Whether you’re a digital native or whether you’ve grown accustomed to life with the internet over time, imagining a world without a massive virtual landscape can feel downright unfathomable. But how often do we really consider the price we pay for these online conveniences? How often do we consider how we’re protecting our online privacy?
The truth is, people are indeed worried about their data being misused online. In fact, 80% of online users in the U.S. are concerned their data is vulnerable to hackers. So online users must be taking steps to protect their sensitive data and personal information — taking steps to protect their online privacy.
5 Places We Compromise Our Online Privacy in 2021
It turns out; this isn’t always the case. For perspective, surveys show people also consider their browser history as costly as a Big Mac meal. That’s about $6. This disconnect in our concerns about online privacy and our actual attempts to protect our online privacy is known as the privacy paradox. And it’s something all online users should be aware of in 2021 — and aware that they need to break their privacy-compromising habits online.
To help put online users on the right path, we’re shedding light on the locations we most commonly put our online privacy at risk.
Why Should We Care About Online Privacy?
Cybercriminals and marketers alike care about your online privacy — briding it, that is — which is why you should care about your online privacy, too.
Sure, targeted marketing efforts might not bother you. Maybe you even like to see that new face wash you’ve been looking for appearing as an ad on your social media feeds. But your data can be used for more than just these advertisements.
After data brokers have a hold on your data, there’s no telling where it may be. This leaves you vulnerable to cyber attacks like identity theft and even extortion. Recovering from these attacks is not a pretty process. It can take years to get your life back in order. That’s why it’s important to stay informed about how we’re putting our personal data on the line when we go online.
The longer we remain complacent about protecting our online privacy, the greater the incentive is for cybercriminals to strike, including in the form of data breaches; by defending yourself and equipping others to do the same, you can be a part of the efforts to de-incentivize these criminals and keep the internet safe.
5 Places We Sacrifice Our Online Privacy
Many of our regular online behaviors make us vulnerable to cyber threats, and we don’t even know it. Yet, opinions as to who should be responsible for protecting user data vary. Should governments, companies, individuals themselves be in charge of protecting our online privacy? At the end of the day, your personal information and data are just that — yours. So it’s up to you to go the extra mile to ensure it stays safe.
Luckily, it’s easier than one might think to protect your online privacy. Just consider some of these popular platforms and sites that pose risks to your online privacy. And also, consider how simple it is to break your privacy-compromising habits risks so that you can navigate the web with peace of mind.
1. Food Delivery Platforms
Food delivery isn’t a new concept. But modern apps and services have revolutionized the food delivery industry by making the process faster and easier than ever. The pandemic further popularized these options, as people everywhere began avoiding restaurants in favor of ordering in.
However, we give a lot of information to these apps, from our names to our addresses to our credit card information. Well-orchestrated phishing scams can trick people into sending their data over to malicious hackers, compromising user safety and online privacy.
The best way to prevent your data from being hacked is to input your information manually for each transaction. Don’t save your data for faster checkout next time. While it may feel like an added hassle, taking a few extra seconds to do this each time will put you at significantly lesser risk in the event of a data breach.
If you do make an account and save your information, use strong and unique passwords. And opt into 2-factor authentication, if available. This is the next best way to protect your data from cyber-attacks.
2. Social Media
People spent a lot of time on social media before the pandemic. Upon its onset, screen time has increased, with surveys showing half of the population spends at least 30 minutes per day on social media. Many interactions can happen in those 30 minutes, from saving posts to liking content to clicking links to online retailers.
We also provide a lot of personal information to our social media apps, from our date of birth to our employment history to our deepest subconscious interests. It’s often cited that social media sites know more about us than our closest friends do.
The best way to protect your online privacy on social media is to keep your account private. Also, limit what you share in your “about” section. And be aware of how your privacy settings are configured.
