Artificial intelligence (AI) and machine learning (ML) have become omnipresent in our personal lives. The same is true in the business world. Data integration can help businesses take their AI applications to the next level.
In many respects, AI is now a mature business technology. Consequently, it’s no longer the sole province of pioneering companies. Not only have advanced analytics powered by ML become popular. Chatbots responding to customer requests through a Natural Language Processing (NLP) AI are also on the rise.
However, many are leaving AI’s full potential untapped.
More than anything, AI needs data. ML algorithms ingest large data sets for training. AI is at its best when it extracts actionable information from a myriad of data points that no human could conceivably make sense of.
Businesses collect ever-larger amounts of data. Yet, most fail to harness all the data they have for their AI applications.
In 2021, a massive section of business operations happens in apps. These are now usually cloud services by Software as a Service (SaaS) companies. There are accounting platforms like FreshBooks. Customer Relationship Management (CRM) systems like Salesforce. Marketing powerhouses like HubSpot.
According to recent statistics, the average business uses 73 different apps. Furthermore, employees conduct over 80% of their work in the cloud. In 2020, the pandemic additionally magnified these trends.
Enormous amounts of data live on each of these platforms. With integrations, these services can take advantage of each other’s data. This enables countless business processes to take place in the cloud, from marketing to financial reporting.
Yet few businesses take advantage of all the possible integrations to unify their data and put it at the disposal of AI and ML.
Having a data integration strategy matters. Here’s why, what the challenges are, and how your business can benefit.
Why Strive for Data Integration? To Create and Harness Synergy.
In business, AI has three central tasks: process automation, generating-depth analyses, and engaging customers and team members.
For all this, data from connected business platforms is invaluable.
Let’s take a core office technology as an example: business phone services. Modern business telephony is cloud-based, thanks to Voice over Internet Protocol (VoIP) technology. For businesses, this has countless advantages, some more obvious than others. VoIP has enabled multiple communication channels, from video conferencing to live chat.
But VoIP business telephony also enables AI applications.
For example, NLP AIs can transcribe voice interactions in real-time. This can be applied to every single conversation between company representatives and customers. Then, ML algorithms can run advanced analytics such as sentiment analysis on the transcripts. The result? Company representatives can receive an instant evaluation of their performance. They can go over the conversation and learn.
But by integrating with other platforms, this kind of AI-generated data becomes even more useful. Connect your cloud business phone system with a CRM, and you can add every transcript to a customer profile. This immediately boosts possibilities for personalization. In turn, this can increase your chances of conversion or retention.
You can also integrate the data into a personnel management platform. For instance, sentiment analyses of a company representative’s conversations might be frequently negative. You’ll see this in their performance evaluation. Then, you can tackle the problem. Targeted training could, for example, showcase positive examples from other transcripts.
Or imagine you’re using sentiment analysis algorithms to sift through online reviews. By itself, this will give you valuable insights into customer attitudes and expectations. But integrating it with data from your digital marketing platform, and you can go much further. AI can then correlate email campaigns or social media strategies with sentiment trends in online reviews.
Getting Crucial Information in Real-Time
Speed is another core advantage of integrating data to boost AI performance. Integration increases the amount of information you can access. What’s more, access becomes easier and much faster.
Let’s go back to the business telephony example. The transcripts from customer calls are in your CRM. Imagine someone calling with a problem they were having with one of your products. A few days later, the same customer calls again. Now, the AI assistant in your business phone service pulls the relevant CRM records. Before your representative even picks up, they get a full run-down of this customer’s case: How they’ve described their problem. Personal information from demographics to billing history. And even what conversational strategies in the last call worked best.
All this saves your customer from having to give a long-form recap of their problem. For one, this reduces frustration on their part. For another, it also allows your representative to address them in a personalized manner. Overall, the exchange will be more efficient and satisfactory for both parties. Your business saves resources and increases customer satisfaction at the same time. All thanks to AI integration.
Generate Positive Feedback Loops
A final significant advantage of business app data integration for AI is positive feedback loops.
This means that by integrating platforms, you generate new data. On this basis, you can refine your strategies and grow your platforms. Leading to more data.
Take chatbots as an example. These days, countless companies use them in their online stores and on their websites. They are powered by NLP AIs. Their usefulness hinges on whether they can provide relevant information to customers.
