Data analytics is the lifeblood of modern business. Every large organization has a CIO, who appreciates the need to invest in big data solutions.
There are a lot of companies that offer solutions for data-driven businesses. One of the most popular is Garnter.
Gartner is a Reliable Company for Businesses that Need Big Data Solutions
Gartner is an information technology research and advisory company. It provides research on the world’s major hardware, software, and services vendors to help tech buyers make more informed decisions about how they can best spend their budgets. It is a great company for organizations that need big data solutions.
Gartner has been around for over three decades and it’s grown into a powerhouse of information and resource for data-driven businesses. With Gartner, your company will have access to a wealth of knowledge that can help you better evaluate software vendors and find out how to refine your own software products based on customer feedback.
Why Gartner?
Gartner uses its research to provide clients with guidance on the market and to help identify and evaluate business opportunities. Its research is rigorous, objective, and often changes over time. Gartner has been involved in the IT business for decades, so they have the credentials to conduct in-depth research and offer valuable insights.
Gartner produces independent research about how enterprise buyers interact with vendors. Gartner works with several major software providers, and this lets it get early access to sales data.
Gartner uses this data to develop reports about how buyers perceive top vendors. These insights will help give you a better chance of winning the business of customers that are actively using software in your industry. It will also give you reviews from peers to help your team choose their own software vendors for internal purposes.
Why do CIOs use Gartner to evaluate software and improve their data analytics strategy?
Scalability and insight. Before you even launch a new product, you want to make sure that the partner you’ve selected can scale with your company’s growth. For example, you want to make sure your cybersecurity is able to scale concurrently with your number of employees, as well as the number of endpoints in your infrastructure. In that case, you’d be interested in Gartner endpoint protection.
Gartner has the knowledge and insights to assess companies across all industries to help you determine the right partner to help your business grow. They also have a lot of big data resources that can help businesses operate more efficiently.
You’ll have access to a resource that can help you assess the companies you choose for your enterprise needs and help you create a plan to meet your compliance requirements.
To get the right type of software for their business’s data analytics needs
As a CIO or IT director, you’ll be better off making a choice based on your company’s needs and not on vendor hype. It may take a lot of research on different vendors to understand the product and its intended use, but you’ll be able to make an informed decision that will benefit your business.
CIOs are charged with efficiently running a business and making sure that the right technology is at the right place, at the right time, and that it helps grow it. You also need to protect your business data – for example, in a Gartner survey, 98% of brands are negligent when it comes to their big data security.
To find out how popular a software is in the market
Understanding what software products your customers like and need is a huge indicator of your position in the market. However, it isn’t easy to gather feedback from your customers.
You need to rely on a trusted source to provide insights into what your customers think about your product and what you should improve. Gartner provides this type of research to help companies gather insight into their products.
With the Gartner Peer Insights product, you can see which software partners are trusted by your customers. Gartner also provides a wide range of free tools and research to help you understand what software your customers prefer and why they might do so.
To get an overview of the vendor’s company and what they’re all about
The annual Gartner Symposium/ITxpo is one of the largest events in the IT industry. It takes place in Las Vegas every year during the second week of November.
The symposium features CIOs and industry luminaries offering their perspectives and the latest industry trends. The Gartner Peer Insights reviews website also hosts a Gartner Magic Quadrant report covering all major software providers.
All of these benefits mean that Gartner is a widely trusted source for technology professionals and business leaders. As the world’s leading independent technology research and advisory company, Gartner provides unbiased and actionable information that helps companies across all sectors innovate and prosper.
Gartner is a Great Company for Businesses That Need Big Data Solutions
Big data has become very important for businesses that are trying to grow in 2021. Gartner is one of the companies that offers resources for data-driven businesses.
In today’s modern era, AI and IoT are technologies poised to impact every part of the industry and society radically. Because most businesses devote their primary efforts to developing their brand, software applications, or network, new technologies are apt to transform how they operate. In addition, as companies attempt to draw better significance from the huge datasets gathered by linked devices, the potential of AI is accelerating the wider implementation of IoT.
While businesses invest heavily in digitization, they are also incorporating AI throughout their IoT initiative, evaluating prospective future IoT ventures, and looking for ways to get greater value from current IoT deployments. Moreover, with the help of an AI development company, businesses can avoid unforeseen downtime, increase operational productivity, develop new services and products, and boost risk control.
Benefits of AI and IoT in Businesses
AI and IoT, when combined, are incredibly powerful technological forces. The advantages of IoT and AI could be combined to reap the full benefits of both. The following elucidates the same:
l Improved Protective Measures.
