Tag: Data Analytics

Welcome Frederik, could you tell us more about your role at BeCause and how your journey has shaped the company’s mission?

As the CEO of BeCause, I focus on how our AI-powered technology platform fits into the larger narrative of advancing hotel sustainability across the broader travel and tourism sectors. Part of my role is facilitating the connections between BeCause and key industry players to create a global ecosystem where reliable, and trustworthy, sustainability data flows seamlessly between hotels and tourism companies, regulatory bodies, booking platforms, eco-certification issuers, industry organizations, and ultimately, travelers.

When we launched BeCause, our goal was to provide hotels with a more efficient, cost-effective, and transparent way to collect and share their sustainability data. This enables them to a) Reduce the time and cost required to get certified — currently, the main way hotels substantiate sustainability claims and showcase their sustainability credentials; and b) Give the growing number of consumers seeking sustainable accommodations easy access to that information.

As the only company on the market focused on solving the specific challenges hotels and tourism companies face in managing their sustainability data, we have emerged as the standard for data exchange. This is especially significant given where we are in our journey as a company. While the flow of sustainability data might not seem particularly exciting, it is central to modern corporate decision-making and, increasingly, financial operations.

Effective data management processes allow hotel leaders to make the innovative sustainability investments required to reduce the industry’s carbon footprint. Within this context, part of my role is to represent this standard, so I participate in organizations such as the Climate Committee at the Danish Industry Federation.

In terms of how my journey has shaped the company’s mission, I think it’s at the heart of what we do. Growing up in Oman, I witnessed the degradation of coral reefs firsthand, much of it caused by tourism. This experience left a lasting impact on me.  Like many, I love to travel and explore the world, but that passion shouldn’t come at the expense of our planet. Opting for more sustainable accommodations is one way we can reduce the harmful effects of travel, but before BeCause consumers lacked a verifiable way to identify which properties were truly committed to protecting the environment.

Professionally, I come from the world of management consulting, so I’m equally at ease with diving into the granular details while maintaining a big-picture perspective – crucial skills for a CEO.

To Know More, Read Full Interview @ https://ai-techpark.com/aitech-interview-with-frederik-steensgaard/

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In today’s fast-paced business landscape, data is not just an asset; it’s a cornerstone of strategic decision-making. For B2B companies, leveraging data effectively can create significant competitive advantages, enabling them to understand their customers better, streamline operations, and drive innovation. This article explores the importance of a robust data strategy and how businesses can harness data to outpace their competition.

The Value of a Strong Data Strategy in B2B

Why Data is the New Competitive Currency

As businesses increasingly rely on data to inform their decisions, it has become the new competitive currency. Companies that effectively harness data can unlock valuable insights that guide product development, enhance customer experiences, and optimize operational efficiency. For instance, consider how a leading B2B SaaS company used data analytics to analyze customer usage patterns, which led to the development of new features that directly addressed user needs, resulting in a significant boost in customer retention.

Aligning Data Strategy with Business Goals

A successful data strategy must align with the overarching business objectives. Organizations should ensure that their data initiatives are not just about collection but are focused on measurable outcomes. For example, a manufacturing company may set specific targets for reducing downtime by analyzing equipment performance data. By aligning data strategy with business goals, companies can demonstrate clear ROI and reinforce the value of data initiatives across the organization.

Key Components of a Robust Data Strategy

Data Collection and Management

Effective data collection is the foundation of any data strategy. B2B organizations must prioritize collecting relevant and high-quality data from diverse sources, such as customer interactions, market research, and internal processes. Additionally, centralized data storage solutions, such as data lakes or warehouses, can streamline data management and improve access across departments.

Implementing robust data governance is equally essential. Establishing clear policies on data usage, ownership, and security ensures that data remains accurate, reliable, and compliant with regulations. This not only enhances decision-making but also builds trust among stakeholders who rely on data for strategic insights.

