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How Brands are Using Your Data

Big Data

Do you know what the hottest commodity on the market is these days? I’ll give you a few guesses. Did you say a trending stock or cryptocurrency? Then, you would be wrong.

It’s information.

We hear so much on the news about data leaks and identity theft that targets businesses and individuals that it makes us wary of giving anyone our personal information online or in the real world. Unfortunately, there’s rarely a choice any more. It’s how commerce is done these days. Conducting business online is convenient, yes, but it has inspired a cottage industry in the buying, selling, and leverage of data.

Your information, even the most basic bits, is big business. This is such an important consideration, the EU has established in 2018 a strict set of rules regarding how it’s collected and used, though website owners around the world are still trying to figure out the fine points and how the potentially severe consequences for abuse or non-compliance might be applied.

From government tracking to other forms of privacy invasion, everyone seems to want to know what we’re doing online, where, and with whom.

It’s called data mining and analytics, and it’s sweeping the world.

What is Data Analytics?

Data analytics is a component of artificial intelligence that’s used by corporations, government agencies, and other organizations to cultivate, evaluate, and segment data in order to gain insight into its meaning. The processes are automated in order to take in raw data and quickly make determinations about its relevance for human consumption and dissemination.

There are four types of data analytics, which can be used separately for a specific purpose or in some combination to gain deeper understanding and make decisions.

  1. Descriptive analytics: Provides a picture of activity over a certain time period, such as sales figures or website traffic.
  1. Prescriptive analytics: Recommends an action based on certain criteria or a set of factors, such as how to respond if sales numbers drop to a predetermined percentage.
  1. Diagnostic analytics: Makes a hypothesis based on several data sets, such as how a change in formatting might have affected readership or bounce rates
  1. Predictive analytics: What might happen in the future based upon the outcome during a similar time period or set of circumstances in the past, such as upcoming summer sales based on the previous year’s numbers.

Data is mined for our benefit every day, but you usually only hear about it when something negative happens.

What’s Being Done With All That Data?

Data can be used to drive readership to news media, influence online recommendations for a more personal shopping experience, or provide you with entertainment choices on viewing media based on your past habits.

These are generally seen as positive elements of consumerism that are intended to make you happy. We usually appreciate the personalization, even if we occasionally get a little creeped out when we like something on one platform and find related links to it on another seconds later.

What we don’t like is when our data is taken without our consent and used for profit. Think about something so simple it’s almost invisible, but you use it everyday to access the internet – your browser. A partial list of things it can record and remember is your location, type of connection you’re using, hardware, social media activity, web sites visited. In the hands of the right person it can connect all the dots to reveal a fairly complete picture of your online persona that will never, EVER go away and is available to anyone with the money to pay for it.

Different types of privacy software have gained popularity in response to these kind of collection methods in recent years, ranging from virtual private networks to secure browsers to password managers with end-to-end encryption – all with the goal of trying to slow the flow of our data outward to those bent on collecting it.

The extent to which these measures works is hard to assess. Much depends on how diligently you use them. For others, these tools seem like a quaint attempt to stop the blood flow after an arm has been hacked off. In other words, privacy no longer exists. It’s been killed dead by unscrupulous app developers, Big Tech, governments, and the maliciousness or simple mindlessness of hackers.

But, not all data cultivation is bad. Much of it is beneficial and necessary, and it’s performed behind the scenes. How are brands using our data? Here are a few ways that you might appreciate.

Data Analytics for Customer Retention

Startups may worry about getting new customers, but most owners know that keeping your customer base satisfied is a primary goal of business. In fact, it costs more to attract new customers than to keep the ones you’ve got happy. When a huge part of managing a company is cost controls, using data analytics for customer retention is a smart investment.

Analytics allows companies to gain insight into consumer behavior so they can tailor marketing and product development accordingly. It enables companies to identify trends and patterns of behavior and align service with customer preferences and habits.

Data Analytics in Innovation and Product Development

Collecting data before developing new products helps ensure a more efficient design process and avoid unforeseen issues. For example, a company may have an idea for an improvement for an existing product, but not foresee a potential hazard.

Data analytics technology is capable of compiling and analyzing millions of bits of information quickly and examine possibilities from a number of angles that human analysts may not consider. Information can be input, previous designs analyzed, and new possibilities recommended with a fairly accurate determination of possible outcomes.

Data Analytics in Supply Chain Management

With even smaller businesses going global, supply chain management would be a nightmare without technology. It protects products, customers, and vendors at all points along the supply chain, and ensures greater accuracy during logistics planning and implementation. The more complex the supplier network, the more essential it is to find cost effective, data-driven solutions.

Data Analytics in Risk Management

Companies need to be able to identify risks, not only to consumers regarding possible liabilities, but also financial risk. Data analysis provides businesses with information and the tools necessary to determine and quantify risk based modeling and predictive assessments. With the right information, big data can foresee possible dangers ahead and make recommendations, identify flaws and vulnerabilities in computer networks, and suggest improvements. Going with your gut is still okay, as long as you have the data to back it up.

Final Thoughts

In the information age, data is currency. Businesses and governments need it to make important decisions, marketers want it so their customers can experience better service, and criminals want it for profit. In fact, data-drive may be one of the biggest industry buzzwords of the year.

Regardless of the reason for anyone to want your data, the ultimate decision as to who owns and controls it, how much of it is shared, and for what reason is when things get interesting.

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