Security and protection are the most important aspects for a business, given the recent growth in data thefts and loss of valuable data. The AI-powered IoT platform protects your confidential info and prevents third parties from accessing it. Various firms are using machine-to-machine interaction to identify inbound attacks and send out automatic answers to cybercriminals. In the financial sector, for instance, unlawful activity in ATMs is detected by IoT sensors and quickly reported to law authorities.
l Successful Execution of Business Analysis.
There must be a perfect equilibrium between demand and supply. AI assists in enhancing stock control and relieving inventory strain by allowing you to understand when you need to refill in advance. This is helpful for merchants that sometimes accumulate too many things only to discover later that they can’t sell them all. Moreover, this demonstrates how much more precise it is than traditional approaches. There are IoT solutions that can assist them in collecting data and performing analytics for inventory management.
l Improved Risk Management.
We have covered how AI and IoT platforms aid with security. When it concerns risk control, which involves dealing with economic damage, employee safety, and cyber attacks, the two handle issues with ease and respond quickly so that similar circumstances do not occur. For instance, Toshiba, a Japanese computer device and service provider, uses data obtained from portable tech combined with AI to ensure safety at work.
l Automated Production Efficiency.
IoT implementation simplifies your organization and aids in creating precise forecasts, both of which are critical for increasing corporate efficiency. Moreover, investing in the IoT is critical in today’s world since the technology may help you identify repetitive tasks and those that take up so much time. A good example of this is Google’s drop in data center cooling costs, which they may achieve through the use of AI and IoT. You, too, can discover which of your organizational operations require some fine-tuning in order to avoid sacrificing productivity.
What Do You Need to Get a Deeper Understanding of the Internet of Things (IoT)?
Sensors and devices in the Internet of Things gather data that could be used to provide meaningful insights. Most businesses, on the other hand, struggle to grasp how IoT may help them grow their businesses, and they need assistance when initiating IoT operations. IoT operations have the ability to lower company costs, improve processes, and provide specific company insights. Also, IoT consulting services might help companies reach their full potential.
These IoT consultants assist firms in understanding IoT technology and developing a plan to improve operations and products. There are also some consulting firms that provide development services for IoT solutions.
Take Away
In the business sector, AI and IoT technologies guarantee the success of data-driven approaches. They are seen as liberating forces because they allow businesses to handle jobs that only humans can handle. In addition, businesses that execute on data insights are becoming pioneers and industry experts at the end of the day. Likewise, entrepreneurs that seek expert assistance are more likely to thrive. They may be able to optimize their operations, cut costs, and gain insight into their business.
Artificial Intelligence (AI) is changing the commercial real estate industry by making everything more efficient, accessible, and transparent. Many companies are using AI to analyze their data and make better business decisions. AI can also be used to provide valuable insights into leasing trends, maximize vacancy rates, and forecast future demand.
Below are the myriad of ways AI will change things for commercial real estate in the next few years.
How can AI be used in commercial real estate?
Real estate is a service business and selling houses or apartments is only one part of the puzzle. Making sure potential clients know how they can utilize real estate services is just as important. Businesses are investing in technology to connect clients with the right person at the right time. Today, in many cases, AI can be used to make it happen.
For example, during the real estate transaction process, artificial intelligence can help agents and brokers access properties quickly, providing a personalized approach to each potential tenant. It can also provide pre-sales that are customized to each client’s needs.
The same process applies to offering services. For example, property managers can use artificial intelligence to automate the scheduling process, improving customer service. It can also help make processes less expensive and less time-consuming.
Making better decisions with AI
The human brain works in a similar way to how a computer works. The thing that differentiates the brain is that it has more data storage capacity, and it uses a vast amount of data to make the right decisions.
AI uses this information to analyze business data and to provide timely, relevant and accurate results for specific industries and businesses. It can also be used to analyze big data and to predict trends. For example, it can identify that a particular industry is likely to experience a shortage.
But to use AI, you have to set it up correctly. You also have to realize that AI is not a magic bullet that will make you a genius. To fully leverage AI, you have to identify specific problems that it will solve, train it to learn, and apply it to your real estate business.
Increase revenues with AI
Using AI, businesses can real estate agency revenue through real estate lead generation. It can be used in marketing campaigns, web scraping for listings, and other types of lead generation.
Commercial real estate agents also benefit from the use of AI. The technology can alert the right people to the right properties, making it easier to connect with prospective tenants. AI can also analyze transaction data to estimate return on investment (ROI). In other words, it can be used to help predict how much revenue a business can expect to earn and at what cost.
There are also areas where the use of AI can help clients and other organizations make better business decisions. For example, it can analyze social media activity and activity to provide a better picture of the buying public. It can also help with providing analytics that can predict future market movements.
Decreasing vacancies with AI
Leasing and holding vacant properties is a highly competitive field. When companies are looking to buy or lease a property, the agent representing them is getting a lot of interest. This creates high demand for spaces, which drives up prices. The result is a great deal of competition for any available spaces. The space must be vacant for the same amount of time as other competing spaces.
