#springbord systems

[springbord]

Springbord is a leading global information service provider that offers services in the realms of data, content, and design space.

Combining a wide range of leading Internet-based capabilities, smart tools and technology, and years of experience and domain expertise, we provide compelling, business-friendly, and client-centric outsourcing services. Springbord helps its clients drastically reduce costs, increase productivity, and drive business growth.

Recognizing the individual needs of each business, we provide tailor-made services to deliver solutions that are specifically relevant to your organization. Our enthusiasm for our work drives us to continuously strive for enhanced business results on your behalf. With an unwavering commitment to putting our clients first and our extensive knowledge of data management, we are able to guarantee exceptional delivery standards for every project we undertake.

What We Offer, 1. Comprehensive Solutions For Your Commercial Real Estate Needs (Services: Lease Abstraction, Lease Administration, CAM Reconciliation, CAM Audit) 2. Solution For Your Data Management Needs. (Services: Data Labeling and Data Annotation) 3. Customized E-Commerce Solutions To Keep Your Customers Coming Back (Services: Catalog Management)

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Reducing Human Error in Data Labeling Tasks

Data labeling is one of the most crucial tasks in machine learning and artificial intelligence (AI) projects. It involves annotating raw data with meaningful tags that machines can use to learn and make predictions. The accuracy of these labels directly impacts the performance of machine learning models. As the reliance on AI grows across industries such as healthcare, e-commerce, autonomous driving, and more, the need for high-quality labeled data has never been more important.

However, manual data labeling, while essential, is prone to human error. Inaccurate or inconsistent labels can degrade the performance of AI models and result in incorrect predictions or decisions. Reducing human error in data labeling tasks is therefore critical to the success of machine learning projects. In this article, we will explore how human error impacts data labeling, and how automation and best practices can help mitigate it.

The Impact of Human Error in Data Labeling

Human error is inevitable in any manual process, and data labeling is no exception. Labeling large datasets is a time-consuming task that requires intense focus and attention to detail. Labelers may become fatigued or distracted, which can lead to mistakes. These errors could be anything from misspelled labels to misinterpretation of data or inconsistent labeling across the dataset. When this happens, machine learning models trained on this flawed data will struggle to learn and make accurate predictions.

Some of the key impacts of human error in data labeling include:

Inconsistent Labeling: Different labelers may interpret data differently, resulting in varying labels for the same data. Inconsistent labeling can confuse machine learning models and make them less reliable.

Labeling Fatigue: Manual data labeling is repetitive, which can lead to fatigue over time. As a result, labelers may overlook critical details or make mistakes.

Bias in Data: Human labelers may inadvertently introduce bias into the data, especially if they have preconceived notions about the data they are labeling. This bias can lead to skewed results and reduce the fairness and accuracy of the model.

Time and Cost Overruns: Mistakes made during manual labeling can lead to rework, delaying project timelines and increasing costs.

To minimize these risks and ensure that data labeling is as accurate and efficient as possible, automation offers a promising solution.

1. Leveraging Automation to Minimize Human Error

Automation can dramatically reduce human error by providing consistency, accuracy, and speed in data labeling tasks. Machine learning algorithms can be used to automatically label data with minimal human intervention, ensuring a higher degree of accuracy across the entire dataset.

Automated data labeling systems can follow predefined rules and criteria, ensuring that every piece of data is labeled in the same way, eliminating the risk of inconsistent labeling. For instance, in image recognition tasks, AI models can automatically detect and classify objects in images, reducing the likelihood of errors due to fatigue or misinterpretation.

Springbord offers advanced automation solutions to help businesses eliminate human error in data labeling tasks. Through Springbord’s data labeling services, organizations can leverage automation to reduce manual workloads and increase the accuracy of their labeled data. Learn more about Springbord’s data labeling services and how they can help you reduce human error by visiting Springbord Data Labeling Services.

