AI-driven tools have swept the world by storm, with a universal presence in all modern business operations. However, every AI tool relies on data insight. Business intelligence and data analytics are vital for the growth and quality of an AI’s learning model.

How BI and AI Compare

Business intelligence (BI) and artificial intelligence (AI) compare by bringing together the best of human intuition and displaying it in different ways. There are similarities and overlaps between these two types of data intelligence systems, however, there are also fundamental differences that keep them separate.

How BI and AI are Applied

Artificial intelligence is the use of a computer system to compare patterns in data and mimic human responses and displays of the pattern. Business intelligence is similar in that it collects data, but, rather than mimic the human response and display that data, business intelligence collects obscure data across a system and then presents it to a human analyst in a way that simplifies it and optimizes it for direct-to-human interaction.

Why BI and AI’s Goals Differ

Simply put, business intelligence is like a good news broadcast: it only tells you the facts about what data exists in the system. AI, however, is like a friendly consultant and suggests what to do with the data collected.

While the goals of the two types of intelligence are different, they pack a punch by being paired together.

Where BI Meets AI

When a business wants to harness an AI’s power to get ahead in its operations, it has to feed the learning model the right kind of data to get the job done. To do justice to your team’s expectations, the business intelligence gathering team needs to produce stellar data visuals and a crystalline path through that data. Making the data make sense is imperative. Data analytics is the AI’s school, and without clear visuals, the AI’s interpretation of data may even misinterpret business goals or brand messaging.

Why AI is Used for Automation

Before you go further, you may be wondering, what is the purpose of automating database processes with AI? While machine learning and AI can feel a bit overdone across the spectrum, the reason for advancing business operations with AI centers around efficiency. Data science professionals have noted that technological processes for housing and distributing data are evolving. Many monolithic data systems are overwhelmed with an abundance of ad hoc reporting demands.

How to Train Your AI

To get the best results from training and AI, you need a clear definition of what the AI’s purpose is and what you want to train it to do. The AI training process may be different for a generative AI, or a tool that creates text and images than it would be for the kind of machine learning tool that would automate an email flow, a customer experience feature, or some other component of e-commerce in a digital business.

Different Learning Types

AI learns in a variety of ways, and different learning techniques are emerging all the time, says the Data Science Dojo. These include as following:

• Supervised learning: explained supervised learning as the category of machine learning and artificial intelligence where datasets are labeled to train algorithms to classify data and predict accurate outcomes with it.
• Unsupervised learning: this type of learning is the inverse of supervised learning, training the AI on unlabeled data instead.
• Reinforcement learning: with this type of machine learning, the agent learns by interacting with an environment.

Gathering Data Visualization

As you determine the best method for your AI model to train under, you’ll have to think ahead about how to prepare data visualization appropriately. Think of it like collecting lesson materials.

Importance of Clean Visuals

As you carry out AI training, your process will be iterative and will require a hands-on approach to keep data visuals and testing visuals clean. For example, machine learning professionals advise new trainers to keep test sets and validation sets separate so that visuals will be

Designing the Dashboard

When you’re ready to display the data for machine learning, it will be time to select an interactive dashboard model design. Interactive data analytics dashboards take on a variety of forms, but the objective is similar for each one: keep the visuals simple, and easy to engage with.

When creating a final dashboard design, incorporating the following elements will advance the efficiency of the completed tool:

• Highlight Key Performance Indicators: Remember, the goal of business intelligence is to show the facts about data in a system, and the goal of artificial intelligence is to produce outcomes or make suggestions. Showing KPIs in an AI-training dashboard will train the AI to make highly metric-influence suggestions.
• Make It Interactive: Adding a splash of color, or interesting display designs can go a long way in making a data dashboard interactive.
• Build To Scale: AIs require an of high-quality data. Building a business intelligence platform to grow with the AI model is important as your business operations will need room to breathe without breaks.

Quick Ethics Note:

Building on those foundational elements is key to a great AI learning model dashboard. It’s also worth mentioning baking ethics in any data you display. Using data responsibly in a visualization built for training an AI model is essential to ensuring the outcomes an AI generates are also ethical.

Why Use Wyn

The value of cutting a clear path through sometimes weedy data is unmatched. Yet, that value can also be cost-ineffective, which pressures many businesses to continue using outdated technologies for data visualization.

As business processes forge ahead with machine learning automation, a business that doesn’t have optimal data visualization is certain to be left behind in tech obsoletion, unless that business can find a cost-effective way to update data collection to suit the demands of modern reporting.

