood of a defendant’s future criminality, according to The New York Times. A recent Wisconsin Supreme Court decision took into account the limitations, or at least the possible limitations, for accuracy in sentencing. When it comes to the future fate of a citizen, it is important to acknowledge the limits of an algorithm’s accuracy.

AI That Detects Signs of "Sexual Preference." The Economist recently published an article about how machines that read faces are on their way, and they may have the power to pick up patterns regarding someone’s sexual preference. What could possibly go wrong?

We Can Avoid Surrendering Our Rights to Robots by Learning and Using Their Rules Through Data Literacy

Data literacy can help us navigate, and even harness, our exhilarating new world of data, algorithms and everything that accompanies them. Data literacy gives us the tools we need to stay a step or two ahead of the robots by allowing us a chance to see the invisible-but-omnipresent algorithms churning all around us.

But What Is Data Literacy?

“Data literacy is the ability to derive meaningful information from data, just as literacy in general is the ability to derive information from the written word,” sums up TechTarget.

Data literacy not only gives us a basic understanding of the data that we encounter, but it also helps with data governance to manage issues that arrive from algorithms that have gone off course.

What Are a Few Data Literacy Skills?

Data literacy is made up of a set of skills that include the following:

• Understanding what data, or type of data, is appropriate to use for a specific purpose.
• The ability to interpret data visualizations like charts and graphs.
• Can think critically about the information extracted from the data analysis.
• Recognize a problem with the data, such as when it is being misrepresented or used misleadingly.

Is Your Data Literacy Up to Snuff?

Do you feel like you can select a set of data to analyze to derive certain results? Could you catch a disturbing trend in algorithms that might suggest a certain bias?

If you are worried that you may be left behind in the wake of a sea of data, it is good time to start exploring ways to become data literate. 

Topics: Data Science, Data, Data Scientist, Data Analyst, algorithm, AI, big data, dataliteracy