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11 Biggest Mistakes Job Candidates Make On Social Media – ReadWrite

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Milosz Krasinski


While social media can help you land a dream job, many mistakes can lead to a failed job interview, but there’s one mistake that employers see all the time: you. Of course, you may think it doesn’t matter what you post on social media. Still, research shows that 70% of hiring managers will Google potential candidates and check their profiles before scheduling an interview.

Even if they don’t find anything incriminating, your posts might give them enough reason not to hire or promote you. No wonder they’re looking — remote recruitment is getting more and more popular.

Mistakes Job Candidates Make On Social Media

Avoiding mistakes job candidates make on social media sounds like common sense. For example, never post anything personal about yourself on the internet, especially when it comes to drinking alcohol or using drugs. But we’re living in a world where people often break these rules without thinking twice because they’re feeling invincible behind their keyboards and phones.

The problem is that many job seekers are making these same mistakes, and it’s becoming harder to find a new job when employers know everything about you.

1. Posting too much personal information on social media

Among the 10 biggest mistakes job candidates make, posting too much personal information is one of the biggest. People who apply for jobs nowadays might be required to provide their social media accounts upon application or even during interviews.

That way, employers can evaluate how effective the person may be in dealing with other people while working in their company. Aside from this, Facebook photos can also give false impressions about your personality. Online search histories could demonstrate careless behavior while using other devices.

Employers are indeed scrutinizing potential employees’ social feeds more closely these days — but take heart. Having some levity and a sense of humor can go a long way toward making you seem like an energetic, fun-loving person. Try posing as your own fictitious character or having some non-work friends pose as you — that way, you can share out-of-context sound bites and silly photos.

2. Photos with alcohol or drug use in them

Photos with alcohol or drug use in them can have a negative impact on a job interview. Employers will most likely think that the candidate is participating in illegal activities, and the lifestyle of the company you work for might not be a good fit for you.

If an employer has offered a position to the candidate, they may not accept it because of these pictures. In addition, once an employer hires someone, they have to go through background checks and screenings before they hire the person.

If there are photos online of you doing drugs or drinking alcohol, it will be hard to get a job. Even if there aren’t any pictures of you doing such things on social media, an employer might be able to search your name and find what they view as “questionable” material.

It is best to avoid having pictures of yourself with alcohol or drug use because it will make you look bad to prospective employers. It can also make an employer think you are a heavy user of substances, even if you’re not.

3. Anything that could be interviewers can take negatively like profanity or uncensored nudity

Posting anything that interviewers take negatively, like profanity or uncensored nudity, can hurt your job interview because the company might not approve it. Sometimes this might happen because the company doesn’t want to bring that kind of attention to themselves. So if you are currently looking for a job, keep these things in mind before posting any content on social media.

It’s important to realize that employers are looking for any negative content when looking at your social media page. A company might not want something like profanity or uncensored nudity on its own site. If you post it on your social media page, it might cause the company to reject you before they even interview you. It’s always best to avoid anything that can hurt your chances of securing a job interview.

4. Not posting anything at all on social media during the job search process

Posting things on social media during the process of looking for a job is generally seen as an excellent way to demonstrate your online presence. However, it’s important to realize that employers are looking for any negative content when looking at your profile and messages.

This means anything you post can actually hurt your chances of getting interviewed. Even if not posting anything at all might seem like the safer choice. You need to find the middle ground to avoid one of the most popular mistakes job candidates make.

Simple: Put nothing but safe replies or likes on Facebook and LinkedIn messages, just in case.

Pretty soon, you start thinking twice before sending another message. There’s always a chance that potential bosses who may be watching out for information could be taken negatively. Whether it’s about themselves, their company, or their personal beliefs doesn’t really matter. It becomes an even bigger challenge for Twitter, where every post is a direct statement. It’s tough to not put your personality into what you are Tweeting about. Following the rules becomes a lot harder than just signing off for good.

5. Over-posting about your new job once you get it

It’s important not to post too much about your new job after you get it. This can hurt your chances of keeping the job if the employer reviews what they find on your social media page. One way to avoid this is by waiting for at least six months before posting about your new job. Posting too many times in a short amount of time may make you seem to brag or don’t care about the position.

The worst thing you can do, though, is posting about the job even before the company has guaranteed you a spot.