Wyn Enterprises solves the pain point of cost efficiency by eliminating hidden fees. With dashboards deployable in mere minutes, Wyn cuts through a lot of billable hours costs and saves time and efficiency in data reporting, all of which is essential for scaling to modern use case scenarios.

Originally published at https://wyn.mescius.com on February 9, 2024.

Why Business Intelligence Models Matter When Training AI was originally published in Wyn Enterprise on Medium, where people are continuing the conversation by highlighting and responding to this story.

The rise of artificial intelligence (AI) has pushed businesses in every industry to feel a new kind of pressure. Pressure to adopt the emerging technology, modernize their stacks, and gain a competitive edge in the marketplace.

Whether you choose it for content generation or advanced analytics, AI can certainly help. It must, however, work in tandem with data science to create intelligent business solutions.

As separate disciplines, data science and generative AI can only achieve so much.

Together, however, they’re akin to Yin and Yang synergy. Whether it’s for improving productivity, boosting sales, enhancing the customer experience, or accomplishing any other business objective, the dynamic relationship of these two forces is the key to unlocking your full enterprise potential.

Understand the Basics: Data Science and Generative AI

Before exploring the two’s collaborative nature, let’s take a step back. What is data science and generative AI?

• Data Science: Analysis of large data sets to gain insights and intelligence (patterns, trends, opportunities, etc.)
• Generative AI: Technology that creates content outputs, such as text, copy, synthetic data, images, videos, or sounds, using existing data and algorithms.

Conceptually, they’re completely different. One is more of a process, while the other is a set of technologies. Let them work in unison, however, and you can create intelligent solutions to many of your business challenges.

Synergizing Data Science and Generative AI

Generative AI can’t function without the role of data science, and vice versa. Because it relies on mathematical models, generative AI needs data to learn and create. Like someone taking in information to make decisions and develop new ideas, data science feeds its machine learning (ML) and deep learning (DL) algorithms so the generative component can function.

Now, let’s switch it up. How does generative AI fuel data science? Since the main objective in data science is obtaining data-driven insights, you can use AI to train and create better models. For example, generative AI can automate data processing so you always work with clean information. It can also do data augmentation to self-generate synthetic records that supplement your current data sets — giving you a larger sample size without collecting more data.

The result: Robust models that yield more accurate predictions, better opportunities, and higher-value insights.

Use Cases — The Yin and Yang in Action

We see firsthand how data science and generative AI have been used together to create intelligent solutions. Examples:

Content Generation

Generative AI algorithms can create text, images, and videos based on your existing data.

For example, an insurance company can take customer policy information, company branding guidelines, and contact details to create newsletter campaigns that speak to each insured’s risk needs.

Patterns & Predictions

Data analysis techniques can find patterns in large data sets. Generative AI can then add additional data records through augmentation for ML to improve the current model.

For example, a cybersecurity company may have a model that collects a large volume of user and network activity data to find anomalous events — indicating an attack is underway. Generative AI can create synthetic data that trains the ML algorithm to improve threat detection.

Alternatively, a healthcare company might deploy AI to generate synthetic patient records with information like demographics, medical history, symptoms, and other details. The data can train the ML algorithm for better modeling — improving patient outcome predictions and treatment options.

Data Segmentation

Data collection and analysis can help categorize records based on activity and profiles. Generative AI can take information from public data sources and create synthetic records for better, more precise modeling.

For example, retailers often segment their customers in their marketing automation platform for more personalized engagement. After collecting vast amounts of data, having AI analyze commonalities between profiles and activity, and testing current models with ML, the retailer may auto-generate new customer personas it found.

Intelligent Synergy to Enable Enterprise Success

With the intelligent synergy data science and generative AI, you can set a new performance ceiling for your business. Improve business intelligence (BI) by getting more accurate predictions and valuable insights. Generate innovative ideas for your products, services, campaigns, and processes that let you stand out in the marketplace. Scale your BI operation with each by having AI automate tedious data management tasks for you.

The possibilities are endless!

Data Science and Generative AI: Unveiling the Yin and Yang of Intelligent Synergy was originally published in Wyn Enterprise on Medium, where people are continuing the conversation by highlighting and responding to this story.

If you can’t trust the data you’re looking at, how valuable is the data? Unreliable, low-quality, or inaccurate data simply leads to poor analysis and decision-making. This can quickly erode user confidence despite organizational efforts to use data effectively.