3. Video Games
Just as the pandemic impacted most other areas of life, our time spent enjoying indoor leisure activities skyrocketed. Online gaming alone increased by 39% during shelter-in-place months. Unfortunately, even in play, cybercriminals are looking for opportunities to infiltrate data and intercept messages.
One less obvious risk involves how hardware used in video games connects you to others, from cameras to microphones to screen-sharing tools. When we create accounts to play online games, we provide account login information, personal information, and sometimes payment methods. Hackers are experts at using even inactive devices to retrieve information.
Prevent your account information and other data from being stolen and sold by using a VPN to encrypt your activity when playing video games. Of course, it’s also a good idea to use the strong and unique login information that would be challenging for even experienced hackers to obtain.
4. Video Conferencing
If the pandemic has taught students, educators, and the workforce anything, it’s how to navigate remote communication tools like Zoom and Google Hangouts. The student’s desk and the worker’s office were replaced with a hybrid home-bedroom-office space.
When stay-at-home orders were at their peak, even major CEOs and politicians used virtual platforms to connect and discuss pressing matters. Many still do. Needless to say, a virtual environment provides ample incentive for cybercriminals to strike.
While the Wi-Fi at your workplace is probably up to par with the highest standards of security, many homes do not have this same protection. When on a video call, our surroundings, voices, and the information we speak is clear to the person on the other side of the camera. However, there may be a third party listening in through an unsecured Wi-Fi network.
Hackers commonly use malicious software such as spyware to watch and listen even when you’re no longer on a call. Luckily, there are several steps you can take to upgrade your security and protect yourself from spyware.
Firstly, be aware of everything that could be in your device’s camera’s view. This angle may be wider than you think, and it may be worth opting for a virtual background. When not in use, consider covering your camera with a piece of opaque tape or using a sliding cover that you can attach next to your camera. Make sure your router is updated, and your settings are secure, and ideally, use a VPN to deter cybercriminals.
5. IoT Fitness Devices
The fitness devices we carry around are beneficial—they hold us accountable to an exercise routine when the pandemic keeps us sitting down. They tell us what we could do to better our health, and they can even be stylish and fun accessories. Among other IoT devices, fitness devices have only become increasingly popular as time has gone on, with 127 new ones coming online every second. This makes the need to enhance our IoT cybersecurity more important than ever.
These devices can collect a lot of information about us, from data on our health to financial information to the conversations we have in the device’s vicinity. Why, you may ask? It’s their job. We need our FitBits and Apple Watches to remember what we do and what we want so that they can provide us with helpful information and reminders.
Like several other devices we use regularly, many IoT fitness devices prefer to offer their consumers the convenience of single-factor authentication over 2-factor authentication. Although it makes the user’s life easier, this decision puts the user at greater risk of cyber attacks from unwelcome and often invisible parties.
While it’s easy to instinctively give all of our apps every permission they ask for, it’s a good idea to limit permissions like location services and microphone access only to the apps that need them. Otherwise, your devices may become easy targets for infiltration.
Ultimately, how much online privacy you’re willing to sacrifice is up to you. While the risk of a cyber attack shouldn’t keep you up at night, it’s a good idea to take the steps that aren’t too difficult to be as safe as you can. The conveniences of the internet will be at your fingertips even after safeguarding your online privacy, so why not enjoy both?
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How Alternative Data is Changing the Finance Sector
Alternative data has been touted as the future for various companies. Financial services companies have taken a particular interest in the field as it has the potential to either provide completely novel signals or improve existing investment strategies.
However, understanding the scale and importance of alternative data has always been challenging as businesses in the sector are often shrouded in mystery. Investing is extremely competitive as alpha often depends on the signal strength other companies can acquire.
Now, however, the veil has been lifted, even if slightly. Finally, there is enough data to understand how far alternative data and web scraping have entrenched themselves into the industry, allowing us to understand their importance.
What is alternative data and web scraping?
Alternative data is a negatively defined term meaning everything that is not traditional data. The latter is considered to be everything that’s published regularly according to regulations, government action, or other oversight. In other words, it’s all the data from statistics departments, financial reports, press releases, etc.