For this, context is crucial. Customer behavior can provide that context. By supplying chatbot AIs with behavioral analytics, you can increase their usefulness.
For this, you can feed customer data from e-commerce and website analytics platforms and feed it to an AI. Have the AI find patterns and use these patterns to train your chatbot. On this basis, it will give suggestions and make recommendations to customers. Then, track the further behavior of these customers. It will give you even more data – to refine your chatbot.
This feedback training between chatbot AIs and e-commerce analytics helps increase communication efficiency. And seriously boost customer experience and thus retention and sales.
The Challenges of Data Integration
As the examples above make clear, data integration entails countless benefits for AI. But it also poses several challenges in its implementation.
First, businesses looking to integrate data face the hurdle of siloed technologies. Data silos are inaccessible for ML algorithm training. Nor can trained models analyze their contents for decision-making.
Second, the readability of data is a central issue. Many platforms have idiosyncratic data structures and lack metadata or context. They might even save files in proprietary formats.
Third, the trustworthiness of data is often in question. A study in the Harvard Business Review has highlighted this. In it, almost 50% of companies admit to data integrity problems. Issues with false, inconsistent, or stale data are frequent. And even more troubling, more than half of enterprise data fails to provide value altogether, due to siloing.
There are several avenues businesses can take to avoid these pitfalls and overcome these challenges to effectively harness integrated data for AI. The first of these is data mapping to establish a single source of truth.
Data Mapping and Single Source of Truth
Strictly speaking, data mapping is a subfield of data integration. It describes the process of connecting a data snippet in one system to a corresponding item in another. For example: Mapping the “name” field in a CRM record to the “caller ID” field in a business phone system.
The goal of this is to unify data while maintaining its integrity.
Crucially, this also helps businesses create single sources of truth. This refers to a single master record, which can boast high accuracy and sees frequent updates. Other systems will derive their data from here.
This doesn’t mean that all data needs to be stored in a single record. But for each item, only one ultimate source should exist. Especially highly sensitive data – like names, addresses, or social security numbers – should be treated like this. This makes it much easier to control and audit how this information is handled while still harnessing its value.
The good news is that modern AI-based cataloging tools can maintain such master records. They can parse information from all your platforms and deduce credible metadata. They can also automate data cleaning and highlight inconsistencies. This is a crucial first step in dealing with data complexity.
Dealing with Data Complexity
As mentioned above, data readability is a major hurdle to overcome. At its core, that means dealing with data complexity.
More and more raw data is available. This raw data is available in countless different formats. These formats range from social media posts over e-commerce statistics to server logs.
In addition, context is lacking for a lot of this information, especially natural language data. Take a thread of social media posts, which may include abbreviations and typos. Many statements might not make sense without the accompanying emoji, GIFs, or videos.
Plus, all this information is scattered across an ever-growing number of platforms. And these platforms, in turn, may have dozens, if not hundreds, of third-party integrations.
Using AI to Overcome Data Integration Challenges
The good news is that AI systems are becoming better and better at handling these challenges. This means you can use AI to generate input data for AI.
First off, AI can conduct data mapping automatically with precision and speed. In addition, the mapping mechanism can weed out issues such as duplicates and missing values. This helps maintain data integrity and speeds up data unification.
Furthermore, AI makes it possible to automate regular exports of data siloed in various places to central data hubs. It can infer data structures and parse uncommon formats. And crucially, it can propagate and consolidate data across systems, drawing from a single source of truth.
Recently, AI and ML algorithms have become adept at parsing situational context. If the schema structure for the incoming data is unknown or only partially known, they will either judge on a pattern basis, or parse the content to decipher a schema.
AI-Assisted Integration or Self-Service APIs?
One crucial question of data integration remains, though. How exactly can a business go about it?
It’s easy to say that AI can do this and that, without specifying exactly how it does so.
Generally, there are three ways in which businesses can approach data integration.
First, enterprise-scale businesses probably have the resources to hire and task data analysts with establishing a system of integration and analysis. They have the required knowledge in coding and data mining to set up a system that is custom-tailored to their employers’ specific needs. With the right access to the administrative space of various business apps, little should stand in the way of success.