In an era where data is a vital asset, developing a robust data strategy is crucial for B2B organizations seeking a competitive edge. By aligning data initiatives with business goals, implementing best practices, and leveraging advanced tools, companies can harness the power of data to drive growth, enhance customer experiences, and remain agile in a dynamic marketplace. Embracing a culture of data-driven decision-making will not only empower organizations to thrive but also position them as leaders in their industries.

To Know More, Read Full Article @ https://ai-techpark.com/data-strategy-competitive-advantage/

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Health Catalyst, Inc. (“Health Catalyst,”  Nasdaq: HCAT), a leading provider of data and analytics technology and services to healthcare organizations, today announced it has successfully completed its acquisition of Lumeon Limited (“Lumeon”), a digital health company with operations in the U.S. and United Kingdom dedicated to helping provider organizations mend broken care coordination processes through automated care orchestration.

Leading providers in the U.S. and the United Kingdom use Lumeon’s Care Orchestration technology to lower costs, optimize clinician and staff time, enhance revenue, and improve quality and patient safety. Together with Health Catalyst, this acquisition aims to leverage Lumeon’s robust and market-leading platform and make it more intelligent through the application of advanced analytics, AI, and Health Catalyst Ignite™ data processing capabilities. Health Catalyst expects this combination will further strengthen and differentiate its core focus on clinical improvement and ambulatory operations, in addition to supporting its current and future Tech-Enabled Managed Services (TEMS) partnerships.

Health Catalyst also anticipates that Lumeon’s presence in the United Kingdom will strengthen its ability to expand and more effectively pursue new opportunities in the region and potentially in other international markets.

Read Full News @ https://ai-techpark.com/health-catalyst-finalizes-acquisition-of-lumeon/

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In recent years, self-service analytics has been the best approach in the field of business intelligence (BI) that aids analytics users in accessing, analyzing, and sharing their data to create actionable insights without the expertise or extra skill set on data analytics.  Therefore, with the increased reliance on data and analytics, analytic users can swiftly move away from conventional IT-centric reporting to much more decentralized self-service tools that will aid in improving business outcomes and making informed decisions for future business opportunities.

In today’s AITechPark article, we will learn more about a few self-service data analytics software and tools that will aid in your daily business processes.

Alteryx Platform

The first self-service data analytics software on our list is Alteryx, which specializes in data preparation and blending. The tools allow analytics users to organize, clean, and analyze data in a repeatable workflow. At the same time, it connects and cleanses the data from data warehouses, cloud applications, spreadsheets, and other sources. However, the issue is that it can be utilized only to connect, research, organize, and model the given data, but not visualize it. To subscribe to this Alteryx Platform, users need to spend $4,950 per year.

Cognos Analytics

With the introduction of Cognos Analytics, IBM presents extensive BI and analytic abilities under two distinct product sequences. This analytical platform allows analytics users to access data and create dashboards and reports. As Cognos Analytics collaborates with IBM Watson Analytics, it enables ML-enabled UX that includes automated pattern detection and supports NLP queries and generation. IBM’s BI software can be deployed both on-premises or as a hosted resolution via the IBM Cloud.

As we have stepped into the world of digitization, there is an increasing reliance on data and analytics. Therefore, with the above self-service analytics tools, users can easily empower their businesses and make better and faster decision-making without errors.

To Know More, Read Full Article @ https://ai-techpark.com/self-service-analytics-tools/

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Business in a dynamic environment no longer considers data a luxury; it’s the fuel that makes wise decisions and drives business success. Imagine real-time insights at your fingertips regarding your customers or the ability to identify operational inefficiencies buried in data sets. Be empowered to drive growth by making data-driven decisions that enable you to optimize marketing campaigns and personalize customer experiences.

However, unlocking this potential is where many of the SMBs struggle. Traditional data analytics solutions often come with fat price tags, thereby positioning themselves beyond companies with limited resources. But fear not! That doesn’t mean it has to be a barrier to entry into the exciting world of data-driven decision-making.

What are data democratization and self-service analytics?