Since alternative data is defined negatively, it’s every information source that’s not traditional. While the definition is somewhat broad, alternative data does have its characteristics. Namely, it’s almost always unstructured, comes in various formats (i.e., text, images, videos), and often is extracted for a highly specific purpose.
Data acquisition is significantly more complicated because both the sources and the formats are varied. Data as a Service (DaaS) businesses can resolve most of the acquisition issues; however, finding one that holds the necessary information can be complex.
Web Scraping and in-house solutions in alternative data acquisition
Many companies turn to building in-house solutions for alternative data acquisition. One of the primary methods for doing so is called web scraping. In short, it’s a method of automating online public data collection by employing bots.
These solutions go through a starting set of URLs and download the data stored within. Most bots will also further collect any URLs stored on the page for continued crawling. As a result, they can blaze through many sources within seconds or minutes.
Collected data is then delivered and parsed for analysis. Some of it, such as pricing information, can be integrated into completely automated solutions. Other data, such as anything from which investment signals might be extracted, is analyzed manually by dedicated professionals.
Web scraping is shaping the financial services industry
As mentioned above, financial services and investment companies have taken a particular interest in web scraping earlier than nearly anyone else. These businesses thrive upon gaining an informational edge over their competitors or the market as a whole.
So, in some sense, it was no surprise when web scraping turned out to be a key player in the financial services industry. So we surveyed over 1000 decision-makers in the financial services industry across the US and UK regions to find out more about how data is being managed in these companies.
Image Credit: Oxylabs; Thank you!
While internal data, as expected, remains the primary source of insight for all decision-making, web scraping has nearly overtaken it in the financial services industry. Almost 71% of our respondents have indicated that they use web scraping to help clients make business decisions.
Web Scraping and Growth Tendencies
Other insights are even more illuminating. For example, while web scraping has shown clear growth tendencies, we didn’t expect 80% of the survey respondents to believe that the focus will shift towards it even more in the coming 12 months. Nevertheless, these trends indicate a clear intent to change the dominant data acquisition methods in the industry.
Finally, there’s reason to believe that the performance of web scraping is equally as impressive. There may have been reason to believe that the process of automated data collection is simply a byproduct of hype. Big data has been a business buzzword for the longest time, so it may seem that some of that emotion might have transferred to web scraping.
Implementing Web Scraping
However, those who have implemented web scraping do not seem to think it’s pure hype. Over a quarter of those who have implemented the process believe it has had the most significant positive impact on revenue. Additionally, nearly half (44%) of all respondents plan to invest in web scraping the most in the coming years.
Our overall findings are consistent across regions. As the US and UK are such significant players in the sector, the conclusions likely extend to global trends, barring some exceptions where web scraping might be trickier to implement due to legal differences.
The survey has only uncovered major differences in how web scraping is handled, not whether it’s worthwhile. For example, in the US, it’s rarely the case that compliance or web scraping itself would be outsourced (12% & 8%, respectively). On the other hand, the UK is much more lenient regarding outsourced departments (22% and 15% for outsourced compliance and outsourced web scraping, respectively).
While the way data is being managed in the financial services industry has been shrouded in mystery for many years, we’re finally getting a better glimpse into the trends and changes the sector has been undergoing. As we can see, web scraping and alternative data play a major role in shaping the industry.
Becoming the true first adopters of web scraping, however, I think, is only the beginning. Both the technology and the industry are still maturing. Therefore, I firmly believe we will see many new and innovative developments in data extraction and analysis in the finance sector, which novel web scraping applications will head.
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How to Implement a Splintered Content Strategy
Content makes the marketing world go round. It doesn’t matter what your overarching marketing strategy looks like – content is the fuel source. You can’t go anywhere without it. The biggest problem is that content can be expensive to create. We operate in a business world where thousands of pieces of content are created every single second. Trying to keep up can feel like an expensive exercise in futility.
