The idea of talking to a machine a few decades ago would have belonged to the realm of science fiction, if not idiocy. And yet, after years of rigorous multidisciplinary research and development, here we are. A future where millions of users command and interact with a growing number of devices using nothing but their voice. All thanks to cutting-edge voice recognition technology.
3 Reasons Why Voice Recognition Will Change Our Lives
Voice recognition has seen a great deal of development in recent years. Smartphones and smart speakers housing AI-based virtual assistants have become a common sight in every household. I distinctly remember trying out the Google Assistant and Microsoft’s Cortana back in the day when they were initially rolled out.
Apart from a few oddly surprising but clearly pre-programmed jokes, the speech recognition itself was fairly inaccurate and frustrating. The conversations weren’t organic. One would have to speak with deliberation, adding long pauses and clear punctuations.
All in all, it was a gimmicky-looking feature at best. Enough to stimulate your typical tech nerd but nowhere even close to becoming an everyday technology.
Fast forward to today, and cutting-edge algorithms can beat any of us at language processing.
Add to that the program’s ability to draw from various sources and languages across the internet, and the conversation is really over. However, it is impossible to ignore the breathtaking rate at which language recognition has evolved in the past few decades.
And much like smartphones and the internet, voice recognition is quickly becoming an essential and indispensable part of our lives. Therefore, it might be worth investigating what a world populated with sophisticated voice recognition algorithms might look like; and how they’ll change our lives.
Voice Recognition can Trigger a Multi-Industry Disruption, Empowering SMBs and Tech Giants Alike
The most significant benefit of being able to talk to your devices is that it is much more convenient than using a computer or a smartphone. Given this advantage, we expect voice recognition to improve the communication between brands and customers in the coming future. This will allow for a greater degree of personalization and increased customer satisfaction.
And this is already happening. Google reports that around 58% of the people who own voice-activated speakers use them to create and manage shopping lists. And around 62% of the same are willing to buy products via their speaker.
Industries like e-commerce stand to benefit the most from such a transition. Especially when smart speakers can help with reducing buying resistance for the customers. In light of such positive trends, it is obvious that voice search is changing the way we shop.
But beyond just shopping, sectors like healthcare and education also benefit immensely from voice recognition technology. Alexa is helping out senior dementia patients, while Google Assistant can help enhance learning for young kids. These are just some select examples, but they indicate a growing trend most industries are catching up on.
How Voice Recognition will take over the consumer market
Apple, Google, and Amazon (along with a few other smaller players) have been relentlessly pouring millions into research and development for application and home-based virtual assistants. It’s no secret that voice will be a massive business opportunity for these giants, hence the aggressive competition.
We don’t know how Big Tech will monetize voice recognition, but their involvement will benefit the industry for sure. As tech companies continue to develop voice technology further, we can expect voice-based devices to get better over time. We’ll see a significant improvement in the pricing, functionality, and even accessibility of these devices. With the improvement curve being an exponential one, following the development curve closely.
Such rapid growth will likely sprout multiple opportunities for voice recognition, many even within previously untapped niches and industries. Smaller software and app development companies will be incentivized to build on the shoulders of these tech giants. They’ll develop their products and service based on the underlying voice recognition architecture built by bigger companies. Allowing for drastic improvements in areas like customer experience and usability for their products.
It’s a win-win for both. Smaller players get access to sophisticated voice recognition technology, something they couldn’t develop on their own. And larger players get an ever-increasing market share by opening up their technology. This will inevitably boost voice adoption and allow the technology to penetrate deeper within the consumer market.
To conclude, voice recognition will soon be empowering a growing number of industries. The estimated market size for voice recognition-based tech and devices is expected to reach around $27.16 Billion by 2026. With such promising figures and groundbreaking implications, voice recognition is sure to take over the consumer market in the coming years.
Rethinking our Privacy in the Age of Virtual Assistants
Voice recognition has a ton of potential. It can significantly enhance business operations, improve consumer-device interaction, and carry the machine-human relationship to a whole new level.
However, behind all the hype lies a glaring but ugly problem, most of us are too afraid to admit. I’m talking about privacy.
Around 50% of smart speaker users fear that voice technology places their privacy at risk. On one level, there is always the risk of cyber-attacks and data breaches. And brands are trying to address this by developing devices that do much of the voice recognition locally. This is to prevent any data from being transferred over to the cloud. But on a deeper level, we face the problem of accidentally installing corporate spyware right into our homes.
It’s no secret that tech companies are behind every bit of data and personal information we produce. While harvesting data to roll out personalized ads may not be unethical in itself, there is certainly a line that needs to be drawn here. To be clear, smart speakers aren’t recording your everyday conversations (yet). But they still need to be ‘awake’ looking out for the trigger words that wake them up.
Again, the problem here isn’t that amazon employees are listening to your dinner table conversations; but instead of ‘privacy expectation.’
What is Privacy Expectation?
Privacy expectations, in simple terms, is the level of privacy consumers can expect from companies while using their products and services.