Second, you can opt for self-service analytics platforms. A growing number of platforms aims to make analytics easily available for non-specialists. Many of these come in the form of cloud APIs. Their aim is to democratize AI analytics. Following this philosophy, some platforms even offer chatbot-like features. These help you create an integration procedure conversationally.
And finally, you can – at the very least – harness the integrations that are already at your disposal. Many major business apps already offer native integrations with each other. Even more, they are compatible with bridges such as Zapier. In many cases, taking the time to explore the options of integrations that you have in your existing app ecosystem can already pay off big time. Prioritize platforms that offer AI analytics and automation capabilities, and then proceed to map out the nexus of available integrations.
Data integration is a challenge. But it’s a challenge worth tackling to reap the rewards.
Harnessing all the data at your business’ disposal is vital in 2021. It will help you understand your customers better, meet their needs, and increase your sales.
The best way to go about this is to use AI to improve AI through data integration.
You can either manually explore your existing integration options, find an easy-to-handle cloud API, or approach the task with the help of a professional analyst.
Putting the required systems and automated processes in place will take some time and effort. At the end of the day, it will be worth it.
You will be able to boost the quality of your algorithms, establish positive feedback loops, and gain insights at an unprecedented level. With decisions based on the insights, you’ll be able to steer your business through the times to come.
Application Dependencies: Are They Holding Back Software Innovation?
In software development, a dependency is a piece of software that another piece of software relies on in order to function. An application’s dependencies are the external components that the application needs in order to work. These can include libraries, frameworks, and other software packages that the application uses.
For example, if an application is written in Python and uses the Django web framework, then Django would be a dependency of the application. In order to run the application, the Django library would need to be installed on the system.
Managing Dependencies in Software Development
Managing dependencies is an important part of software development, as it helps to ensure that an application has all the necessary components it needs to run correctly. This can be especially important when deploying an application to a new environment, as all of the dependencies will need to be installed and configured correctly in order for the application to work.
While dependencies make it possible to develop applications faster and add advanced functionality quickly without having to build them from scratch, they also introduce serious risks that can bring software development projects to a halt. I’ll describe what types of dependencies commonly exist in software projects and how they impact software innovation.
Types of Software Dependencies
Functional dependencies are components or resources that are necessary for an application to function. They result from the tasks that enable businesses to achieve their desired outcomes. It is important to identify and map these dependencies to detect and address issues, removing redundant dependencies.
Sometimes, you might need an unavailable dependency, such as one still in development. Mocking is a technique used in software development to create simulated versions of components or dependencies for testing purposes. Mocking allows developers to test the behavior of a piece of code in isolation by replacing its dependencies with mock objects that mimic the behavior of the real dependencies.
Developmental dependencies, on the other hand, are dependencies that are only needed during the development and testing phase of a software application. These dependencies might include tools for testing, debugging, or building the application and are not necessary for the application to run in production.
For example, an application may depend on a testing framework such as JUnit or PyTest during development in order to run automated tests. Still, the testing framework would not be required when the application is deployed.
Similarly, an application may depend on a build tool such as Gradle or Maven during development in order to compile and package the code, but the build tool would not be needed when the application is running.
Non-Functional and Operational
Non-functional dependencies are dependencies that relate to the overall behavior and performance of a software application rather than its specific functionalities. Examples of non-functional dependencies might include dependencies on particular hardware or software configurations or dependencies on system-level services such as networking or security.
Operational requirements can be hidden in functional requirements, so they only become apparent later in the project. To resolve an issue with such dependencies, it is important to establish policies, identify the root cause of the issue, and determine the appropriate resolution.
Dangers and Risks of Application Dependencies
There are several risks associated with application dependencies, and the danger increases with greater reliance on external software components:
- Security vulnerabilities: Dependencies can contain bugs or flaws that can be exploited by attackers. It is important to keep dependencies up-to-date and to regularly check for and install any available security patches.
- Compatibility issues: Dependencies are not always compatible with the version of the software they are being used with, or they might rely on other dependencies that are not present.
- License issues: Dependencies may be subject to different licenses, and using them in an application may create legal issues if the terms of the license are not followed. It is important to carefully review the licenses of any dependencies before using them in an application.