The key to successful digital marketing in an era of saturated online channels is extracting maximum value from your content. If the traditional approach is built around “single-use” content, you need to switch gears and opt for a multi-use approach that allows you to leverage the same content over and over again. One way to do this is by building out a “splintered” content strategy.
What is a Splintered Content Strategy?
The best way to understand the splintered approach to content creation is via an analogy. In the analogy, you start with one core topic that relates to your brand and readers. This topic is represented as a tree. Then, when you want to get more value out of the tree, you chop it down into big logs. These logs represent sub-topics of more significant topics. These logs can then be split and broken down into even smaller niches. (And this process of splintering the original topic into smaller/different pieces of micro-content can go on and on.)
Content splintering is not to be confused with content republishing or duplication. The mission isn’t to reuse the same content so much as to extract more value from the original content by finding new uses, applications, angles, and related topics. Not only does this approach help you maximize your ROI, but it also creates a tightly-correlated and highly-consistent web of content that makes both search engines and readers happy.
What You’ll Need for a Splintered Content Strategy
In order to get started with creating splintered content, you’ll need a few things:
- Keyword research. The process always begins with keyword research. First, you need to perform detailed SEO research to zero in on the keywords that specifically resonate with your target audience. This feeds your topic selection and actual content creation. (You can think of keyword research as developing a blueprint. Just like you can’t build a house without plans, you can’t implement a splintered content strategy without keyword research.)
- General topic. Armed with the right keywords, you can begin the process of choosing a broad topic. A general topic is a very basic, overarching topic that speaks to a specific target audience.
- Content writers. You’ll need a team of people to actually create the content. While it’s possible to do this on your own, you ideally want to hire content writers to do the heavy lifting on your behalf. This allows you to focus on the big-picture strategy.
- Consistency. A splintered content strategy requires consistency. Yes, there are ways to automate and streamline, but you have to ensure that you’re consistently churning out content (and that the content is closely correlated).
A good splintered content strategy takes time to develop. So, in addition to everything mentioned above, you’ll also need patience and resilience. Watch what’s working, and don’t be afraid to iterate. And remember one thing: You can always splinter a piece of content into more pieces.
How to Plan and Execute a Splintered Content Strategy
Now that we’re clear on splintered content and some of the different resources you’ll need to be successful, let’s dig into the actual how-to by looking at an illustration of how this could play out. (Note: This is not a comprehensive breakdown. These are merely some ideas you can use. Feel free to add, subtract, or modify to fit your own strategy needs.)
Typically, a splintered content strategy begins with a pillar blog post. This is a meaty, comprehensive resource on a significant topic that’s relevant to your target audience. For example, a financial advisor might write a pillar blog post on “How to Sell Your House.” This post would be several thousand words and include various subheadings that drill into specific elements of selling a house.
The most important thing to remember with a pillar post is that you don’t want to get to micro with the topic. You certainly want to get micro with the targeting – meaning you’re writing to a very specific audience – but not with the topic. Of course, you can always zoom in within the blog post, and with the splinters it produces, but it’s much more difficult to zoom out.
Turn the Blog Post Into a Podcast Series
Once you have your pillar piece of content in place, the splintering begins. One option is to turn the blog post into a series of podcast episodes. Each episode can touch on one of the subheadings.
If these are the subheadings from the blog post, they would look like this:
- How to prepare for selling > Episode 1
- How to find a real estate agent > Episode 2
- How to declutter and stage your property > Episode 3
- How to price your property > Episode 4
- How to choose the right offer > Episode 5
- How to negotiate with repair requests > Episode 6
- How to prepare for closing day > Episode 7
- How to move out > Episode 8
Depending on the length of your pillar content, you may have to beef up some of the sections from the original post to create enough content for a 20- to 30-minute episode, but you’ll at least have a solid outline of what you want to cover.