2. Consistency with Predefined Rules and Criteria

When human labelers are tasked with labeling large datasets, it’s difficult to maintain uniformity across all labeled data. One labeler might categorize an image as a “dog,” while another might use “canine” or “puppy,” leading to inconsistent results. Inconsistencies like this can confuse machine learning algorithms, as they rely on consistent and standardized labels to make predictions.

Automation solves this problem by applying the same labeling criteria across the entire dataset. AI-driven systems are programmed to apply consistent labels based on predefined rules, ensuring that every piece of data is tagged uniformly. This consistency helps improve the accuracy of machine learning models and reduces the chances of mislabeling due to human interpretation.

Moreover, automated systems can continuously apply and update these rules, adapting to changing data over time without the need for constant oversight.

3. Minimizing Bias with AI-Driven Labeling

Human labelers may unintentionally introduce bias into data, especially when labeling subjective or complex data. For example, when labeling sentiment in text, a labeler may unconsciously label a piece of content as negative due to their own personal biases. Similarly, in image recognition tasks, labelers may be more likely to associate certain objects with specific categories, based on their own experiences and interpretations.

Automated systems, on the other hand, follow strict algorithms and guidelines that help minimize bias. These AI models can be trained to recognize patterns objectively, without being influenced by personal opinions or experiences. When used correctly, automation can help reduce the risk of biased labeling, ensuring that the labeled data is representative of the broader dataset.

For businesses seeking to reduce bias in data labeling, Springbord’s data labeling services incorporate AI-powered automation tools that help ensure unbiased labeling across all types of data. You can learn more about Springbord's approach to data labeling by visiting Springbord Data Labeling Services.

4. Improving Labeling Speed and Reducing Fatigue

Manual data labeling is inherently time-consuming and repetitive, and labelers often suffer from fatigue, especially when working on large datasets. Fatigue can lead to mistakes, such as misclassifying data or overlooking important details. As the data grows, so does the potential for errors.

Automation can reduce the strain on human labelers by performing the majority of the labeling work. This allows labelers to focus on more complex tasks, such as reviewing and correcting labels flagged by the automated system. By automating repetitive tasks, organizations can speed up the data labeling process and ensure that labels are consistently applied without the risks associated with fatigue.

Additionally, automated systems can work 24/7, further improving efficiency and reducing the time required to process large datasets.

5. Active Learning for Continuous Improvement

Even with automation, there may still be cases where human input is required. Active learning, an iterative process in which a model identifies uncertain or ambiguous data points, can be used to continually improve the accuracy of data labeling. In active learning, the system asks for human assistance when it encounters data that it is unsure about, ensuring that only the most complex or unclear data is reviewed manually.

By combining automation and human oversight, businesses can continuously improve the accuracy of labeled data while minimizing the time and effort required from human labelers. This hybrid approach helps build higher-quality datasets over time, reducing the overall error rate in the labeling process.

Springbord integrates active learning into its data labeling workflow, ensuring that automation and human oversight work in tandem to deliver highly accurate labeled data. Learn more about Springbord’s active learning approach by visiting Springbord Data Labeling Services.

6. Utilizing Quality Control Mechanisms

Even in automated systems, it's essential to have mechanisms in place to ensure that errors are identified and corrected. Many automation tools come equipped with built-in quality control features that monitor the consistency and accuracy of labels. These features can flag inconsistencies, duplicate labels, or data points that don't match predefined criteria.

When quality control mechanisms are integrated into the data labeling process, errors can be caught before they make it into the final labeled dataset. This proactive approach ensures that human error is minimized, and data labeling remains consistent and accurate across large datasets.

Conclusion

Reducing human error in data labeling tasks is essential for improving the quality of machine learning models and ensuring accurate predictions. While human labelers will always be an integral part of the data labeling process, automation offers a powerful tool to reduce mistakes, improve consistency, and increase efficiency.

By leveraging AI-driven automation, businesses can scale their data labeling efforts while maintaining high standards of accuracy and reliability. Springbord’s data labeling services offer advanced automation solutions that help organizations reduce human error and ensure the quality of their labeled datasets. To learn more about how Springbord can help you optimize your data labeling processes, visit Springbord Data Labeling Services.