Without AI, companies are at a big disadvantage in this market. It can provide insights to help understand market trends and the ability of an area to withstand strong competition. It can also learn to better price new spaces to encourage tenants to move in.
AI can also help tenants navigate a complex process that requires dealing with a lot of small issues. It can reduce time spent on filing forms and setting up the structure of the lease. AI can also reduce headaches and errors when it comes to dealing with tenants.
Data is processed to generate information, which can be later used for creating better business strategies and increasing the company’s competitive edge. Data mining and knowledge go hand in hand, providing insightful information to create applications that can make predictions, identify patterns, and, last but not least, facilitate decision-making. Working with massive structured and unstructured data sets can turn out to be complicated. Nonetheless, it’s important to treat the entire process as valuable work rather than treating it as a nightmare.
It’s obvious that you’ll want to use big data, but it’s not so obvious how you’re going to work with it. Knowing some techniques in advance can lighten the road. So, let’s have a close look at some of the best strategies to work with large data sets.
Preserve information: Keep your raw data raw
Raw data is better than cooked data because it’s accessible for further processing and analysis. There’s not much value in holding on to raw data without putting it to good use, yet as the cost of storage continues to decrease, organizations find it useful to collect raw data for additional processing. If it’s not done right away, then later. The raw data can be fed into a database or data warehouse. An analyst can examine the data using business intelligence tools to derive useful information.
To arrange your data and keep it raw, you need to:
Make sure the data pipeline is simple so you can easily move data from point A to point B. Save a copy before editing to prevent changes to the original data. Summarize and sample your data at query time.
Attention needs to be paid to the fact that it’s not always possible to archive or analyze all the data that’s being produced. Nonetheless, you must invest time and effort into extracting the best possible value from the data sets. Everyone has to manage raw data at one point or another; yet, not everybody stores it in a way that’s useful for further analysis or comparison to other data sets.
It’s much easier to work with graphs
As data sets become bigger, it becomes harder to visualize information. It’s recommended to use lots and lots of graphs. Draw a chart highlighting each endpoint in your data. If you’re working with thousands or tens of thousands of nodes, this can be very useful. You can finally understand what you’re looking at and what the data is saying. The graphs can either be single, grouped, or stacked. The format can be classified by size, but you can choose to organize data horizontally or vertically/by column.
Data visualization enables you to:
Make sense of the distributional characteristics of variablesEasily identify data entry issuesChoose suitable variables for data analysisAssess the outcome of predictive models Communicate the results to those interested
It doesn’t matter if you use graphs or charts, you need to get better at data visualization. Data visualization, empowered by the computer, is one of the most practical tools you have at your disposal. You’re familiar with the saying “A picture is worth a thousand words”. Just so you know, a picture isn’t a substitute for a thousand words.
Store and organize the data in a scalable way
Data storage is a key component of any successful organization. The way in which you store data impacts ease of access, use, not to mention security. Choosing the right data storage model for your requirements is paramount. There are countless implementations to choose from, including SQL and NoSQL databases. Speaking of which. A NoSQl database can use documents for the storage and retrieval of data. The central concept is the idea of a document. Documents encompass and encode data (or information) in a standard format. A document is susceptible to change.
The documents can be in PDF format. You won’t have any problems storing document files. You don’t necessarily need to download Abode Acrobat to manipulate PDF files. There are reliable alternatives such as PDFChef that make it possible to edit and protect PDF documents. getting back on topic, documents can encode data in various formats, such as Word, XML, JSON, and BSON. Data type description and the value for the concerned description can be found in the document. The structure of the documents that make up the database can be similar or present certain differences. It’s not necessary to alter the schema to add to the database.
Manage workflow data and remove unnecessarily complex processes
The workflow is basically a sequence of tasks that processes a set of data. It’s necessary to have a structured workflow to explore new opportunities. The good news is that you don’t have to do things manually. These days, you have software to help you handle the process. To put it simply, you can manage both documents and processes. You can identify redundant tasks, map out the workflow, automate the process, and discover areas for improvement. Even leading organizations can end up with unorganized documents, disconnected tasks, and so on.
The most important features any workflow management system should have are:
Integration with other cloud appsWYSIWYG form designerSLA status indicatorsNotifications when and where you need them
The best part about data workflow management is that you can take a task and develop a custom solution to bring clarity to the entire team on what needs to be done and, most importantly, how.
We have one last thing that we’d like to add. It’s a good idea to record metadata. Standardizing metadata helps ensure that information assets continue to meet the desired needs for the long term. The metadata describes exactly how observations were collected, formatted, and organized. The specialized set of information preserves and provides access to electronic records. No matter what your strategy is, try to think about the future. It might be necessary one day to integrate your data with that of other departments. Metadata makes the task a lot easier. It improves the data quality and system effectiveness.