Turn Podcasts Into YouTube Videos
Here’s a really easy way to multiply your content via splintering. Just take the audio from each podcast and turn it into a YouTube video with graphic overlays and stock video footage. (Or, if you think ahead, you can record a video of you recording the podcast – a la “Joe Rogan” style.)
Turn YouTube Videos Into Social Clips
Cut your 20-minute YouTube video down into four or five different three-minute clips and soundbites for social media. These make for really sticky content that can be shared and distributed very quickly.
Turn Each Podcast Into Long-Form Social Posts
Take each podcast episode you recorded and turn them into their own long-form social posts. Of course, some of this content will cover information already hashed out in the original pillar post, but that’s fine. As long as you aren’t duplicating content word-for-word, it’s totally fine if there’s overlap.
Turn Long-Form Social Posts Into Tweets
Your long-form social posts can then be turned into a dozen or more individual short-form tweets. Find the best sentences, most shocking statements, and most powerful statistics from these posts and schedule a series of automated posts to go out over a few weeks. (You can automate this process using a tool like Hootsuite or Buffer.)
Turn Content Into an Email Campaign
Finally, take your best content and turn it into a series of emails to your list. You may even be able to set up an autoresponder series that slowly drips on people with a specific call-to-action.
Using the example from this article, a real estate agent might send out a series of 10 emails over 30 days with a call-to-action to get a free listing valuation.
Take Your Content Strategy to the Next Level With Splintered Content Strategy
There isn’t necessarily a proper way to implement a splintered content strategy. But, like everything regarding marketing, there’s ample room for creativity.
Use the parts of this article that resonate with you and adapt the rest to fit your vision for your content. Just remember the core objective of this entire approach: content maximization.
The goal is to get the most value out of your content as possible. And you do that by turning each piece of content you create into at least one more piece of content. If you do this efficiently, you will be successful.
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Successful AI Requires the Right Data Architecture – Here’s How
For companies that can master it, Artificial Intelligence (AI) promises to deliver cost savings, a competitive edge, and a foothold in the future of business. But while the rate of AI adoption continues to rise, the level of investment is often out of kilter with monetary returns. To be successful with AI you’ll want the right data architecture. This article tells you how.
Currently, only 26% of AI initiatives are being put into widespread production with an organization. Unfortunately, this means many companies spend a lot of time on AI deployments without seeing tangible ROI.
All Companies Must Perform Like a Tech Company
Meanwhile, in a world where every company must perform like a tech company to stay ahead, there’s increasing pressure on technical teams and Engineering and IT leaders to harness data for commercial growth. Especially as spending on cloud storage increases, businesses are keen to improve efficiency and maximize ROI from data that are costly to store. But unfortunately, they don’t have the luxury of time.
To meet this demand for rapid results, mapping data architecture can no longer stretch on for months with no defined goal. At the same time, focusing on standard data cleaning or Business Intelligence (BI) reporting is regressive.
Tech leaders must build data architecture with AI at the forefront of their objectives.
To do otherwise — they’ll find themselves retrofitting it later. In today’s businesses, data architecture should drive toward a defined outcome—and that outcome should include AI applications with clear benefits for end-users. This is key to setting your business up for future success, even if you’re not (yet) ready for AI.
Starting From Scratch? Begin With Best Practices for Data
Data Architecture requires knowledge. There are a lot of tools out there, and how you stitch them together is governed by your business and what you need to achieve. The starting point is always a literature review to understand what has worked for similar enterprises, as well as a deep dive into the tools you’re considering and their use cases.
Microsoft has a good repository for data models, plus a lot of literature on best data practices. There are also some great books out there that can help you develop a more strategic, business-minded approach to data architecture.
Prediction Machines by Ajay Agarwal, Joshua Gans, and Avi Goldfarb is ideal for understanding AI at a more foundational level, with functional insights into how to use AI and data to run efficiently. Finally, for more seasoned engineers and technical experts, I recommend Designing Data-Intensive Applications by Martin Kleppmann. This book will give you the very latest thinking in the field, with actionable guidance on how to build data applications, architecture, and strategy.