- Maintenance and updates: These are essential in order to stay current and secure. If a dependency is no longer maintained or supported, it can become a liability for the application that relies on it.
- Complexity: An application with a large number of dependencies can be more complex to maintain and deploy, as all of the dependencies will need to be managed and kept up-to-date. This can result in something called dependency hell.
How Application Dependencies Impact Software Projects
Application dependencies are an important aspect of software development that can significantly impact the success of a software project. Understanding and managing these dependencies is crucial for building and maintaining high-quality software systems that are resilient, scalable, and easy to maintain:
Application dependencies can make the software more complex to build and maintain.
For example, if a software system has many dependencies on external libraries or frameworks, it may require more coordination between different teams and systems to ensure that these dependencies are properly managed. This can increase the time and effort required to deliver the project, and it can make it more difficult to make changes to the system in the future.
Application dependencies can affect software stability and reliability
If a change is made to a dependent component of the system, it can have unintended consequences on other parts of the system that rely on that component. This can make it more difficult to ensure that new features or changes are safe and reliable, and it can increase the risk of regressions or other issues.
Application dependencies can impact the scalability and performance of a software system
If dependencies are not properly managed or optimized, they can become bottlenecks or points of failure that limit the ability of the system to handle high levels of traffic or workload. This can impact the usability and reliability of the system, and it can reduce the value that it delivers to stakeholders.
Therefore, it is important for software teams to carefully understand and manage application dependencies in order to ensure that their projects are successful. This may require using tools and practices such as dependency mapping, automated testing, and continuous monitoring to track and manage dependencies effectively.
In conclusion, application dependencies can have a significant impact on software development projects. While dependencies can provide valuable functionality and save developers time and effort, they can also increase the complexity of a project, introduce security vulnerabilities, impact performance, and cause conflicts.
It’s important for developers to carefully consider the dependencies that their applications rely on and to try to minimize the number of dependencies as much as possible in order to keep the project simple and maintainable.
By keeping your project simple and maintainable — developers can help ensure that their applications are able to take advantage of the latest innovations and technologies and are able to adapt and evolve over time.
Featured Image Credit: Photo by Mikhail Nilov; Pexels; Thank you!
Leveraging Social Media To Grow Your Career In 2023
Employees are ready to change their jobs, with nearly half of American workers planning to look for a new job in the coming six months. According to a new Robert Half report, which surveyed 2,500 professionals, around 46% of them said they plan on making a career or job change in the first half of the year.
Job-hopping has become a workplace trend among young working professionals in the post-pandemic labor market. A recent Gallup study found that 60% of surveyed millennials – ages 27 to 40 years – are more likely to look for different opportunities this year. The percentage of non-millennials workers looking to switch jobs is roughly 15% lower.
A majority of Generation Z candidates have also claimed that they are likely to make a job change this year. In a 2022 Lever Great Resignation report, around 65% of Gen Z professionals said that they are likely to leave their job by the end of the year. Moreover, 13% of them are twice as likely to quit their jobs in the next month.
Job-hopping has become almost synonymous in the post-COVID workforce, and younger professionals are fueling this trend by leaving unfulfilling roles and moving on to greener pastures.
Yet, with so many professionals changing jobs, or looking to switch careers, even against the backdrop of a looming recession, many of them have geared themselves towards social media as a way to build a professional brand and market themselves to potential employers.
Using Social Media For Career Growth
Keeping your social media professional can be a hard ball to juggle. In a 2020 Harris Poll survey, around 70% of employers said that every company should screen candidates’ social media throughout the hiring process. Additionally, the majority of employers – 78% – believe that all their current employees should adhere to a work–appropriate social media profile.
Employees should care about what they share and post on social media. Although the debate over whether social media screening during the hiring process is ethical is still ongoing, candidates willing to leverage social media to develop or boost their careers will need to set up a social media strategy that can help them land the job they want.
Much of our digital identity is pinned to our social media accounts, and a lot of what we share, like and the people we interact with via these channels can speak a great deal of the types of person we are outside of the workplace.
Aside from employees using these platforms to grow their network, or search for possible job opportunities, employers and recruiters are using it to look for any possible red or green flags that you might bring to the workplace.
Social media has moved beyond its traditional form, and today it’s become a digital ecosystem that helps to connect like-minded professionals and their potential employers.