Machine Learning is a branch of Artificial Intelligence that works by giving computers the ability to learn without being explicitly programmed. Machine Learning is already being used in many aspects of our life, from recommending movies or music based on past preferences to giving doctors’ advice on relevant treatments for their patients.
As technology advances, machine learning will have more opportunities to help businesses engage with their customers and improve the overall customer experience. Machine learning programs can be trained on large sets of data, such as customer reviews and feedback, to identify patterns and make predictions about future behaviors.
In this article we will explore how you can use machine learning to potentially change and encourage reviews, which we know affects consumer purchasing decisions.
If we train a machine learning program on this data set, it will be able to predict whether or not someone is likely to leave positive reviews. If the program predicts that someone is likely to leave a positive review, we can send them an email encouraging them to do so.
This is only one way you could use machine learning for this purpose. You can analyze different aspects of a purchase order and make changes based on what will be best for your company’s bottom line.
How to Set Up Machine Learning for Review-Related Goals
In order to set up a machine learning program, you need three things:
A large sample of data from successful customers who followed through with the goal you want your new machine learning program to achieve;The right analytical tools that can work with this type of data; andAccess to the right data scientists who understand these analytical tools and are able to train your program.
If you don’t have all three things, consider partnering with a marketing firm that specializes in machine learning like broadly.com to help you through the process.
Machine Learning for Review Research
There are many ways that machine learning can be used for research related to reviews. Machine learning can be used to identify trends in the data, such as what types of reviews get more clicks on a website.
In addition, machine learning is increasingly being used for “sentiment analysis” – determining what the sentiment of a review is (positive, negative, or neutral).
If you have some data that’s already been manually labeled with sentiment, machine learning is a fast and accurate way to do additional research and identify larger trends.
Machine Learning and Sentiment Analysis
The two most common ways to use an off-the-shelf machine learning system for sentiment analysis are: Training your own model from scratch; or accessing an API call on a third-party sentiment analysis system. Both of these options will work if you have the data required to train an accurate model.
Training your own model is faster, but it can take time and resources that smaller companies might not have. Using a third-party API is fast, but the results are often lower quality than they would be with a custom-trained model.
Using Machine Learning to Improve Reviews
Once you have a machine learning program set up, there are several ways you can use it to improve the reviews your business gets.
Here are three simple examples of how to use machine learning in everyday life:
Remove or reward positive reviews;Featurize negative reviews into marketing assets; andIdentify which customer segments are most likely to leave negative reviews.
Removing or Rewarding Positive Reviews
One simple way machine learning can be used in everyday life is by rewarding positive reviews. If we train our program on the existing data set, we can predict which reviews are most likely to be positive. Then, for example, we could automatically add a thank-you note to the review and offer the reviewer a discount code for their next purchase.
This increases the likelihood of them leaving another positive review about this product in their next transaction… and it helps build trust with customers who may be the reviewers of the future.
Turning Negative Reviews into Marketing Assets
Another way machine learning can be used is by turning negative reviews into marketing assets. If your program analyzes a product review and determines that it’s largely positive, you could automatically turn this review into a blog post to help bring more traffic to your website. This process works well for a few reasons: It’s a high-quality review that can be transformed into valuable content; and only one or two sentences would need to be changed, keeping the rest of the wording exactly as it is.
Identifying Which Customer Segments Are Most Likely to Leave Negative Reviews
The last way machine learning can be used in everyday life is by identifying which customer segments are most likely to leave negative reviews. If you have enough data, you could train your program on the existing positive and negative reviews to figure out if there’s an algorithm that can accurately predict whether a review will be positive or negative based on who they are (such as what products they’ve purchased in the past, what customer segment they belong to, and so on).
If you were able to identify this algorithm, you could automatically pre-emptively reach out to the customers who are most likely to leave a negative review as soon as they purchase an item. This would allow your business to either steer them away from your products or provide extra assistance before any problems arise.
Conclusion
Machine learning and sentiment analysis is a fast and accurate way to do additional research and identify larger trends. This is one of the many ways that they are improving our lives. Whether you’re selling a product online or running a brick-and-mortar business, these behavioral neuroscience principles will work for you. They’ll help drive more visitors into your marketing funnel and convert casual visits into sales.
While it might sound ironic that high-tech fields such as data science still require you to submit a resume, even the most cursory look over a list of job openings should prove this to be true. Managers and HR department staffers in even the most technically-oriented companies are actually on the lookout for candidates with an impressive resume. This might encourage some applicants to stretch the truth.