Data governance provides the foundation for trust. With the right framework in place, you benefit in a variety of ways by increasing:

• Data quality
• Trust in data accuracy
• Regulatory compliance
• Data security

Yet, 80% of organizations looking to scale digitally fail because of underlying problems with data governance, according to Gartner. Often, it’s because team members see data governance as a set of complex rules rather than a key business driver. Good data governance improves decision-making and creates value.

“Leading firms have eliminated millions of dollars in cost from their data ecosystems and enabled digital and analytics use cases worth millions or even billions of dollars. Data governance is one of the top three differences between firms that capture this value and firms that don’t.” — McKinsey & Company

Still, many companies lack a mature data governance framework. A 2023 survey reported that only 27% of CIOs considered their governance of enterprise information as mature. Nearly 70% said they were in the beginning stages of creating the necessary data discipline.

Component of a Robust Data Governance Framework

While there are no one-size-fits-all data governance policies, there is a set of underlying principles that should guide your adoption.

Data Quality

Data quality focuses on ensuring the data you utilize is accurate, consistent, and reliable. Key elements include:

• Data profiling: Analyzing data structure, content, and quality.
• Data cleansing: Identifying and correcting errors, inconsistencies, and missing values.
• Data standardization: Setting and enforcing clear definitions and formats for data elements.
• Data validation: Implementing processes to verify data accuracy and compliance with standards.
• Monitoring and alerting: Continuously checking and reporting on data quality issues.

Data Stewardship

Stewardship assigns accountability for specific data assets to individuals or teams responsible for its quality, security, and usage. Data stewards act as champions for their assigned data, ensuring it’s managed effectively and used responsibly. Best practices include:

• Clear roles and responsibilities: Defining ownership and accountabilities for different data domains.
• Training and empowerment: Equipping data stewards with the knowledge and tools to fulfill their responsibilities.
• Communication and collaboration: Fostering communication between data stewards and other stakeholders.

Data Protection and Compliance

With data breaches rising 78% in 2023, data protection and compliance must prioritize safeguarding data privacy and security. Key components include:

• Data security policies and procedures: Establishing protocols for access control, encryption, and data loss prevention.
• Privacy policies and procedures: Defining how personal data is collected, used, stored, and protected.
• Compliance with regulations: Adhering to relevant data privacy regulations like GDPR and CCPA.
• Incident response plan: Having a process for detecting, responding, and recovering from data breaches.

Data Management

Data management across the entire lifecycle of data is crucial from creation and collection to storage, analysis, and deletion. Robust data management includes:

• Data architecture: Defining the organization and structure of data storage and systems.
• Data classification: Categorizing data based on sensitivity, usage, and retention requirements.
• Data retention and archiving: Determining how long data is stored and how it’s archived or disposed of.
• Data access: Defining who can access what data and under what conditions.

Enabling Data Governance

It’s not enough to simply write the rules for data governance. Organizations must put in place the right strategies to enable and enforce a data governance framework. Several key steps will drive adoption.

Executive Sponsorship

Executive leadership must drive the process forward, requiring strict adherence to the framework across the organization. Data must be seen as a strategic priority and key driver for business growth. As such, governance must be universally implemented to be effective.

Integration Across Production and Consumption

To ensure data governance aligns with business needs, the responsibility for governance must be integrated at each step in the process. Product teams and development teams must ensure compliance, especially when adding new technology or modernizing business systems.

Data Prioritization

Effective data governance is holistic, ensuring accuracy throughout organizations. However, it can be a challenging process to retrofit policies against existing datasets. Governance teams can define data criticality within each domain and focus governance initially on the data that has the biggest business impact along with any new data created. Over time, however, all data must conform to governance policies to be effective.

Data Governance Controls

Once data governance has been applied, it’s important to validate it. This might include manual checks, automated reviews, and audits. The right data governance tools or platforms can automate some of these processes to simplify validation.

Monitoring and Adapting

Maintaining data governance is an ongoing process. As needs change or new industry regulations emerge, it will be important to review policies and adapt as necessary. It helps to set up tracking for key metrics, such as data quality scores, anomalies, or breaches for regular review.

Create Broad Awareness

There’s a human component to data governance, too. Ultimately, the quality of the data will depend on broad acceptance by everyone who has access to your data. When team members are committed to data governance principles, they are more likely to ensure that data is high quality and safe. Data governance must become embedded into company culture and not seen as a set of rules, but as essential to informed decision-making.