How To Use Social Media To Boost Career Opportunities
Searching for a job is more than browsing through recruitment websites and job listings on LinkedIn or Google. The internet, and social media is a vast place, with near-endless possibilities, and when it comes to growing your career through social media, you will need to know a few things first.
Have A Social Strategy
It might sound strange at first, but having a social media strategy will help you come in contact with the right people faster. Your social media strategy should include building an online identity that reflects your professional and personal side.
You can use different platforms for different connections or networks, it’s all about how you present yourself through your brand. Think of the type of content you share regularly, does it reflect who you are as a professional? How often do you post, or reply to comments and messages? Are there any areas where you can improve or update the information to help you grow your network of contacts?
Write some questions down to get you started, and start working on building an online identity that can get noticed by like-minded individuals in the same industry.
Network With Industry Professionals
Nowadays it’s easier than ever before to reach out to a company or recruiter through their social media, and the same goes for connecting with professionals working in the same industry.
Instead of using social media to only share insightful content, or engage with your friends, try to grow your professional network. On top of this, it’s important to engage with these people as well, even if it’s simply exchanging a few words now and again.
Be active in your mission to get to know the people that are out there, and spend a bit of time researching their profiles to better understand the type of skills and qualifications these people may have. Networking is one of the best possible ways to move around your industry without putting in much effort.
Grow Your Skills
Looking at other people’s social media profiles, whether it’s Twitter or Indeed.com, or even Instagram will give a better idea of the type of skills you might need to develop to help grow and make the next big career jump.
Often professionals will share their skills, and what they’re experts in at the top of their social media accounts, this way it is easier for recruiters to know who the person is, and for like-minded professionals to engage with them.
If you compare the skills of several professionals already working in the field you’re interested in, you will get a better idea of where you might need to upskill yourself by completing some courses or doing a bit of reading.
Advertise Your Expertise
When we say advertise, we don’t necessarily mean flashy and colorful digital adverts that you’d hope will get the attention of your potential employer.
Instead try and convey your expertise through the type of content you can share such as blog posts, news articles, industry research, or even projects you’ve worked on. Additionally, you can also share your job title and relevant experience in the bio section of your profile.
The better you are at showing people your expertise in a professional, yet unpretentious way, the faster your feed will fill up with similar content and other experienced individuals.
Update Your Profiles
This is relevant to almost every social media profile you have, regardless of what you use it for. People often neglect social media platforms they don’t use anymore, and while it can be tedious to spend so much time updating photos or replying to messages, decide on a couple of platforms you’d like to use and stick to them.
Make sure that the platforms you end up using have a recent photo, and that all other relevant personal information has been updated such as your job title, industry experience, and your current city. You don’t need to do this every week, only when needed, or when you’ve changed jobs or moved.
The better you curate your social media, the easier it will be for employers and recruiters to notice you as you actively begin to network.
Social media can be a professional tool, despite it receiving so much negative clout in recent years. Although it’s hard to determine whether possible employers or recruiters will screen your social media accounts before or during the hiring process, it’s best to always keep a well-groomed online identity – especially if you’re looking to make progress in your career.
Make well-informed decisions, and think about the type of content you’re sharing. Remember to engage with like-minded professionals, and have conversations online through the information you share with your followers.
The better you are at curating one or two social media platforms for career purposes, the quicker you’ll be able to expand your network, and grow your professional skills. Don’t think too much about it, try and have a balance as much as possible, as this will help you to enjoy your social media experience while maintaining a professional, yet fun digital identity.
Published First on ValueWalk. Read Here.
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Have You Heard of These 6 Amazing Ways to Use AI in Construction?
Artificial intelligence might have started as the fictional villain of sci-fi stories, but it’s quickly becoming indispensable in many industries. The construction industry is one among many beginning to adopt this new technology. How can companies and contractors start employing AI in construction? How could this industry 4.0 technology change the industry in the coming years?
1. Programming an Extra Set of Eyes
Drone cameras have already become invaluable for contractors, especially for site surveying and inspections. While they can help keep inspectors and workers safe, they still require a live person behind the camera and the controls. In the future, users could train AI to see, analyze and understand the images they’re observing, reducing or eliminating the need for a human operator or drone pilot.