If you’re applying for a job in the IT or data science spaces, then you don’t want to do this. Consider adding statistics and other data points about your previous job, even if you don’t have exact numbers. This will give you the freedom to promote yourself as an expert without ever suggesting something that’s untrue.
Why Truth is So Important in Data Science Resumes
Human resources directors might not be directly related to the technology resources of most of the firms they represent, but that doesn’t mean they can’t leverage the same tools everyone else in said firms rely on. That means they’ll spot a lie, so you want to go over everything in your resume to make sure you were telling the truth.
That being said, the ideal length of a resume is somewhere around 475-600 words, so you shouldn’t feel like you’re under any pressure to put filler in there. Short and sweet rules the day, especially when you think about how many resumes some reviewers have to look through a day. While apps are indeed used to automatically process resumes and prevent any personal biases from leaking into the review process, the final decisions are still made by humans at even the leading data science firms.
As a result, you want to be concise as well as truthful. Perhaps most importantly, you want to present your information in a way that makes sense for the type of job you’re applying for.
Formatting Your Resume in the Preferred Way
Relatively few companies use automated resume application processes for professional employees. These are generally deployed only at facilities that expect a large degree of turnover, such as a retail operation. Anyone who is applying for a professional-level career at a place that manages large amounts of information is more than likely going to need to submit an old-fashioned resume.
A growing number of companies are actually asking potential hires to submit their resumes as traditional flat text files, which should be editable with almost any piece of software. Considering that computer professionals tend to be evenly split when it comes to GNU/Linux, Windows and Mac platform face-offs, this is pretty good news. Creating a resume that meets these requirements could be as easy as firing up your favorite notepad application and typing away.
Other organizations are starting to rely more heavily on the Roman Executive format, which first rose to prominence in the legal profession within the last decade or two. This may require you to be more careful when it comes to what sections you include, but you still won’t want to get carried away with employment history.
In fact, some observers feel that industry insiders now care more about skills than they do about the places that you may have worked in the past.
Balancing History with the Present
Chances are that you’ve worked several jobs in the past that don’t have anything to do with the IT or data science fields. While you might want to include these if they’re particularly impressive, you don’t need to write a complete employment history that stretches all the way back to your summer job during high school. Instead, you might want to narrow things down to jobs that show you have a background in data analytics.
You’ll certainly want to include any relevant educational experience or professional certifications that you might have, but more employers these days are probably looking for proof that you know how to do the kind of job that you’re applying for. Consider adding a dedicated technical skills section that spells out all of your various competencies.
While you don’t want to brag or sound shamelessly self-promotional, you should be honest about any area that you personally feel you excel in. Potential employers will expect that you’re able to do anything you put on this list, so be honest, but don’t be afraid to show yourself in the best possible light.
Once you’re done, read things over a few times before you finally submit your resume. Taking a little extra time to proofread can help you catch errors you might not otherwise have found.
Security specialists and networking engineers are starting to warn users about a dangerous new type of social engineering attack that impacts those who use online meeting applications. Attackers that gain control of a compromised email or messenger account have been able to generate large numbers of forged calendar invites, which they can then send out to a large number of people all at once. As soon as someone who clicks on these invites enters their information, a remote machine makes note of it and sends it back to the bad actors who were behind the attack in the first place.
Arguably, more people use online meeting services now than ever, which makes these sorts of attacks particularly concerning. According to one study, Zoom alone logs over 3.3 trillion minutes of usage every year and that number is likely to grow. Due to the privacy features of some apps like Slack and Discord, it can be difficult to know how many people are on a server unless you’re in it yourself. That means some users may be exposed to these kinds of social engineering attacks without many of their coworkers even being aware of the fact.
It’s this concern in particular that has many people in the cybersecurity industry on the edge of their seats.
Leveraging Calendar Invites as an Attack Vector
Highly skilled website imitators have been able to fashion realistic-looking calendar invite pages that appear like they come from any of the popular services that are being targeted by these attackers. Users of online meeting services generally have full sized contact lists, meaning that someone who gained control of one of these would be free to send out a huge number of invites nearly instantly. These invites would, at least theoretically, look like they came from a legitimate source.
Depending on how realistic they looked, they could encourage outside users to give up their email credentials or surrender contact details related to file sharing services attached to their meeting application. Those who work from home might be sharing information via something like DropBox or OneDrive. If that’s the case, then they may have few qualms about sharing their login information with an otherwise legitimate looking login screen. Once they enter it, however, a bad actor could suddenly start uploading infected material that they could share with other people.
To make matters worse, comparatively little work has been done to secure most digital calendar apps. A great deal of development in the space has been to solve other unrelated issues that had plagued them since they first started to become popular. Developers who’ve already felt beleaguered over these problems are now being asked to address potential security leaks.