Through the integration of automation, businesses can streamline the data labeling process, mitigate human error, and ultimately improve the performance of their machine learning models.

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5 Tips for Retailers to Make It Easier to Manage their Online Retail Catalogs

Online retail catalog management can be overwhelming for retailers, especially when tracking thousands of products, managing inventory, and updating prices and product descriptions. This blog post will explore five tips retailers can use to streamline their online retail catalog management.

Tip 1: Validate all product-related information

One of the most important things when managing an online retail catalog is to validate all product-related information. This includes product names, descriptions, pricing, and images. Retailers should ensure that all information is accurate and up-to-date to prevent customer confusion and frustration.

Tip 2: Maintain uniformity in displaying product-related information

Keeping the way information about products is displayed consistent is another critical aspect of managing an online retail catalog. This can make it easier for customers to find and compare products and ensure that all information is accurate and up-to-date. Retailers can use a template or set of guidelines for how the data should be presented or a tool like a Product Information Management System (PIMS) to automate the process.

Tip 3: Deploy a Product Information Management System (PIMS)

Using a PIMS is one of the most effective ways to simplify online retail catalog management. A PIMS can help automate many tasks associated with managing an online retail catalog and ensure that all information is accurate and up-to-date. It can also help automate the process of maintaining uniformity in how product-related information is displayed.

Tip 4: Smartly define your website's structure

Ensuring your website is structured to make it easy for customers to navigate and find what they want is the first step in managing your online retail catalog. Retailers should create a clear hierarchy for their products with categories and subcategories that make it easy for customers to find the products they are looking for. Product pages should be easy to navigate, and retailers should include the following:

Clear and concise product descriptions.

High-quality images.

Detailed information about the product.

Tip 5: Make it SEO-friendly

Optimizing your website for search engines is essential to make it easier for customers to find your products. Incorporating keywords into your page titles, meta descriptions, and product descriptions is one SEO best practice that can help boost your site's visibility. Additionally, retailers should ensure that their website is easy to crawl and indexed by search engines by making sure that their site's structure is clear and organized.

Conclusion

By following these tips, retailers can simplify how they manage their online retail catalogs and save time and money. With the help of Springbord, a leading provider of e-commerce solutions, retailers can easily manage their online catalogs and stay on top of their inventory.

Learn more on Springbord's blog

[mattapparently]

Ok. I understand the fluffy appeal of Heffalumps and Woozles...but I NEED to talk about The Hounds. It's one of the best songs The Protomen have made an it's a tremendously slept-on villain song.

Firstly, there's the context, but not within the story. The entirety of the album (Act 2: The Father of Death) can be split into 2 timeframes. The first half of the album is solemn and acoustic, sticking primarily to acoustic guitar, piano, snares, maybe a trumpet if they're feeling feisty. The second half jumps into the future, and while there are still pianos (mainly in Breaking Out and a bit of Here Comes the Arm), most of the other instruments have been swapped out with electric alternatives. The Hounds utilizes electric guitars and bombastic horns...but is on the first half of the album. It's showy and captivating...just like Albert.

The whole song is him going through his plan to frame Thomas for Emily's murder, knowing full well Thomas isn't responsible. He knows his evidence is circumstantial at best, but that's not his aim. It's sewing doubt. If he can make the people certain in his guilt, including Thomas himself, then he can use him as a scapegoat to seize power. He needs to rile people against Thomas, and the best way to do that is by putting on a show. That's what he means during the lines, "If you think that you can run / If you think that you can stand / well, you forget who turned this city on / you forget who plugged this city in!". The entire city is watching this whole ordeal go down, and only Thomas and Albert know the truth about the situation. But Albert isn't speaking the truth...and that's part of the plan.

"When I say he was a monster / When I set fire to his name / It doesn't matter where you hear it from, / whether truth or lies get said. All's the same! / Whatever's on the table plays!"

He knows he's not arguing in good faith, but he's fine with that. He doesn't care about if he's correct. He's just worried about getting Thomas off the streets, so he can protect people and avenge Emily...right?