And new technologies, especially something like smart speakers and virtual assistants, generally tend to lower down our privacy expectations. In other words, they make us comfortable with sharing a growing amount of data as we get familiar with the technology.
At the risk of sounding like a conspiracy theorist, I’m not arguing that voice recognition (in its current form at least) is a major threat to our privacy. In fact, most companies in the business are reasonably compliant to privacy laws; and whatever audio samples that companies do record are primarily used for training the algorithms.
But the problem here is that voice recognition is significantly different from other data-harvesting technologies because it shifts the privacy paradigm much further towards the companies and away from the consumers. For example, with social media, tech companies only had access to the data that users were willing to publish online.
While with something like smart speakers, if companies choose to track us in the future, the option of choosing the information we wish to share (and not share) is simply absent. We run the risk of opening our entire lives to algorithms that’ll scan every word we utter with the sole intent of getting us to buy a pair of socks or something.
A new Privacy Paradigm
Bear in mind, though, that this is entirely a hypothetical possibility. But the fact that corporations haven’t done it yet, isn’t sufficient evidence to believe that they won’t do it in the future. After all, Companies need to make money from the smart speakers they sell, especially when they are practically giving them away for free. And the way they’ll make up for it is by locking the users in their ecosystems and targeting them with voice-based ads.
With every breakthrough device/technology, consumers are willing to trade in a bit more of their information and privacy in exchange for a better experience and service. Voice technology takes it to a whole new level because the data we are talking about here isn’t digital but biometric. No other consumer technology in the past has opened up such large volumes of biometric data to algorithms, and the implications of doing so can go way beyond our wildest speculations.
How Voice Recognition Will Change Who We Are.
The interesting thing about conversing with a smart speaker is that it’s not the same as how we usually communicate. Everyday conversations are full of several subtleties like body language cues, facial expressions, and interpersonal chemistry—stuff that’s simply absent in a smart speaker.
More than that, smart speakers are designed to be ‘smart’; coupled with the internet, they are practically omniscient and considerably more engaging and entertaining than other humans. Is it plausible then that interacting with my amazon echo could change the way I communicate?
Could it inflate my expectations when it comes to conversations? Leaving me dissatisfied and thus, less interested in talking to other people. I know that sounds far-fetched, but if video games can change our brains and texting can change our language, why is it a stretch to assume that voice recognition can change how we communicate?
Voice-based virtual assistants are expected to do well in areas like education and even therapy. These are places that were traditionally thought to be the forte of humans. But it isn’t just the assistant’s universality that is fascinating here, but also its personality. Alexa isn’t just an assistant. It’s no longer just a tool, but with every patch and update, as its engineers strive to make its responses look more organic, Alexa slowly gains personhood.
That opens a brand new can of worms. We pass on a whole lot of power over to our assistants (and by extension, the companies that control them) the day we start treating them like people and not machines. And although that makes a great science-fiction plot, we must admit that such a future is much closer than it appears.
Voice recognition and AI
Consider the example of Replika. Replika is a chatbot app marketed as ‘the companion who cares.’ I discovered Replika from the Youtube comment section of a music video. It turns out when users expressed that they felt lonely, Replica recommended them a music video to make them feel better. And it seems like it worked. The comments section is teaming with warm messages and love letters directed to Replika as people express their gratitude towards the AI.
And Replika is just a chatbot, a good one I bet, but still a chatbot. Imagine then what fully flushed out voice assistants—programmed with a natural-sounding human voice and empathy—could do.
If virtual assistants and chatbots can promote music to their loyal users, they can just as easily promote products or ideas. Don’t get me wrong, It’s fine as long as our Personal AIs tell us silly jokes and offer trivial recommendations. But the fun quickly ends the day they start telling us what is true or who to vote for. Bear in mind that the distance between the two is alarmingly short and constantly shrinking. Again, if social media can impact our political views, why can’t voice assistants?
I admit these are some extreme examples. And I do not wish to indicate that the voice assistants are inherently bad or evil. My only claim here is that voice recognition can fundamentally change not just the digital-human interface but also our everyday lives.
As we get comfortable with using voice just as much as we use our screens, we might also end up getting comfortable with trusting algorithms just as much as we trust people. Voice recognition is the humanization of our machines at its finest.
Voice recognition is an upcoming groundbreaking technology with far-reaching implications, especially in contrast with its initial use cases. And while it can be alarming to witness Tech giants racing for an oligopoly within the sector, the presence of reliable free and open-source voice recognition alternatives is certainly assuring.
In the coming years, we can expect voice-powered devices to improve our business as well as personal experiences. Having meaningful conversations with our computers will no longer be science fiction. Wide-scale voice adoption will compel us to think deeply about areas like privacy and the influence of technology on our lives.
It’s an exciting future full of ecstatic adventures and unexpected dangers. And although we may not know where voice recognition might take us, we can at least know for sure that it’ll change the world as we know it today.
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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.
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.
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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!