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How AI in Video Will Enhance Work in the Modern-Day Work Environment – ReadWrite

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Emily Senkosky


Shaking off the dust from what could be described as the longest year known to man — remote work is a hot topic in the world of employment. By establishing both its benefits, as well as its challenges, remote work has people talking about its permanence. What is more, employees have become accustomed to remote working, in fact, many of them actually prefer it to the office. According to a FlexJobs survey, 65% of employee respondents reported wanting to be full-time remote post-pandemic, and 31% want a hybrid remote work environment — that’s 96% who desire some form of remote work.

These numbers inevitably mean that the methods in which we worked during the pandemic, primarily via the screen and through video calls, will have some longevity.

In the past year, there has been a daunting amount of “incidental” or unintentional content creation via the many different digital platforms we now operate on. With massive amounts of data, however, there are large sums of insight to be had.

With the right tools, your business can work smarter, not harder, and have this valuable knowledge extracted from content derived from workers’ day-to-day interactions. This acumen can be the competitive edge your business needs as we move forward with our technological workday—with AI in video enhancing many factors of the modern age work world.

How AI Has Changed the Video Streaming Experience

With the growing amounts of online meetings and content creation happening in 2021, the key to video streaming in the modern work world is navigating it. Video has the potential to bring content to life, but more importantly, it gives the ability to access what’s in the video in an intuitive and efficient way.

Let’s look at it this way, would you buy a textbook if it had no table of contents, index, or chapters? Of course, you wouldn’t. It would be crazy to have to just find your way through pages of unstructured text, but that’s what we do for video.

By implementing AI into video, you have the ability to customize and easily access all of the contexts that exist in the video’s contents.

Through Machine Learning (ML) and Natural Language Processing (NLP), AI can do all of the hard work of deriving data for you—helping to mitigate your search time and any fatigue that might come along with it. Through audio and visual data, AI takes all of the available understanding from the video and tags content by keywords, concepts, and important and relevant topics.

The ML and NLP then construct a transcript, and from there, the AI creates an intuitive index—creating transcriptions, chapters, and chapter titles, and finally a table of contents. This makes searching for content easier and more efficient for each user.

Video now, significantly differs from video in the past.

To date, when it has come to utilizing the power of video, most of the time it has been done in a highly meticulous manner.  Rather than manually tagging video media with editor tool applications—crafting tags one-by-one or creating a time-sliced video by tagging minute intervals—AI can do the work for you.

One label or title, or a tag at “minute six” is pretty much meaningless when it comes to searching because the keyword is limited to the interpretation of the publisher.

When you are looking for anything—whether that be in the grocery store or on Google’s search engine—you most often have something specific in mind. AI allows for a new variation of video tagging with the capability to draw relevance to a plethora of topics and keywords. This enhances both the approachability and scope of video organization and use. AI saves companies staff, time, and resources to apply these methods to their existing bank of video content.

OCR and How it is Changing Video Conferencing

An emerging video technology, Optical Character Recognition (OCR), can now read the still-snapshots in your video and determine if any relevant text can be drawn out. This can be used on things such as PowerPoint presentations in the background or words written on a whiteboard behind the speaker in a video. By combining both the audio recording and the textual elements derived from OCR, AI can gain more content than ever before and create an all-encompassing transcript of the video.

This AI-driven translation gives the capacity for “media contextualization” — which simply means the ability to look inside a video and draw out all of the pertinent information that is needed at that moment.

This process is made possible by NLP and ML, which combine their capabilities to create a knowledge base to which all relevant information is centralized. The deep learning process can then come in to analyze and organize all of the text into a systemized database, understanding when the context has changed.

Then, the AI-driven technology knows when to create a new “chapter” accordingly — outputting a phrase or blurb that is the best-suited title for that segment of the content. From there, an entire table of contents is manifested for each video recording, with all of the information accurately and efficiently organized.

By allowing the AI to go further and make sense of all the data points demonstrated in a video, the technology can help to propagate relevant information across departments within a company.

This is important, especially with the uptick in conferences recorded in the modern work world. There are miles and miles of potential insights to be had within a business’ recorded video calls, but a need to make sense of it in a framework that is relevant to the individual.

AI-driven technology, with OCR implemented, helps to make contextual connections throughout all of the information and create a user-friendly structure. This makes for a much more intuitive video-user experience and allows people to find and share exactly what they are looking for.

Connecting Context Through Ontology and DBpedia

With a well-organized, informed, and centralized base of knowledge, AI innovates further through what is known as ontology. Ontology is a set of concepts and categories in a subject area or domain that shows common properties and their relations.

One of my clients, a company called Ziotag, uses proprietary AI ontology technology to create tags within video media. This is initiated by first allocating all of the different terms that people might talk about on a certain topic.

With this insight, the AI can do its magic and create ontology tags to procure more than 50,000 concepts—finding the ways to which they can all relate to each other.

This creates a multi-faceted and dynamic foundation of data points that could almost represent a human brain—using concepts, keywords, and context-understanding to deduce what a user might be looking for in the knowledge base.

When this local apprehension is applied to the bigger picture of the internet, the possibilities are endless. A project by Wikipedia known as DBpedia extracts structured information from 111 different language editions of Wikipedia to elicit knowledge using Semantic Web and Linked Data technologies.