Data democratization means extending access to organizational data to all employees, regardless of their technical nature. It essentially rests on the very foundation that the availability of data should be such that everybody in the entity can get access to information for making decisions and creating a culture that is transparent and collaborative in nature.

Self-service analytics involves tools and platforms that allow users to perform analysis on their own, outside the IT department. They are designed to be user-friendly enough for people in other functions within a company to generate reports, visualize trends, and extract insights on their own from any data they may want.

For small and medium-sized businesses, the benefits that come from data democratization and self-service analytics are huge:

Empower Employees to Make Data-Driven Decisions:

Arm workers at all levels with the ability to make more informed decisions that will have improved outcomes and innovative implications by providing them with relevant data and the proper tools with which to analyze it.

Improve Operational Efficiency:

Much of this IT bottleneck is removed through self-service analytics, improving operational efficiency and increasing decision-making at high speeds.

Gain Insights from Customer Data:

With data democratization, SMBs can get a closer look at customer behavior and preferences to ensure better customer experiences and focused marketing.

Basically, data democratization and self-service analytics democratize the power vested in data to drive efficiency, innovation, and growth within SMBs.

To Know More, Read Full Article @ https://ai-techpark.com/data-democratization-and-self-service-analytics/ 

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In this digital age, data is the new oil, especially with the emergence of augmented analytics as a game-changing tool that has the potential to transform how businesses harness this vast technological resource for strategic advantages. Earlier, the whole data analysis process was tedious and manual, as each project would have taken weeks or months to get executed. At the same time, other teams had to eagerly wait to get the correct information and further make decisions and actions that would benefit the business’s future.

Therefore, to pace up the business process, the data science team required a better solution to make faster decisions with deeper insights. That’s where an organization needs to depend on tools such as augmented analytics. Augmented analytics combines artificial intelligence (AI), machine learning (ML), and natural language processing (NLP) to enhance the data analytics processes, making them more accessible, faster, and less prone to human error.

Organizations using augmented analytics report up to a 40% reduction in data preparation time and a 30% increase in insight generation speed. Furthermore, augmented analytics automates data preparation, insight generation, and visualization, enabling users to gain valuable insights from data without extensive technical expertise.

Yellowfin

Yellowfin specializes in dashboards and data visualization that have inbuilt ML algorithms that provide automated answers in the form of an easy guide for all the best practices in visualizations and narratives. It has a broad spectrum of data sources, including cloud and on-premises databases such as spreadsheets, which enables easy data integration for analysis. The platform comes pre-built with a variety of dashboards for data scientists that can embed interactive content into third-party platforms, such as a web page or company website, allowing users of all expertise levels to streamline their business processes and report creation and sharing. However, when compared to other augmented analytics tools, Yellowfin had issues updating the data in their dashboard on every single update, which poses a challenge for SMEs and SMBs while managing costs and eventually impacts overall business performance.

Sisense

Sisense is one of the most user-friendly augmented analytics tools available for businesses that are dealing with complex data in any size or format. The software allows data scientists to integrate data and discover insights through a single interface without scripting or coding, allowing them to prepare and model data. Eventually allows chief data officers (CDOs) to make an AI-driven analytics decision-making process. However, the software is extremely difficult to use, with complicated data models and an average support response time. In terms of pricing, Sisense functions on a subscription pricing model and offers a one-month trial period for interested buyers; however, the exact pricing details are not disclosed.

To Know More, Read Full Article @ https://ai-techpark.com/top-5-best-augmented-analytics-tools-of-2024/ 

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Askia, can you share more about your role as Chief Growth Officer at DriveLine.ai and the key responsibilities associated with it?

In my role as Chief Growth Officer, I wear several hats, all focused on one critical goal: driving revenue growth and expansion. Through a multi-pronged approach that leverages strategic partnerships, comprehensive growth strategies, I am responsible for propelling DriveLine to market leadership.

My key responsibilities include the development of strategic partnerships and alliances, implementing comprehensive growth strategies, identifying and leveraging category and industry trends including new market opportunities, and the productization of our audience and location intelligence.