"What was her name!?"

This is the critical question asked to Albert in the middle of the song. The people ask him in the middle of his rantings what Emily's name was. He frames the whole issue around her murder and how important it is to make Thomas pay. But when asked by the crowd what her name was...

"Doesn't matter. Now listen! / The Good Doctor has to pay!"

He brushes it off. He says her name doesn't matter, because she's the victim in this. It's not about her. It's about tearing a man down and using his persecution to build another up. The song is about sensationalism in crisis and who profits off of it. Albert uses Thomas' trial as a springbord to game the system and take control of the city. It's incredible in action. Albert is vile and cruel...but he puts on a hell of a show.

Villain Song Showdown Bracket B Round 1

The Hounds (Act II: The Father of Death by The Protomen) - Villain: Dr. Albert Wily

Reading the lyrics can help give some more context on the story

Heffalumps and Woozles (Winnie the Pooh) - Villain: Heffalumps and Woozles

Mod comment: The horns in The Hounds are going hard

[agambarta]

Springbord Systems Walkin 2019 | Non-Technical Associates in Chennai Great Opportunity for all Graduates!! Springbord Systems Walkin 2019 is scheduled from 9th May 2019 To 31st May 2019…

[spring-bord-blog]

Real estate companies are increasingly adopting the process of lease abstraction that allows them to summarize essential business, legal and financial terms and conditions. A summary of key lease provisions makes it easy to locate and access information as per business requirement.  However, the volume and complexities of commercial real estate leases can make the process of abstraction a logistical nightmare, making it less effective and prone to human errors.

Lease abstraction requires meticulous, expert and careful approach to ensure you get an accurate lease abstract. While the summary itself can help optimize time and effort, but a single mistake or inaccurate extraction could negatively impact the business. To avoid such risks, most of the real estate companies are now outsourcing lease abstraction to third party service providers. Outsourcing allows you to gain access to professionals at a lower cost and also insulates you against inaccuracies/errors in abstraction. What more? Here are 5 key advantages of outsourcing that are hard to pass up on.

#1 Optimum Resource Utilization Outsourcing lease abstraction to professionals help you free up the time of your in-house executives who can utilize these hours on more important and strategic tasks, exploring other business growth opportunities. Moreover, the third-party service providers provide a tailored summary for all legal, financial and business provisions and send them in a desired format as per client’s requirements. Custom summary reports not just make it easy to read but also seamlessly upload into the company’s financial system or other business applications saving time and effort throughout the process.

#2 More Cost-effective Lease abstraction demands skill, efficiency and commitment of extensible man-hours. Managing such complex and time-consuming tasks in-house can be expensive, including the time and cost needed to source and train the staff. Also, it may not guarantee timeliness and accuracy despite the investment and training. On the other hand, outsourcing lease abstraction projects to professionals allows business to share the work-load and extend their in-house lease administration capabilities, without going through the challenge of recruitment and training and creating processes and controls. In addition, it offers flexibility and scalability, all at a much viable and cheaper cost.

#3 Better Compliance Management This is probably the most critical advantage of outsourcing, where a service provider can generate every relevant Financial Accounting Standards Board (FASB) reports and disclosures on a monthly basis. Every report is duly audited and validated by experienced lease accountants. Access to such expertise also helps you get support in managing and adhering to other financial reporting requirements as per business specific needs. Outsourcing ensures proper governance of the process eliminating any risk of non -compliance and related cost overruns.

#4 Quick Turnaround Time When you partner with a professional service provider apart from cost and effort advantages you also benefit from faster turnaround time. Since lease abstraction is their core service, they are adept in managing complex and voluminous projects within a short timeline while ensuring thorough and accurate delivery.

#5 Efficient Property Portfolio Management Streamlined and compliant engagement with occupants and property management warrant review of leases to ensure adherence to – termination clause, security and maintenance terms and other such details as per the contractual obligation. Expert professionals help extract, standardize, and consolidate hard-to-find and complex data accurately and quickly. They convert the information into user-friendly formats, making it way simpler to review terms, assess risk/details and ensure necessary planning and action resulting in prompt and effective property management.