The largest DBpedia knowledge base exists in English and consists of over 400 million facts that describe 3.7 million things — just to give you a bit of scale. These mappings were created via a worldwide crowd-sourcing effort in the hopes of enabling knowledge from all of the different Wikipedia editions to combine and create context from data.

Ziotag’s ontology approach mirrors this data connection strategy, helping the AI to discern concepts from a variety of resources.

By comprehending context from a vast amount of knowledge, AI can transform video, giving immeasurable insight to those that use it.

These insights can be seen when searching words with similar names but very different meanings. To toss out a simple example, look at the word ‘salt.’ When you searched that word, were you looking for the scientific compound sodium chloride or table salt? Or maybe you were looking for the local restaurant titled as such or the history of mining for it?

Ontology AI technology can distinguish what you were looking for through linking meaning vectors, thereby tailoring the organization of concepts in video to your individual needs.

Automating Business Processes

The combination of all of these innovations in AI can change the processes to which modern-day companies can perform digital operations, drastically increasing both efficiency and interconnectivity.

By extracting MetaData from employee interactions and team meetings, the AI-driven technology can make expertise visible across departments.

This makes vital information that employees need readily available, traversing any silos that might exist in the business infrastructure. Once knowledge is centralized, AI can be proactive and create delightfully personalized experiences.

Gleaning context from employee’s interactions both on video or through different applications such as Slack or email, AI can start to create a dialogue for personal workflows.

In large companies with a distributed workforce, this can be especially pivotal — integrating the different communications channels and compiling all of the exchanged information like a diligent librarian. Furthermore, the AI can also gain further cognizance with access to data on the person’s role in a company or their daily workload.

This could, for example, help individuals get up to speed quickly if they could not attend a meeting or were out for a while due to vacation. What is the best part is that the information can be absorbed in a completely customizable manner.

This information can be translated back in a way that is most convenient to the worker — whether that be expanding chapters, searching subjects with ease, or reading and listening at their pleasure.

Automating processes will revolutionize how AI is implemented into business, changing the digital workplace to be more efficient and less disparate. This by proxy will inform a new way of working across many industries. Computer science and AI is a massive structure, and its foundation was built on a vast array of developments.

When the higher goal is focused on, many pertinent innovations can be achieved.

The next generation of AI will be able to do even more, with each floor of the computer science structure defined by the level below and no known limit to its potential height. AI in video will be a multiplier in the construction of this edifice, propelling the remote work world into the future.

By: Emily Senkosky, in collaboration with Graham Morehead at Ziotag Inc., a New York-based technology company that uses Artificial Intelligence (AI) to make searching and navigating video and audio content a seamless experience.

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Emily Senkosky

Writer

Emily Senkosky is a writer living in Medellin, Colombia. She has experience in topics from Artificial Intelligence (AI) in transportation, to AI in enterprise knowledge, to the music industry, all the way to themes on positive psychology. Emily loves the art of wielding a new perspective through language and finding ways to make complicated subjects digestible to her readers. Always keeping her finger on the pulse of society through her wide variety of interests, she also loves to play the devil’s advocate and bring new ideas to the table.
After running her own arts magazine for many years, she often freelances pieces that combine arts and entertainment and her love for nature. As a film photographer and avid outdoor adventurist, she writes these pieces in her own time and supplements her articles with her artistic photography. With a capacity for insatiable curiosity, Emily loves using her ability for linguistics to explore new ideas and subjects daily.

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How Alternative Data is Changing the Finance Sector

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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).

Conclusion

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!

Julius Cerniauskas

CEO at Oxylabs

Julius Cerniauskas is Lithuania’s technology industry leader & the CEO of Oxylabs, covering topics on web scraping, big data, machine learning & tech trends.

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How to Implement a Splintered Content Strategy

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How to Use SEO if You Have No Experience


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.

Conclusion

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!

Timothy Carter

Chief Revenue Officer

Timothy Carter is the Chief Revenue Officer of the Seattle digital marketing agency SEO.co, DEV.co & PPC.co. He has spent more than 20 years in the world of SEO and digital marketing leading, building and scaling sales operations, helping companies increase revenue efficiency and drive growth from websites and sales teams. When he’s not working, Tim enjoys playing a few rounds of disc golf, running, and spending time with his wife and family on the beach — preferably in Hawaii with a cup of Kona coffee. Follow him on Twitter @TimothyCarter

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Successful AI Requires the Right Data Architecture – Here’s How

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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:

  • Cheaper Processing:

    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.

  • Bundled Tools:

    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.

Conclusion

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!

Atul Sharma

Atul founded Decision Intelligence company Peak in 2015 with Richard Potter and David Leitch. He has played a pivotal role in shaping Peak’s Decision Intelligence platform, which emerged as an early leader in a category that is expected to be the biggest technology movement for a generation. Peak’s platform is used by leading brands including Nike, Pepsico, KFC and Sika.
On a mission to change the way the world works, the tech scaleup has grown quickly over the last seven years and now numbers over 250 people globally. Regularly named a top place to work in the UK, this year Peak received the Best Companies 3-star accreditation, which recognizes extraordinary levels of employee engagement.
Prior to Peak, Atul spent over 20 years working in data architecture and data engineering. He has worked on designing and implementing data integration and data warehouse engagements for global companies such as Morrisons Plc, The Economist, HBOS, Admin Re (Part of Swiss Re) and Shell.

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