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Will Self-Driven Cars Replace Humans in the Future?Will Self-Driven Cars Replace Humans in the Future? – ReadWrite

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The driverless revolution in cars is just starting despite there being some twists and turns in its adoption. The invention of self-driven cars is poised to impact the transportation industry greatly. Since the first car got invented decades ago, their basic control has been a human being behind the steering wheel.

There had been no major strides as far as vehicle control is concerned. However, that is changing drastically, thanks to the new technological advancements happening in the automotive industry. Tech experts have been working on using technology to enhance car safety.

One of their biggest milestones, however, has been the introduction of self-driven cars. Right now, various car manufacturers are making cars that can move without any human input. But then, it is not certain what the future holds for these cars and conventional human-driven ones.

Will Self-Driven Cars Replace Humans in the Future?

Many people ask themselves whether this advancement spells doom for regular cars. This article looks into whether the world will fully adopt self-driven cars in the future. Besides, it looks into how safe they are, what they mean environmentally, etc.

Here’s all the information you need to know.

How Exactly Do Self-Driving Cars Work?

Before getting into the nitty-gritty of self-driving cars, it is vital to understand exactly how they work. Many people think that these cars use rocket science to move from one place to another. However, they work in a simple and easily understandable manner.

These cars basically use cameras mounted at the front, back, and sides. These cameras detect objects around the car; the car processes this information and steers to avoid hitting them. Then, there’re in-car computers that can understand traffic rules then guide the car to any destination.

You could be thinking about how they estimate braking distance and many more. These cars bounce sound and light off objects to determine how far they are and calculate the time it will take to stop without hitting the objects.

Besides, they can detect motion, for instance, when in moving traffic. They can determine the direction that traffic is taking and its average speed before adjusting accordingly. Although this isn’t a conclusive description of how these cars work, it gives a short description of how self-driven cars work.

Why is it Taking Longer Than Expected to Get Self-Driving Cars on the Road?

One would expect that these sophisticated cars should have been popular worldwide today. However, these types of cars are yet to become as popular as they should be. Experts have attributed this because these cars rely on artificial intelligence to work, a technology whose adaptation has been slow. There are a lot of things that factor in this delay.

  • One of the biggest challenges has been in training in-car computers on how to navigate roads. The easiest way to do this ideally is to teach cars with billions of videos showing real-life drives. That teaches the computers proper driving behavior making the road safe for all users. 
  • Even though engineers pour their heart and soul into developing a technology that facilitates a self-driving experience, they still have some shortcomings that are deal-breakers. These cars’ mistakes leave them falling down as obstacles and causing a ruckus.
  • Although billions of videos can serve this purpose well, it is becoming increasingly difficult to train cars about how to navigate certain situations. For instance, it isn’t easy to train cars on what to do when they encounter debris on the road or during an accident, as few videos show that.

However, carmakers and tech experts have tried to create as many such situations to train cars. They have engineered certain situations to represent real road situations. Despite doing this, it is evident that training cars on every road situation is daunting and yet to be possible; thus, their slow adoption.

Will People Accept Self-Driving Cars?

Another thing that will determine whether self-driven cars will take over is whether they’ll get accepted. Perception issues aren’t new in the tech world, and that’s one thing self-driven cars will need to overcome. Otherwise, getting accepted in a world with varied opinions on new technologies isn’t easy.

  • According to a 2019 survey, 71% of people said they’d be scared to be driven by a computer. That shows there’s a huge percentage of people that are yet to accept driverless vehicles. According to many people, they’ll need to see evidence of how safe this technology is before they start to trust it.
  • Reuters’ further research in 2019 showed that many people think that driverless cars are dangerous than those driven by human beings. Experts say that this technology will not get accepted easily because of how it has performed in its early stages. There have been accident reports, and that doesn’t click well the less tech-savvy.

Thus, the first step towards enhancing the acceptance of this technology is educating the masses. Tech experts must teach people about AI and how important it is. Besides, the sensitization should extend to how this technology will help to change the transport industry.

The best thing about technology is that it only gets better. Although people haven’t embraced AI in cars, technology keeps improving in safety and other aspects. Soon, people will have no option other than accept it as it will become safer to be a passenger in an autonomous car than a regular car.

Are Self-Driving Cars Safe?

Safety is a concern everywhere, and it is even more vital when it comes to cars. Self-driven cars have to prove that they’re safe if they’re to replace regular cars fully. Thankfully, expert reports from the tests that have already been conducted show that these cars are safer than human-driven ones.

However, self-driven cars are yet to become safer and better in all aspects. There are still instances in which human-driven cars have proved to be better. As mentioned earlier, it takes more than following traffic rules to be safe. Self-driven cars cannot perform some critical functions that humans can perform.

But then, self-driven cars excel in certain aspects that humans have constantly failed. For instance, a self-driven car doesn’t get fatigued, drink, drive, or suffer from sleep deprivation. All these have been attributed to many road accidents globally; driverless cars help mitigate them, making them safe.

The accident reports that have involved self-driven cars have led to heated debates. One of the most popular cases is when a woman got hit by a car in self-driving mode. Before that, a man had died as his self-driven Tesla hit a truck, adding to the statistics.

It is worth mentioning that self-driven cars have a long way to go as far as safety is concerned. The safety limitations that these cars are experiencing are making it difficult to convince people. In general, self-driven cars may not outperform human-driven cars soon regarding road safety.

Are Self-Driving Cars Going to be Good for the Environment?

Eco-friendliness is another concern that car manufacturers and users have to think about. Self-driven cars have been a great part of this discussion. Many people question whether these cars can help to solve the existing environmental issues and how efficient their solutions will be today and in the future.