Patching Calendar Apps Against Social Engineering Attacks
Engineers are finding it difficult to patch these leaks, due in no small part to the fact that they’re usually based more around a perceived level of trust than actual technical limitations. In many cases, the attacks themselves are limited to someone spoofing someone else’s account and then asking for account details in an otherwise open chatroom. As long as people don’t ever put their contact details into a form that is run by someone other than the people providing a service, these attacks are unlikely to take place. Technical staff are primarily working to educate consumers about the danger of sharing credentials.
Individual users who are looking to do something in the meantime might want to explore other options. Few commercial-grade security products are robust enough to deal with these new threats, so they may wish to look at Lifelock alternatives for identity theft protection, which may offer features not seen in more popular applications. These can help users to mitigate the damage done if they’ve found themselves in a spot after providing contact information to a fraudulent recipient.
Some may be surprised that people continue to fall afoul of these kinds of schemes in 2021, especially considering how much attention has been paid to them in the past. Bad actors have a new trick up their sleeves that’s making it easier to fool even jaded netizens, however.
Convincing People to Surrender their Details
Once a person has had an account stolen for whatever reason, bad actors could potentially do a fairly good job of acting as them. By using special Unicode characters, they could make a fraudulent URL look like it actually came from the servers of the app in question, which could ensure that even the most seasoned of users may give up their credentials. Security specialists have begun looking at ways to tighten up the Unicode text protocols to reduce the risk of this happening.
In the meantime, users are asked to be vigilant and ask themselves whether someone would actually need a password or other information after they’re already logged into an app.
You may not have thought about creative professionals having a strong foundation in data analytics. Artists are known for their creative insights, rather than their analytical or scientific competencies. However, the world has changed, which means that a background in big data and other types of technology is equally important.
Data-driven businesses need to keep this in mind when looking for creative professionals. You want to make sure that any creative agency you hire understands the relevance of big data and knows how to incorporate it into their designs.
Data-Savvy Creative Agencies Are Necessary in 2021
Being able to market your brand effectively is vital in today’s business landscape, if you hope to remain successful and be able to stay ahead of your competitors. Without unique advertising campaigns, customers will find it increasingly difficult to find a brand they resonate with, which could cost you. However, not all businesses have the time or resource to focus on what it takes to make a great marketing campaign.
Nor do they have the wide array of skills needed to offer end-to-end marketing services. In this day and age, that entails using big data strategically. You need to make sure your creative agency has as strong of a foundation in big data technology as with their artistic abilities.
For those businesses that need additional support when it comes to marketing and branding, hiring a creative agency with a background in data analytics and AI development could be a very effective solution.
Before you can appreciate the importance of big data, you need to get an understanding of the role of creative agencies in general.
What is a creative agency?
Typically, a creative agency can support brands with a wide variety of services across both print and digital media, by providing concepts, illustrations, videos and written content. The creative team can be made up of many different roles, including designers, copywriters and art directors.
Hiring a creative agency can give you access to resource for assets that you can use in campaigns to promote your brand and its services or products. An agency will be able to get a good understanding of your market and its trends, in order to come up with the right campaigns to drive results, whether it’s a new video for social media or a refresh of your website.
Benefits of Using a Creative Agency with Data Analytics Competencies
Now that you understand the general industry, you may want to learn more about the benefits of using an agency with a detailed understanding of big data. So, what value can you get from hiring a creative agency with a background in data analytics and why should you consider it? There are actually a lot of benefits of using big data in marketing.
Offering a fresh perspective
Being able to solve business challenges and come up with new ideas can sometimes be difficult if you are right at the heart of the business, day-in and day-out. It can cloud your judgement and often make it difficult to come up with original ideas, not to mention whether it will work.
A creative agency is a great extension of your team but with an objective outlook of your brand, giving them an opportunity to identify any opportunities or ideas that you might otherwise have missed. In this way, you have access to an additional perspective that could make all the difference to your next creative campaign.
Creative firms that understand big data will be able to come up with more informed observations. They will be able to identify trends more easily by using sophisticated predictive analytics models predicated on big data. This will help them develop a data-driven marketing model that aligns with your needs.
Industry knowledge
A creative agency has been creating great campaigns for years, while your team might be new to the concept. This means your creative support can add real value to your brand, as they’ll bring with them a strong background in creating assets that really help the business to reach its goals.
A creative agency has the industry knowledge to represent your brand’s message through carefully constructed imagery, video, written content and more. By combining a creative flair with a solid strategy, your business can benefit from an improved brand image and become more engaging with your audience.
Creative agencies with a strong commitment to big data will be able to adapt more readily, because they will use the latest tools and employ more effective strategies for training their teams.