Beyond these key responsibilities, I also contribute to other areas which support our growth including working closely with our product and business development teams, to ensure alignment and collaboration across the organization.

With 17+ years of experience in consumer strategy, how has your journey shaped your approach to driving consumer behavior for brands?

Over the past 17+ years, my approach to consumer strategy has been profoundly reshaped a few times. My journey began in 2000 at KTLA-TV, where I dove headfirst into the bustling world of advertising sales, right as the digital advertising revolution converged with television. This early exposure to the nascent digital landscape, when monetization through consumer interaction was still largely uncharted territory, instilled in me a deep appreciation for innovation and a future-focused approach has become a defining characteristic of my strategic skill set ever since.

With almost two decades of experience navigating the ever-evolving media landscape, I have not only witnessed significant changes, but actively participated in shaping them. Through triumphs and setbacks, I have acquired a deep understanding of consumer behavior and the critical role it plays in successful media campaign outcomes. This valuable knowledge informs my strategic approach, ensuring that every campaign I develop is human-centered, data-driven, results-oriented, and impactful.

Can you elaborate on your future-focused approach to campaign performance and how it is applied across various client types, whether local, regional, national, or global?

Every component of advertising is related to a time period, timing and/or seasonality, making advertising campaigns intrinsically planned for the future. By focusing on the future, I help brands achieve their marketing goals in a sustainable and scalable way. By applying my future-focused approach to campaign performance, I help brands achieve success regardless of their size or location. This means focusing on long-term trends, anticipating future consumer behavior, and proactively adapting to stay ahead of the curve.

To Know More, Read Full Interview @ https://ai-techpark.com/aitech-interview-with-askia-underwood/ 

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In a highly regulated business environment, it is a challenging task for IT organizations to manage data-related risks and compliance issues. Despite investing in the data value chain, C-suites often do not recognize the value of a robust data governance framework, eventually leading to a lack of data governance in organizations.

Therefore, a well-defined data governance framework is needed to help in risk management and ensure that the organization can fulfill the demands of compliance with regulations, along with the state and legal requirements on data management.

To create a well-designed data governance framework, an IT organization needs a governance team that includes the Chief Data Officer (CDO), the data management team, and other IT executives. Together, they work to create policies and standards for governance, implementing, and enforcing the data governance framework in their organization.

However, to keep pace with this digital transformation, this article can be an ideal one-stop shop for CDOs, as they can follow these four principles for creating a valued data governance framework and grasp the future of data governance frameworks.

The Rise of the Chief Data Officer (CDO)

Data has become an invaluable asset; therefore, organizations need a C-level executive to set the company’s wide data strategy to remain competitive.

In this regard, the responsibility and role of the chief data officers (CDOs) were established in 2002. However, it has grown remarkably in recent years, and organizations are still trying to figure out the best integration of this position into the existing structure.

A CDO is responsible for managing an organization’s data strategy by ensuring data quality and driving business processes through data analytics and governance; furthermore, they are responsible for data repositories, pipelines, and tools related to data privacy and security to make sure that the data governance framework is implemented properly.

The Four Principles of Data Governance Frameworks

The foundation of a robust data governance framework stands on four essential principles that help CDOs deeply understand the effectiveness of data management and the use of data across different departments in the organization. These principles are pillars that ensure that the data is accurate, protected, and can be used in compliance with regulations and laws.

C-suites should accept the changes and train themselves through external entities, such as academic institutions, technology vendors, and consulting firms, which will aid them in bringing new perspectives and specialized knowledge while developing a data governance framework.

To Know More, Read Full Article @ https://ai-techpark.com/chief-data-officer-in-data-governance/

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Quantum computing (QC) and machine learning (ML) are the two most hot technologies that are being adopted in the IT field. QC has the power of quantum physics to perform computation by providing an unprecedented level of scalability and accuracy; on the other hand, ML has deep learning capabilities and intelligent automation as leverage to scale out large data sets. Thus, the combination of these two applications, i.e., QC and ML, can create new opportunities that could solve complex problems with greater accuracy and efficiency than the traditional way of computing could.