Springbord has been handling extensive commercial lease abstraction projects for years now. Talk to us to find out how we can share the work load and help you boost accuracy and precision across lease abstraction process.

[springbordrealestateservices]

Scaling Data Labeling Efforts with Automation

As the volume and complexity of data grow across various industries, organizations are increasingly relying on machine learning (ML) and artificial intelligence (AI) to make data-driven decisions. A key element in building efficient and accurate AI models is data labeling, the process of tagging raw data with meaningful labels. Data labeling is a crucial step that enables algorithms to learn and make predictions, but the scale at which this task needs to be performed can overwhelm manual processes.

Fortunately, automation offers a powerful solution for scaling data labeling efforts, improving accuracy, and reducing operational costs. By incorporating automation into data labeling workflows, businesses can handle vast datasets quickly, consistently, and accurately. This article explores how automation can scale data labeling efforts and why it’s an essential tool for organizations dealing with large volumes of data.

Why Scaling Data Labeling Is Essential

Data labeling plays a vital role in training machine learning models. The success of ML and AI applications hinges on the quality of the labeled data, as models rely on labeled datasets to learn patterns and make predictions. Whether it’s images, videos, text, or audio, the accuracy and consistency of the data labels directly influence the performance of AI systems.

For businesses that are working with large datasets, manually labeling data becomes a significant bottleneck. With millions of data points to process, scaling manual data labeling efforts can be expensive, time-consuming, and prone to human error. This is where automation comes in—by automating data labeling tasks, organizations can scale their labeling efforts to meet the demands of their machine learning models.

Here are some ways automation can scale data labeling:

1. Speeding Up the Labeling Process

Manual data labeling can be a slow and labor-intensive process. Human labelers must review and tag every piece of data, which can take significant time, especially when the dataset is large. This delay can be detrimental in industries like e-commerce, healthcare, and autonomous vehicles, where timely data labeling is crucial for deploying machine learning models effectively.

Automation accelerates this process by using machine learning algorithms to label data without requiring human intervention. For example, in the case of image recognition, AI models can automatically detect objects in images and apply the appropriate labels. For text-based data, natural language processing (NLP) tools can extract relevant keywords or sentiments and assign the correct labels.

By automating the labeling process, businesses can significantly speed up the data preparation phase, reducing project timelines and allowing for faster model development and deployment.

2. Ensuring Consistency Across Large Datasets

In manual data labeling, inconsistencies are common. Different human labelers may interpret data differently, leading to discrepancies in labels. For example, when labeling images, one labeler may tag an object as "dog," while another may use "puppy" or "canine," leading to inconsistency in the dataset.

Automation eliminates these inconsistencies by applying a uniform set of rules to label all data. Automated systems follow predefined labeling criteria, ensuring that every piece of data is tagged with the same level of consistency, regardless of its size or complexity. This consistency is crucial for training machine learning models that require a high degree of accuracy for pattern recognition.

For businesses looking to maintain consistency in data labeling across vast datasets, Springbord's data labeling services provide an automated, standardized approach to ensure accuracy and uniformity across all labeled data. Explore how Springbord can help scale your labeling efforts by visiting Springbord Data Labeling Services.

3. Reducing Human Error

Human error is an unavoidable factor in manual data labeling. Whether it's due to fatigue, distractions, or misinterpretations of data, human labelers can make mistakes that compromise the quality of labeled datasets. These errors can lead to inaccurate predictions from machine learning models, undermining the effectiveness of AI-driven systems.

Automating the labeling process reduces the risk of human error. Since automated systems follow predefined algorithms and rules, they apply labels without subjectivity or fatigue. Additionally, automation tools can often detect inconsistencies or errors in labeling and flag them for review, ensuring that any issues are caught before they impact the dataset or machine learning model.

By minimizing human error, automation helps organizations build high-quality labeled datasets that result in more accurate machine learning models.