Cost-effective
In most cases, hiring a creative agency with a focus on data analytics can be more cost-effective for many brands instead of hiring an internal team. As demand for new creative assets fluctuates throughout the year, you can lean on an agency more or less to meet your requirements. What’s more, you can save time and resource by not having to train up new staff, and instead leave to the professionals who can hit the ground running. This can make a huge difference to a busy business who are looking to keep costs low without compromising on quality.
Saving you time
Hiring a creative agency to help you with branding, marketing and advertising means that you can continue to focus on strategy. Having a professional take care of creative assets can prevent you from being spread too thin. This can be important because if you’re business is continuing to grow, you’ll want your staff focused on turning those leads into new customers and being able to deal with customer enquiries effectively.
They will be even more efficient with their time if they use the right big data strategy. New forms of big data technology will help them automate a lot of functions that would normally require more active employee engagement.
Unstructured data represents one of today’s most significant business challenges. Unlike defined data – the sort of information you’d find in spreadsheets or clearly broken down survey responses – unstructured data may be textual, video, or audio, and its production is on the rise.
In fact, by some estimates, as much as 80-90% of new data is unstructured, and that presents real challenges from a data management standpoint.
How can businesses make meaning out of unstructured data and generally manage all of the information they’re generating in a productive way? It’s a challenging process, but it is possible with the right array of tools.
Centralizing Information
The first step towards making use of unstructured data is to find a way to centralize this information, and this is a top priority for many businesses today. In fact, 56% of businesses say that getting their unstructured data into the cloud is a top priority.
Migration is not quite management, but it’s the first step towards organizing and evaluating unstructured data, and that’s important. The biggest barrier to this task, however, is a lack of IT capacity and budget – but businesses that can successfully budget for these expenses may find profitable insights in that data, more than making up for those initial costs.
Find Out What’s Inside
The next aspect to addressing unstructured data is extracting more concrete information from it, and this may be the most complicated element. How do you quantify unstructured data? There are a number of approaches, but AI is one of the most important tools because by using innovations like natural language processing (NLP), the system can identify frequently used terms, evaluate tone, and much more.
Once businesses can see “inside” their unstructured data, there’s a lot to explore. Better data management can drive business growth, and can help provide direction for a number of operational changes. For example, businesses have used information derived from unstructured data to improve safety, advance healthcare outcomes, and automate business facilities based on worker insights – but let’s take a closer look.
One type of unstructured data common in the healthcare industry is imaging, whether that’s a CT, MRI, or an X-ray. Radiologists can be slow in evaluating non-emergent imaging, and the human eye is only so sensitive. When imaging is paired with AI technology, however, facilities can turn over imaging results more quickly and with greater accuracy.
Another area of healthcare that is well-positioned to benefit from better unstructured data management and analysis is drug development. This may seem like a fairly structured area, but given what we know about unstructured data production, pharmaceutical research generates a lot more of it than you might realize. The industry also struggles with prioritizing and organizing such data, which has a negative impact on collaboration and product development in the pharmaceutical industry.
Collaboration Considerations
As noted regarding the pharmaceutical industry, collaboration is a critical business function and the inability to collaborate can be a serious hindrance to progress – and this is true across industries. With unstructured data, though, you can’t just send along a data set and, depending on how you aim to evaluate or manipulate the information at hand, businesses often need to be able to exchange, comment on, and modify large files across teams and locations. So, what’s the best way to tackle this task?
If you need to send large files fast while continuing to collaborate, one option to consider is using a cloud-based file storage system. These platforms essentially prevent the need to regularly transfer files by storing them in a shared repository featuring access and privacy controls and ensuring users always have the most recent iteration of the document when collaborating on a document.
Storing and transferring files may not fundamentally reveal much about the content of unstructured data, but as we’ve seen, just centralizing these files remains a serious issue for many businesses. Until basic tasks like migration cease to be top priorities for major corporations, we can’t underestimate the importance of centralized transfer and storage tools.
Experts continue to raise concerns about how and if businesses are making use of unstructured data, but in addition to actively sharing that information, until access to machine learning protocols is more widespread, the ability to effectively utilize this information and derive insights will be compromised. While large retail and finance organizations currently lead in this regard, small businesses are in a challenging position because NLP and other AI tools can still be expensive, especially when they need to be modified to suit unique industry terms or functions. Without the ability to access the insights contained in unstructured data, however, businesses can’t compete in the modern marketplace.
It takes a variety of tools to properly navigate unstructured data, and what tools you’ll need depend heavily on the types of unstructured data that dominate a business’s approach.
At the end of the day, though, the most important thing is that your company makes an active effort to engage the information in your unstructured data while it’s still relevant. So much of what you need to know is in there, just waiting to be unpacked.
Kubernetes has become the leading container orchestration platform to manage containerized data-rich environments at any scale. It has vastly simplified container deployment and management yet with the added complexity of managing clusters. Therefore, we need to understand the underlying architecture as well as common issues in order to speed up the Kubernetes troubleshooting process if you want to create big data applications.