In this article, we will dive into how to implement quantum machine learning (QML) and what the best practices are for AI technologists.

Success Story- Quantum Machine Learning in Automotive Industry

The BMW Group is among the first automotive firms to take an interest in quantum computing. In 2021, BMW Group issued the Quantum Computing Challenge in association with AWS to crowdsource innovations around specific use cases, believing that quantum computing could benefit businesses by solving complex computing problems.

The objective was to determine if the image-trained machine learning system presently in use to detect fractures in produced elements might be improved. To properly train the model, high-resolution photographs of the manufactured components were required. In addition, the organization required a lot of them because those kinds of defects are quite uncommon. There is potential for improvement because obtaining and storing these photos requires time and memory.

BMW Group gave a statement that, “In light of the required human expertise to hand-tune algorithms, machine learning (ML) techniques promise a more general and scalable approach to quality control. Quantum computing may one day break through classical computational bottlenecks, providing faster and more efficient training with higher accuracy.”

After implementing the QML solution, the BMW Group has witnessed 97% accuracy by enhancing the classical algorithm by orchestrating quantum processing unit (QPU) calculations at a crucial part of the analysis. The Quantum model was trained on 40% of the whole dataset. In contrast, the Benchmark model was trained on 70%, which implies that the classical approach is more efficient and manages to provide accurate predictions without unnecessary inputs.

Future Implementation of Quantum Machine Learning

Quantum machine learning (QML) algorithms have the potential to solve maximum problems in a much faster time than the classical algorithm. According to IBM researcher Kristan Temme, there is strong evidence that QML is emerging at a significant speed in all industries. He quotes, “At this point, I’d say it’s a bit difficult to exactly pinpoint a given application that would be of value.”

There are also proven examples where QML has been an advantageous technology over classical computing.

To Know More, Read Full Article @ https://ai-techpark.com/best-practices-for-quantum-machine-learning/ 

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In the fast-moving business world, IT professionals and enthusiasts cannot ignore the use of machine learning (ML) in their companies. Machine learning tends to give a better insight into improving business performance, like understanding trends and patterns that human eyes generally miss out on. Thus, Machine learning (ML) and artificial intelligence (AI) aren’t just words; rather, they have the potential to change the industry positively. Through this article, we will focus on the importance of implementing machine learning and its use cases in different industries that will benefit you in the present and future.

The Usefulness of ML in Different Industries

Machine learning is a game-changer, and let’s see here how different industries have made the best use of it:

Predictive Analytics for Recommendations

Predictive analytics are generally used to identify opportunities before an event occurs. For example, identifying the customers that have spent the most time on your e-commerce website will result in profit for your company in the long run. These insights are only possible through predictive analytics, which allows your company to optimize market spending and focus on acquiring customers that will generate profit.

 Automate Decision-making

Automated and intelligent decision-making solutions and tools can be used by you to make quick decisions for efficient teamwork. For instance, some industries require strict adherence to compliance, which can only be applied by decision-management tools that help in maintaining records of legal protocols. These tools can make quick decisions if the business fails to obey any compliance rules.

 Creating a Data-Driven Culture

Creating a data-driven culture helps in getting numbers and insights that are generated through data. A data-driven organization not only empowers your teams but also improves your decision-making efficiency and effectiveness. One such example of a data-driven culture is DBS Bank, which has embraced AI and data analytics to provide customers with personalized recommendations. This is helping the customers and the bank authorities make better financial decisions and also improving customer loyalty. By embracing a data-driven culture, DBS Bank has also invested in training employees in data analytics and big data.

Machine learning is an important tool for making automated decisions in various business processes. These models help you identify errors and make unbiased and informed decisions. By analyzing data through customer interaction, preference, and behavior, ML algorithms can help identify the correct patterns and trends, which will help your company in the long run.

To Know More, Read Full Article @ https://ai-techpark.com/ml-helps-make-decisions/ 

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