Human inspectors will still be necessary, but if programmers can teach an extra set of AI-powered eyes to recognize when something is incorrect or missing, it could help streamline these processes.
As a bonus, these AI eyes could potentially recognize or identify errors and safety issues human inspectors might overlook. Observational AI systems rely on pattern recognition and spend most of their time observing hundreds of thousands of images to ensure they can correctly identify their targets.
They don’t experience the problem of familiarity. It’s like editing a piece of text. The more often the author reads it, the more likely they are to overlook errors rather than fix them. AI observational systems don’t have that problem, making them more efficient for safety applications.
2. Turning Data into Actionable Insights
Construction might be one of the slowest industries to adopt new technologies. Nonetheless, that hasn’t stopped the slow introduction of smart building. Incorporating devices into a construction project generates massive amounts of data. Without an AI or machine learning system, that information languishes in digital limbo. Skilled analysts may be able to make heads or tails of it, but putting it to use requires additional tools.
In construction, AI can sort through massive amounts of data, find patterns and deliver actionable insights that can improve productivity and worksite efficiency. It can use equipment maintenance data to create a better care schedule, preventing costly downtime due to equipment failure. With enough information, it can even predict when these maintenance cycles should occur based on past data.
3. Adopting Virtual Assistants
Alexa or Siri might seem like something users only need after their shift ends, but these virtual assistants and many others can help improve outcomes. Digital helpers designed for construction applications can manage communication, bolster inter-team coordination, schedule and track appointments, and more. Advanced assistants can access data generated by the above technology and help with budgeting and estimation.
Modern helpers may take time to customize to a company’s specific needs, but their benefits vastly outweigh the time investment. Utilizing natural language processing (NLP) can make these virtual tools even more powerful. NLP allows users to speak to their virtual assistants as they would to the person next to them.
4. Incorporating AI Into Wearable Technologies
Like virtual assistants, wearable technology might not seem like it has much of a place in the construction industry, but that couldn’t be further from the truth. Fitbits and Apple Watches might not offer much, but they are far from the only wearable devices available in the construction industry.
Monitoring an employee’s heart rate and other vital statistics can indicate when they might be in distress. Fatigue monitors can prevent on-the-job accidents by alerting supervisors when someone is operating heavy equipment while not adequately rested. Impact sensors can detect when someone experiences a fall. Connecting these wearable sensors to a centralized AI in construction sites allows the system to monitor employees in real-time and send alerts as needed.
5. Procuring and Supply Chains
Supply chains across industries took a significant hit during the COVID-19 pandemic. The price of some construction supplies skyrocketed, and the supply chains for other materials slowed to a crawl — if they were still available. Incorporating AI into procurement and supply chain systems can help improve efficiency and reduce the chance an upset like the pandemic could derail these supply chains again.
There are applications for AI and related technologies throughout the supply chain, from manufacturing and harvesting to those last-mile deliveries. Sensors can collect information about everything from location to distance traveled. They can monitor temperature and humidity for materials requiring more climate control, making it easier to protect all necessary supplies while in transit. Pairing these sensors with an AI system can make sense of data while generating actionable insights.
6. Integrating Robotics and Automation
Contrary to the story popular media tries to spin, robots aren’t appearing in workplaces intending to steal jobs. Instead, they could help improve workplace efficiency and reduce on-the-job injuries by completing mundane, repetitive, or dangerous tasks. Introducing robotics and automation can lower the potential for stress injuries since construction workers are most frequently diagnosed with this type of ailment.
When it comes to dangerous tasks, AI-powered robotics or automation are ideal. These applications can include everything from cleaning tanks or operating in low-oxygen environments to completing tasks in situations that would otherwise be unsafe for human life. While it is currently possible to use these robots manually via remote control, adding AI to the mix would free up workers for more critical or complex tasks employers can’t automate.
Looking to the Future of AI in Construction
There are so many amazing applications for AI construction that it’s easy to forget it’s still novel technology. It will take some time before the industry is ready to adopt this technology and capitalize on all its benefits. AI could make all the difference for companies looking to differentiate themselves in this competitive field.
Featured Image Credit: Provided by the Author; Photo by Sam Moghadam Khamseh; Unsplash; Thank you!