There have been different arguments to support this debate. For instance, experts say that self-driving cars will reduce the need to own a car as people will be requesting one when they need to move. That will mean lesser car trips and fewer emissions into the environment, thus conserving it.

Besides, some technological advancements are also positively impacting the environment. For instance, experts say that adaptive cruise control is making the environment more efficient by 5-7%. But then, there isn’t any solid evidence showing that these cars are better for the environment.

Although regular cars emit more gases to the environment, they do not impact industries. Therefore, there’s no reason to rate them lower than self-driving cars. The only positive has been better fuel efficiency, but that doesn’t make any machine good for the environment.

Even though preliminary research has shown that driverless cars reduce the number of trips people make, it isn’t conclusive. Many researchers are getting their hands dirty out there to confirm that.

Meanwhile, the question of whether they’re an eco-friendlier option than regular cars hasn’t been settled. There needs to be more proof to confirm that these cars are an excellent option.

What Benefits Could Self-Driving Cars Offer?

Self-driving cars have faced a lot of hurdles in regards to acceptance and adoption. However, the benefits of using these cars outweigh the disadvantages making them worth thinking about. As manufacturers continue to convince people to accept this technology, the final decisions lie with users.

If you’re undecided about whether to buy a self-driving car or not, you need to consider the benefits and limitations first. According to experts, one thing that driverless cars will impact is road safety. They will significantly reduce the number of automotive accidents reported making them worthy investments.

  • Some statistics associated with human-driven cars are worrying. For instance, according to the National Highway Traffic Safety Administration, 94% of auto accidents in the US result from human error. That alone shows that there’s a need to find alternatives to human-driven cars to enhance safety.
  • Besides, people are getting busier than before, with many doing more than a single job to sustain their lifestyles. That means lesser free time to run other important errands. The best thing about self-driven cars is that they will increase the amount of free time people have by moving from one place to another without human beings.

Thus, self-driven cars contribute a lot to how much produce companies manage. Besides making it easier to move, they also impact a person’s quality of life, making it more bearable.

Also, many self-driven cars are comfortable and make it easier for passengers to do their personal work while enjoying long drives, almost like creating car offices.

Is the Rise of Self-Driving Cars an Investment Opportunity?

 

tesla-cars

Business-minded people could be wondering what the rise in self-driving cars means for them. It is common to hear people ask whether it is worth investing in these cars. And since investing in such cars requires a big budget, it is worth taking your time to get the facts right before deciding.

A 2016 Business Insider report projected that there would be over 10 million driverless cars on roads by 2020. More projections show that this growth will not only directly relate to these cars. 

In short, the impact of self-driven cars isn’t limited to car manufacturers only. There are lots of other professionals who’re set to benefit from this technological advancement. For instance, tech professionals should consider this a suitable opportunity to find employment and practice what they love.

Thus, the rise of driverless cars is an opportunity to invest. But then, you should not limit yourself. Casting your net wider can help you make more wise decisions. That means everyone has an opportunity to benefit from this emerging tech, and the best thing is that it will impact even those who won’t directly interact with it.

What Do Self-Driving Vehicles Mean for Car Companies?

Car companies are another lot that could be greatly interested in self-driven vehicles. The question that arises here is on what the introduction of this tech means to car companies globally. Many car companies are quick to jump on the bandwagon of self-driving cars. 

Soon, Kia dealerships will be filled with vehicles that are autonomous in their driving. They are planning to have a complete fleet by 2030. It is rumored that Kia is in its testing phase now. This is also the story for other car manufacturers such as Tesla, BMW, etc.

A situation where car companies do not have to employ drivers may attract more investors. With the technology fitted in these cars, it will be easier to request a ride from any location. The car will arrive by itself, pick up the customer and drop them at their destination without human assistance.

The ridesharing industry will be one of the biggest beneficiaries once self-driving vehicles become popular. Many companies will grow their businesses and achieve consistent profitability, making self-driving cars great investments that will shape the future of car companies.

Could Driverless Vehicles Completely Replace Regular Cars?

Now the big question is whether these cars will completely replace human-driven cars. The reason this question keeps coming up is that more people lose their lives on roads every year. Finding solutions that will reduce these numbers will be great and driverless cars look like a possible solution.

Although safety could be a reason to shift to self-driven cars, it will still not be implemented soon. There’re major technological strides that the industry needs to make before reaching such a point. Thus, human-driven cars are not about to go extinct unless the self-driven car manufacturers do better in some aspects.

One of the ways to ensure that human-driven cars exist on the roads is to use legislation. A few significant strides in enhancing safety in these cars may push governments to think about improving security. But then, there’s more research to do and prototyping to ensure that these cars are safer before phasing out the conventional models.

The complete shift to autonomous cars is also facing a lot of criticism. For instance, many people argue that technology isn’t trusted as it has had glitches and total breakdowns in other sectors. 

In the transport sector, a simple glitch could cost lives and lead to huge financial losses. Thus, the question of whether driverless cars can replace regular cars is difficult to answer. 

However, there’s light at the end only if the industry that can make major strides both safety and convenience wise.

Conclusion

As you can see, driverless cars are a true way for the future. Everyone desires roads that are free from accidents and that are safe for all users. The invention of self-driven cars is timely and has already shown great efforts in reducing accidents. 

However, it hasn’t convinced experts and the rest of the people that it can guarantee safety. But then, its development looks promising, and the wait won’t be too long. Several factors will determine how quickly the world gets there.

For instance, the acceptance of this technology will be critical. The faster people embrace this technology the easier the stakeholders will implement it including manufacturers and car companies. Soon, you will be moving in a fully computerized car that moves without any human assistance.

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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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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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