4. Scaling to Handle Large Datasets

As the volume of data continues to grow, scaling manual data labeling efforts becomes increasingly difficult. To scale manually, organizations would need to hire and train more human labelers, manage them effectively, and ensure consistency in their work. This process is not only costly but also time-consuming.

Automation, on the other hand, offers a scalable solution to handle large datasets without requiring additional human resources. Whether you're working with millions of images, hours of audio, or large volumes of text data, automated systems can process vast amounts of data in a fraction of the time it would take for human labelers.

For organizations dealing with significant data volumes, Springbord’s Data Labeling Services provide an automated platform to scale labeling efforts while maintaining high levels of accuracy. Discover how Springbord can streamline your data labeling process and scale your operations by visiting Springbord Data Labeling Services.

5. Cost Savings

Manually labeling large datasets requires significant investment in human resources, training, and ongoing management. In addition, the time spent on manual labeling increases project costs and delays time-to-market. For companies that need to label data regularly or for large projects, this cost can be a major financial burden.

Automation can help reduce these costs by eliminating the need for a large workforce. Once set up, automated systems can handle labeling tasks at a much lower cost than human labor. Moreover, since automated systems work faster, organizations can complete data labeling projects in a shorter time frame, further reducing costs.

For organizations seeking to optimize their data labeling efforts, Springbord’s data labeling solutions provide a cost-effective way to scale operations and maximize efficiency. Learn how Springbord can help you reduce costs and improve accuracy in your data labeling processes.

6. Enhancing Flexibility and Adaptability

Not all data labeling tasks are the same. Some datasets may require complex labels, while others may involve multi-modal data, such as text, images, and videos. Manual data labeling can be cumbersome and slow when handling diverse data types or large amounts of complex data.

Automation systems are highly adaptable and can be tailored to different types of data and use cases. For example, AI models can be trained to label different types of data, such as categorizing text, detecting objects in images, or transcribing spoken words into text. This flexibility makes it easier for businesses to handle a variety of data labeling tasks without the need for specialized human expertise.

With Springbord's data labeling services, businesses can customize automation to suit their unique data labeling requirements, whether they’re working with image recognition, video tagging, or text classification. Explore Springbord’s approach to flexible and scalable data labeling by visiting Springbord Data Labeling Services.

7. Continuous Improvement with Active Learning

Automated data labeling systems don’t have to work in isolation. By combining automation with human feedback, organizations can improve their models over time. Active learning is a process where the system identifies data points that it is unsure about and requests human intervention to refine labels. This collaborative approach allows the system to continue learning and improve its accuracy over time.

By using active learning in conjunction with automation, businesses can ensure that their models keep improving, even as the data changes or new data types emerge.

Conclusion

Scaling data labeling efforts is essential for organizations seeking to make the most of their machine learning and AI initiatives. As the volume of data grows, manual labeling becomes increasingly inefficient and costly. However, automation offers an effective solution to scale data labeling while improving speed, accuracy, and consistency.

By incorporating automated data labeling into your workflow, you can accelerate the data preparation process, reduce human error, and handle large datasets with ease. Whether you’re working with text, images, audio, or video, automation makes it possible to scale your labeling efforts without compromising quality.

Springbord offers comprehensive and flexible data labeling services that allow businesses to scale their data labeling processes efficiently and accurately. If you’re looking to streamline your data labeling tasks and achieve better outcomes, explore Springbord’s Data Labeling Services today at Springbord Data Labeling Services.

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Why Outsourcing Can Help With Data Labeling Companies

The effectiveness of artificial intelligence and robotic process automation depends on the quality of training data. Data labeling, the process of assigning labels to data samples, is crucial for creating machine learning models that can learn from data. In this article, we discuss the importance of data labeling for companies and explore the benefits of outsourcing this task to specialists.

The Use of Data Labeling on Companies

Machine learning algorithms require labeled data and tags attached to raw data samples like visuals, sounds, and texts. Companies can enhance their decision-making processes using machine learning algorithms with better-trained models. When fed more data, a well-trained machine learning system can create more complex forecasting models.