Three Fundamentals for a Successful Data Architecture
Several core principles will help you design a data architecture capable of powering AI applications that deliver ROI. Think of the following as compass points to check yourself against whenever you’re building, formatting, and organizing data:
Building Toward an Objective:
Always have your eye on the business outcome you’re working toward as you build and develop your data architecture is the cardinal rule. In particular, I recommend looking at your company’s near-term goals and aligning your data strategy accordingly.
For example, if your business strategy is to achieve $30M in revenues by year-end, figure out how you can use data to drive this. It doesn’t have to be daunting: break the more important goal down into smaller objectives, and work toward those.
Designing for Rapid Value Creation:
While setting a clear objective is key, the end solution must always be agile enough to adapt to changing business needs. For example, small-scale projects might grow to become multi-channel, and you need to build with that in mind. Fixed modeling and fixed rules will only create more work down the line.
Any architecture you design should be capable of accommodating more data as it becomes available and leveraging that data toward your company’s latest goals. I also recommend automating as much as you can. This will help you make a valuable business impact with your data strategy quickly and repeatedly over time.
For example, automate this process from the get-go if you know you need to deliver monthly reporting. That way, you’ll only spend time on it during the first month. From there, the impact will be consistently efficient and positive.
Knowing How to Test for Success:
To keep yourself on the right track, it’s essential to know if your data architecture is performing effectively. Data architecture works when it can (1) support AI and (2) deliver usable, relevant data to every employee in the business. Keeping close to these guardrails will help ensure your data strategy is fit for purpose and fit for the future.
The Future of Data Architecture: Innovations to Know About
While these key principles are a great starting place for technical leaders and teams, it’s also important not to get stuck in one way of doing things. Otherwise, businesses risk missing opportunities that could deliver even greater value in the long term. Instead, tech leaders must constantly be plugged into the new technologies coming to market that can enhance their work and deliver better outcomes for their business:
We’re already seeing innovations making processing more cost-efficient. This is critical because many of the advanced technologies being developed require such high levels of computer power they only exist in theory. Neural networks are a prime example. But as the required level of computer power becomes more feasible, we’ll have access to more sophisticated ways of solving problems.
For example, a data scientist must train every machine learning model. But in the future, there’s potential to build models that can train other models. Of course, this is still just a theory, but we’ll definitely see innovation like this accelerate as processing power becomes more accessible.
Additionally, when it comes to apps or software that can decrease time to value for AI, we’re in a phase now where most technology available can only do one thing well. The tools needed to productionize AI — like storage, machine learning providers, API deployment, and quality control — are unbundled.
Currently, businesses risk wasting precious time simply figuring out which tools they need and how to integrate them. But technology is gradually emerging that can help solve for multiple data architecture use cases, as well as databases that are specialized for powering AI applications.
These more bundled offerings will help businesses put AI into production faster. It’s similar to what we’ve seen in the fintech space. Companies initially focused on being the best in one core competency before eventually merging to create bundled solutions.
Data Marts vs. Data Warehouses:
Looking further into the future, it seems safe to predict that data lakes will become the most important AI and data stack investment for all organizations. Data lakes will help organizations understand predictions and how best to execute those insights. I see data marts becoming increasingly valuable for the future.
Marts deliver the same data to every team in a business in a format they can understand. For example, Marketing and Finance teams see the same data represented in metrics that are familiar and – most importantly – a format they can use. The new generation of data marts will have more than dimensions, facts, and hierarchy. They won’t just be slicing and dicing information — but will support decision-making within specific departments.
As the technology continues to develop, it’s critical that businesses stay up to speed, or they’ll get left behind. That means tech leaders staying connected to their teams, and allowing them to bring new innovations to the table.
Even as a company’s data architecture and AI applications grow more robust, it’s essential to make time to experiment, learn and (ultimately) innovate.
Image Credit: by Polina Zimmerman; Pexels; Thank you!