They can see it as unprofessional, and you never know what will happen once they do the background check. Plus, if other group members also applied for the position, they might not feel too happy about their chances of getting it when they see your constant postings on social media sites.

It’s important to realize that no matter how much you enjoy your new job, it’s not worth risking if you aren’t 100% sure that you can keep it.

Your social media profile should be treated with the same sort of professionalism as a resume. You want to give off a certain impression. Sharing too much detail could do the opposite-so; make sure you’re always careful when posting about work.

6. Hiding behind a professional profile picture while still using an alias online

Many people choose to use a fake social media profile picture when they are on social media for their job search. The reason is simple — they want to avoid mistakes job candidates make. The problem is that this can hurt your career by giving off the impression that you are hiding.

Employers will often Google an applicant’s name, including their alias, before considering them for the position.

Your online persona must remain consistent with who you are in the professional realm. For example, an employer might check up on you to see how well you represent their company. Choosing a profile picture for your social media sites that is professional, typically smiling, and preferably contains no one else in the shot can help avoid unnecessary questions. It’s also best not to use an alias or nickname when posting comments or writing posts.

7. Uploading photos of your children to Facebook without considering how they might affect future employers

Being a parent is a special experience, and children absolutely deserve to be shown off to their family and friends. However, an employer who sees those photos might not see you as someone they want on staff because those images may seem like you’re unable to focus on your job.

The last thing a company needs is a distracted employee during work hours with responsibilities for the safety of others.

8. Oversharing about your personal life

It’s one thing to schedule an interview with someone you already know. But there’s no reason to overshare on social media with strangers before the interview. And if your connections know what you’re up to without even meeting them, why not make a stronger first impression instead of seeming like a braggart?

For example, maybe you want them to understand that you’re looking for a new position in your industry. So you upload some pictures of yourself doing things related to your desired career field. And then keep adding more images as the years go by. That way, when they finally see your face for the first time at that job interview, it’ll feel like they already know you because they’ve seen the evolution of who you are as a professional.

9. Making jokes about current events

One of the biggest mistakes job candidates make is to show disdain for the company and its industry. Making jokes about current events and throwing shade at other people in your industry can hurt your chances of getting a job offer.

This mistake is a little different from the others because it has a lot of components, but here are a few tips:

– Don’t make jokes about anything topical

– Avoid criticizing anyone on social media who is an important influencer for your desired company

-Never make fun of anything that could be perceived as a stereotype.

10. Complaining constantly on social media about work, bosses, and co-workers

Complaining about work on social media can be a huge mistake. If you’re constantly rambling about how much work sucks, then bosses and co-workers are going to take note of that attitude. They could then decide that they don’t want someone working for them who has an unhappy disposition.

Some people are also more sensitive to seeing negative comments about their work environment than others. By being negative about your work or workplace on social media. You might run the risk of other employees getting offended or feeling bad for pointing out flaws in their own company.

11. Liking too many questionable political sites on Facebook

If you like too many questionable political sites or pages on Facebook, then this could hurt your chances of getting the job interview. The employer may think that you’re not conservative (or liberal) enough for their company. This can also lead to some applicants feeling like they will have to censor themselves to get the job.

Conclusion

The 10 biggest mistakes job candidates make on social media can vary, but there are a few things that an employer will never let slide. Posting too much personal information or anything that could be taken negatively, like profanity or uncensored nudity, is sure to lose you the opportunity for a company’s position.

It’s important to consider how your posts affect others and any consequences they may have before posting them online. Hiding behind a professional profile picture and using an alias is also not advised. Employers will want to know who they are hiring, which could make the process more difficult than necessary.

Another example is complaining about work or bosses on social media. Employers will notice and may not want to hire you because they don’t think you’ll match their company culture.

You also have to consider how posting anything could affect other employees. This includes political views that might offend someone else’s personal beliefs. It can sometimes feel like a chore to refrain from self-promotion when looking for a new position, but it can distract from the interview process.  It might, indeed, be best not to post at all while searching for jobs, especially for the corporate ones.

Image Credit: anna tarazevich; pexels; thank you!

Milosz Krasinski

Managing Director at Chilli Fruit Web Consulting boutique London based digital PR agency. Co-Founder at Sigma Digital Oxford. International SEO consultant, speaker. Sometimes blogging at miloszkrasinski.com

Politics

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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