Common Types of Kubernetes Issues that Data Developers Must Recognize
Due to the complexity of Kubernetes, it can take considerable time and resources to troubleshoot issues in even relatively small K8s clusters such as dev or testing environments, especially if they have massive amounts of data sets. However, we can simplify this process by categorizing different issue types and narrowing down the troubleshooting scope for data-driven developers.
Big Data Application Issues
The first thing we need to ensure when troubleshooting Kubernetes is that the application is working as expected. This can be a challenge for applications that are highly dependent on complex data sets. Otherwise, we will be unnecessarily troubleshooting an issue that is not related to Kubernetes. This can be done by testing container functionality either in a holistic data-driven test environment or even in a local environment. This is one of the most important things to be aware of as a data-driven software developer.
Network Connectivity Issues
Connectivity issues can be categorized as internal connectivity issues that occur within the cluster and external connectivity issues that block access to the cluster or third-party data sets.
External Network Connectivity
Kubernetes clusters can be configured with external load balancers and firewalls to further enhance and complement internal Kubernetes configurations. In these instances, we need to check if any issues or configurations of these external networking resources block the Kubernetes cluster.
Internal Network Connectivity
Kubernetes network will consist of the following connectivity types;
Container to containerPod to PodPod to serviceService to external sources
Each connectivity type can contribute to a multitude of errors. The ideal approach for troubleshooting these network connectivity issues is to start from external connectivity options like k8s ingress and then move to services like load balancers, node ports, then pods, and finally, container connectivity. With each step, we reduce the troubleshooting scope by simply checking if communication between the correct resources happens.
Pod Configuration Issues
One of the most common issues faced by Kubernetes admins is Pod configuration issues. These issues can range from faulty deployment configurations, container image corruptions to issues in the node itself. However, they are also the simplest to diagnose as Kubernetes provides clear error messages indicating the root cause of an issue. Furthermore, we can easily figure out issues related to pods by looking at the Pod status or using describe or log commands.
Node Related Issues
These issues occur when the worker nodes are experiencing issues. Various node-related issues such as network issues, hardware failures, data loss or failures in provisioning issues in a node can directly impact pod creation and management, which will in turn directly impact the application. Kubernetes has built-in redundancy, which enables the application to recover even if some nodes fail. However, these node failures can cause performance degradations, and the best way to avoid such scenarios is to try to mitigate node failures. The Node Problem Detector provides an ideal solution to monitor the health of the k8s nodes and ensure maximum data stability.
Cluster Service/Component Issues
Kubernetes consists of multiple components that are required for smooth cluster operations. Especially different types of controllers from replication controllers, scaling controllers to resource controllers like node controller, services controller, etc. Issues in these components can even lead to complete cluster failures as they deal with the core functionality of Kubernetes. Thus, high availability architecture is used in most production environments to mitigate such errors. It enables the cluster to function normally even if one Kubernetes control plane fails.
Infrastructure Issues
Infrastructure-related issues are only applicable for self-managed Kubernetes clusters as the service provider is responsible for all the infrastructure in managed solutions. These issues are highly dependent on the underlying hardware and software configurations, requiring considerable time and effort to pinpoint and remedy them. As these infrastructure issues are outside the scope of Kubernetes, users will need external monitoring and diagnostic tools and services to help troubleshoot them.
Troubleshooting Kubernetes Issues
Kubernetes comes with an excellent toolset for monitoring, logging, and debugging. Therefore, it is essential to utilize all these inbuilt tools and services when troubleshooting Kubernetes clusters.
The kubectl itself provides a simple yet powerful command set to troubleshoot Kubernetes resources easily. These commands include the describe command to obtain information on Pods/Nodes, exec command to gain shell access to a container, etc. Resource metric pipeline that uses the Metrics API (kubectl top) is also a great tool for getting a broader understanding of the behavior of K8s resources quickly.
Another factor is logs. Logs are sometimes underappreciated yet critical to troubleshooting as they can provide a complete view of the issues and events that led to a particular issue. K8s logging architecture provides a robust platform to enable cluster-level logging utilizing third-party logging backends to store and analyze data.
On top of that, we can use third-party tools and services to complement inbuilt tools and simplify Kubernetes troubleshooting even further. Crash-Diagnostics and KubeEye are examples of some open-source external k8s troubleshooting tools.
Troubleshooting is a Vital Process for Data Application Developers Using Kubernetes
Kubernetes troubleshooting is itself a complex subject matter. However, as Kubernetes users, we must be able to troubleshoot K8s without being overwhelmed by this complexity. The best approach is to minimize the troubleshooting scope and use all the tools and services at your disposal to identify and resolve Kubernetes issues easily.