The Importance of Outsourcing Data Labeling

High-quality data is essential for efficient machine learning models. Many businesses prefer to outsource the labeling of their data to specialists to reap the benefits of their machine-learning models. Outsourcing data labeling can save time and produce better results than in-house data labeling.

Comparing In-house, Crowdsourcing, and Outsourcing Data Labeling:

In-house data labeling involves employing data scientists and infrastructure within the organization. Crowdsourcing recruits regular people to perform tasks like labeling data. Outsourcing data labeling to specialists can be a better option due to the following reasons:

Required Time - Outsourcing data labeling is preferable to doing it in-house because it takes time to train a team and construct the infrastructure needed for data labeling. Crowdsourcing can also be slow due to the internet.

Price - Outsourcing produces better results than in-house data labeling because outsourcing firms spend less on technology and hire fewer data scientists to focus on the labeling process. However, crowdsourcing is more expensive than outsourcing.

Data Quality in Terms of Labeling - Outsourcing and in-house data labeling produce higher quality results than crowdsourcing because trained professionals are used. However, different outsourcing businesses may have varying degrees of expertise regarding data labeling.

Security - Outsourcing offers less security than in-house but more than crowdsourcing. Outsourcing organizations have certifications and various security procedures that lessen the likelihood of data exploitation.

Conclusion

Outsourcing data labeling can significantly reduce costs while still producing high-quality results. Professional firms like Springbord can safely outsource annotation work and complete complex, diverse projects of significant scale. Outsourcing data labeling provides businesses access to highly qualified workers, cutting-edge technology, and rigorous quality assurance procedures.

[springbordrealestateservices]

Organizations that now have or plan to acquire commercial real estate leases should implement a lease administration system that is effective, simple, and extensible.

At Springbord, we've been assisting businesses with lease portfolio management, risk reduction, compliance monitoring, data integration across systems, and improved lease data management.

Get in touch with us to learn more about our Lease management offerings.

http://bit.ly/3G0Z1v3

[spring-bord-blog]

Rapidly evolving digital technologies, proliferation of multiple touch points and changing customer behavior have dramatically shifted the retailing landscape.  With modern retailing becoming more competitive and complex than ever, retailers are aggressively embracing newer business models and multi-channel retailing to retain and gain market share.

There is no doubt that these disruptive trends – social media, ecommerce and mobile commerce have unlocked a world of opportunities for retailers. As data volumes continue to explode from these sources, it offers unparalleled capability to connect, interact and engage with customers at a deeper level. Retailers who leverage big data to its full potential, could boost margins by up to 60%. Now, more than ever, retailers recognize the value of integrating structured and unstructured data to meet evolving customer requirements. However, unfortunately most retailers are still struggling to improve data management. Given the volume, velocity and variety of customer data accumulating across channels, retailers are grappling to effectively capture and make sense of this data.  The key to harnessing the potential of big data is to automate data management that can help create and maintain holistic and accurate view of data across business systems.

Conquering the data volume

Although retailers collect a lot of transactional data, these do not offer complete customer, contextual or business insights to improve marketing strategies. Retailers need to have flexible and high volume data management ability to source and aggregate data from distributed and multiple sources. Automating information management enables you to easily integrate large volumes of structured and unstructured data and gain a holistic view of the market and customer information.

Harnessing the data variety

Integrating newer sources of data also means managing the ‘variety challenge’- new and old data, social media, buying behavior, transaction and search history as well as legacy data. Advanced data management will give you the ability to process the variety of information effortlessly. This in turn helps you gain a granular view of your customers and accurately predict shopping behavior and shoppers’ desires. Based on these insights you can refine marketing strategies and create more compelling campaigns across channels.

