Technology is amazing. And it seems to get even more impressive every year. Every day, there’s some new gadget or breakthrough in the news worth getting excited about. And every year, our collective capabilities as a species seem to be getting broader and further-reaching.
For decades, we’ve seen a veritable explosion in technological development – an exponential curve of innovation that constantly takes us to new heights. And we’re told that this technological curve is continuing – that we’re still growing exponentially, with massive leaps forward every year.
But is this really true?
There’s a compelling case to be made that while technological progress is still moving forward, it’s slowing down. And if that’s true, we need to be prepared for the consequences of such a shift in momentum.
The Low-Hanging Fruit
Our first clue that tech innovation is slowing down is a change to the traditional model of tech development. In many ways, technology is all about solving problems; every new tech advancement is a solution for some long-standing issue. It makes sense that our current wave of tech advancement resembles an exponential curve because new technologies make it faster and easier to solve other, often unrelated problems.
For example, the development of the internet was revolutionary for technological development overall. People now can review massive databases of information, communicate with other like-minded professionals, share ideas, and even publish their ideas to a broader audience. These capabilities have led to new ideas and new technologies that otherwise could never have been possible.
But this trajectory is limited. In the course of tech development, we often explore new territory very quickly – but only for a limited period of time. Think of it this way. As early human beings began exploring new territory, they found themselves surrounded by an abundance of game animals, trees, and fish. But as they hunted, harvested lumber, and fished, many of those resources began to dry up. In other words, they’d taken all the low-hanging fruit, and were forced to come up with new ideas. They had to explore new territory, invent new agricultural methods, and even find new sources of nourishment.
Our current burst of technological progress could be almost exclusively focused on low-hanging fruit. We’re solving the easiest problems first, and we’re solving them in quick succession. But the hard problems – like general intelligence-level AI, efficient battery storage, and even finding a cure for cancer – show little progress even over the course of decades.
Any futurist will tell you that all of humanity’s problems can be solved eventually. But we have to understand that our pace of innovation tends to slow down as we master all the “easy” problems and start looking at the “hard” ones.
Digital Innovation vs. Chemical Innovation
We also need to understand that most of the tech progress we’ve seen in the past 30 or 40 years has been limited to the digital world. These technologies have been astounding, accelerated by novel high-growth startups, but they’ve almost been exclusively focused on digital communication efficiency. The internet, software engineering, and AI have all taken amazing strides forward. But on the level of chemistry and physics, we’ve advanced very little.
We’re still incredibly reliant on non-renewable resources to fuel our consumption. We haven’t discovered any groundbreaking new elements, molecules, or chemical processes. And our understanding of the universe at the base level of physics hasn’t changed much, if at all, since the 1980s. We’re still struggling to reconcile major physics ideas that were first introduced nearly 100 years ago.
So what? Digital innovation may be so incredibly fast-paced that it can be the conduit through which we solve all other problems, right?
That may not be the case. For the majority of the digital age, we’ve depended on the momentum of Moore’s law. Moore’s law is an informal observation that the number of transistors that we can fit on a dense integrated circuit tends to double every two years. In other words, our computing power can double every two years, leading to major breakthroughs in a number of different technologies.
However, it appears that the age of Moore’s law may be nearing its end. There’s an absolute physical limit to the amount of space on a transistor chip. With exponential growth since the 1960s, we’ve gone from integrated circuits with 10 transistors to ICs with something like 10 billion transistors. How much further can we really go without breaking the laws of physics?
We may be able to push things even further, but to do so, we’ll need to invest in high-end chipmaking equipment and innovate entirely new manufacturing methods. Doing so will sharply increase the cost of chip production, ultimately negating the cost-effectiveness benefits.
Of course, there’s a solid counterargument here. It holds that digital innovation may continue at the same rate of exponential growth even if we’re unable to maintain the consistency of Moore’s law; even if the number of transistors on a chip remains more or less stagnant, we can find new ways to use the chips we already have.
Consumer Products and Perceptions
We see an endless conveyor belt of new gadgets and new consumer-facing technologies emerging on a constant basis. But how innovative are all these products, really?
Apple introduced the iPhone, a game-changing new type of technology, back in 2007. It combined several existing technologies into one, comprehensive unit, and changed the way we think about mobile tech forever. In the past 14 years, how much innovation have we truly seen in this space? We’ve seen a flock of competitors coming out with smartphone options of their own. And of course, we’ve seen Apple unveil a new model of iPhone nearly every year.
But these new, “innovative” smartphones only make marginal improvements to the original formula. Their cameras are sharper. Their processing power is beefier. Their storage capacity and battery life are more robust. But they can hardly be considered new technology, at least not at the same groundbreaking level of their predecessor.
As consumers, we’re getting used to a slower pace of technological breakthroughs. We’re content to see new smartphones, new video game consoles, and new TVs that offer merely slight improvements over their counterparts, rather than completely changing the game – and this is enough for us to continue thinking that we’re living in an age of exponential technology growth.
What Does a Tech Slowdown Mean?
So what does all this mean? Is it really a big deal that there’s a major tech slowdown?
Much of our economic growth depends on technological innovation. Countless retirement plans like 401(k) depend on the growth of the stock market, which in turn depends on baseline economic growth; a slowdown in tech innovation leads to a slowdown in GDP, resulting in a cascade of economic effects that could cripple the economy at large.
The larger danger is that we don’t realize the tech slowdown is occurring until it’s too late. Tech stocks are being traded and inflated as if they’re inventing fundamentally new technologies; as a general trend, they multiply in price in response to even the most meager announcements. If carried out for years to come, this could result in a massive tech bubble, or a broader investment bubble, that pops once investors begin realizing just how slow our growth has crawled.
Of course, this slowdown may be merely a temporary lull. Just as the digital era sparked the launch of a million new problem-solving technologies, we may be on the cusp of another, equally paradigm-shifting breakthrough. To get there, we’ll need to refocus our research efforts and accept the limitations of the digital space.
Our half-century long honeymoon with explosive tech growth in the digital era has been incredible, but it’s nearing its end. If we want to keep moving forward (as we should), we need to reset our expectations, redouble our research efforts, and start looking into new territory for technological expansion.
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.
Image Credit: Pixabay; Pexels; Thank you!
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.
Image Credit: by Kampus Production; Pexels; Thank you!
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!