Regulating the data velocity in real-time

Immediacy is the key to engaging the ‘always connected’ customers. Timely recommendations or offers at the right time can yield immediate and tangible results. The easiest way for retailers to create captivating experiences for shoppers is to be present wherever their customers are. And for this you need to have the ability to keep pace and manage the large volume of data accumulating in an instant across multiple sources. Automated data management helps you easily gather and process high velocity data pouring in from various sources. This in turn enables you to make real-time decisions and offer relevant and timely promotions.

Master the big data to craft better customer experience

We live in an era that is dominated by empowered customers who expect nothing less than real-time, highly relevant and personalized experience at all times. Moreover, given the ubiquity of smart devices and the advances in digital technologies, customized marketing will continue to evolve as newer mobile services and applications gain consumer adoption.

Given this situation, brands need to build robust master data management capabilities to effectively correlate the explosive growth of data across channels, facilitate advanced analytics and tap into social sentiment. Automated data management allows retailers to focus on customers and deliver consistent and outstanding customer experience, driving sales, loyalty and brand affinity.

Contact Springbord to find out how we can help you enhance data management, synchronize it across its sources and drive smarter business strategies and outcomes.

[spring-bord-blog]

5 reasons to outsource lease audit of your tenants

Running an organization successfully includes management of various departments and resources. Taking care of the property where the office is setup, accounts to at least 15% of the operating costs of an organization, which is usually done by the property manager / real estate intermediary. With mounting business pressures, managing your commercial property lease and the technicalities becomes a hassle. As property managers, your tenant trusts you to manage their commercial properties seamlessly. But it is in the tenants’ interest that a lease audit be done every year during the tenure of stay in any commercial property.

 Lease audit, also known as rent audit, includes the examination of all documents associated with the lease. Lease auditors help clients audit their lease (or their clients’ lease) ensuring non-violation of the terms. Many companies are now looking to outsource lease management for want of better results. Here's a look into why should outsource your clients’ lease audit to the experts:

 1.      Improved process efficiency – Outsourcing lease audit ensures to put better processes and procedures in place. A dedicated team will ensure all lease terms are met and revisits the terms as and when required to address deviations. Experts working on commercial real estate management handle critical lease issues ensuring clients are not charged for more than what they signed up for. With outsourcing, deadlines, renewals and terminations are up to date, reducing paperwork and load enabling the organisation to focus on the company's growth.

2.      Reduced expenditure – Lease audit firms charge a nominal fee for managing your lease audit. This reduces the cost for property managers where staffing and training the personnel is concerned. Overheads of the employees is also cut down when audit is outsources as insurance and other perks are not applicable. In addition to the above, errors due to miscalculation and misinterpretations of the lease terms is reduced, hence saving unwarranted expenditure. Overcharges, unnecessary property taxes and hidden regulations are taken care of in your favour.

3.      Reduced risks – Having to manage various business processes, lease audit might not be the top priority for your client. This paves way to a lethargic approach. Once outsourced to a company whose reputation is built on this, necessary process is in place and thus mitigates risk. Also, to have a third party (than their own property managers) audit their leases gives a sense of assurance to the tenants.

4.      Improved global reach – An organization might be spread across various countries and cultures. Lease audit companies have teams working with expertise on global markets, thus avoiding the need for different teams at various locations. Further, they have the flexibility to upsize or downsize the team as per your requirement at various offices.

5.      State of the art practices – Since the audit companies focus on lease administration and invest on systems designed to aid them, they are likely to follow best practices in the industry today. Continuous surveys help keep track of changing trends and grow with the industry standards. Lease audit tools provide reliable data resulting in effective communication of the issues, if any.

 75% of the businesses have vouched that outsourcing lease audit has not only resulted in reduction of expenses, but also cost recovery. A number of service providers crowd the market for you to choose from.

 Clients approach trusted real estate intermediaries / commercial property managers, who in turn, outsource the tenants’ lease audit to expert companies such as  to Springbord Systems, who have years of experience in auditing commercial property leases. The Springbord team is multi-lingual and multi-disciplinary with knowledge of several regional legal aspects. This ISO/IEC 27001:2013 certified company provides a cost-effective solution and is known to be consistent in their delivery excellence. Know more here