Data Governance is More Than Compliance

When organizations establish a strong data governance program, it creates a culture of transparency and accountability about data usage. In turn, this increases trust — key to making confident, data-driven business decisions. After all, the goal of data governance isn’t really compliance, it’s to create better business outcomes that add significant value to your organization.

Wyn Enterprise provides industry-leading business intelligence (BI) with extensible security to match your data governance protocols. Built-in end-to-end security, role-based permissions, and data governance and modeling create the framework you need for robust governance. Wyn Enterprises goes beyond simply allowing you to work in a secure, centralized environment. It empowers you to actively manage and configure your data governance and security with granular control.

Try Wyn Enterprise for free.

How to Build Data Trust Through Effective Governance was originally published in Wyn Enterprise on Medium, where people are continuing the conversation by highlighting and responding to this story.

Data is everywhere. From customer activity to sales transactions to inventory to financial records and everything in between. The question is no longer, “Are we tracking this information?” But “are we activating it?” In 2024, data monetization, or extracting value from your data, is now vital to a solid business strategy.

Do you have the intel to spot new opportunities that fix process bottlenecks, cut costs, boost sales, or improve customer experiences? Could you sell your data to third parties for additional revenue?

With such fierce competition across all industries, everyone is looking for that edge — the one that enables enterprise success and recognition as a top player in their respective vertical.

Luckily, advantages could be right in front of you, depending on how you use your data. Here are the top five trends driving successful data monetization in 2024:

1. Increased Data Monetization Partnerships:

2024 will see plenty of “spreading the wealth” for data. While the information collected might not benefit you directly, it could be of value to another organization or even an entire industry. So far as to be willing to pay for it.

We often, for example, see threat intelligence sharing in the cybersecurity world to improve detection mechanisms and security controls. Similarly, healthcare providers share patient information with pharmaceutical companies for treatment research, while insurance companies provide third-party manufacturers with claims information to help reduce product risks.

2. More Personalized, Democratized BI Experiences:

Traditionally, business intelligence (BI) was an exclusive club managed by data scientists using their own specialty tools. Now and into 2024, everyone has a role in finding data insights that improve the business’s bottom line.Companies, including Wyn Enterprise, have shifted focus on democratizing data through key features:

• Embedded BI tools that analyze data within any custom application
• Self-service reporting that lets any user create ad-hoc reports and interactive dashboards
• Multitenancy analytics that natively works within your favorite SaaS products

The result of this shift: A more personalized employee experience enables anyone to activate and monetize company data.

3. Adoption Boost of AI-Driven Insights:

This is the apparent trend you’re seeing all over LinkedIn and news sites. Among many use cases, artificial intelligence (AI) has a huge role in data analytics. It can pull insights from large data sets — helping you make predictions, spot patterns, and find opportunities faster than a human analyst.

Taking a step further, natural language processing (NPL) technology has significantly advanced over the last few years — expanding AI use cases into more than just analyzing records found in a data system. NPL can convert human language, such as customer reviews or emails, into useful predictions or trends and let you expand BI through sources you never imagined.

4. Stricter Data Governance:

Because of the industry pressures to adopt AI tools for analytics, data governance will take more priority in 2024. No matter how advanced your BI tools are, insights are obsolete if your data records are incomplete, outdated, duplicated, or just wrong.

Many companies have a long journey to data maturity before successfully incorporating AI into their business strategy. Much of this begins with governance policies that maintain clean, accurate, and up-to-date records by establishing guidelines on data ownership, security, classification rules, quality standards, usage policies, and other vital areas.

5. A Continuing Focus on Ethical Data Practices:

You can’t talk about data without bringing up compliance and ethics. New regulations spring up each year on baseline cybersecurity requirements, rules on using or selling personal data, and customer communication requirements — all impacting how you activate or monetize data.

In 2024, we’ve already seen Google and Yahoo set authentication rules for email communications starting 2/1. Additionally, four U.S. states will begin enforcing customer privacy laws in 2024, including California (CCPA), Texas (TDPSA), Oregon (OCPA), and Montana (MTCDPA).

Data is most often considered an asset. But it quickly becomes a liability if you’re restricted in its use or fail to comply with regulations — resulting in hefty fines. Ultimately, focusing on more ethical data practices will require new investments into security and compliance management, adopting privacy-enhancing technologies (PETs), and rethinking current monetization strategies.

Wyn Enterprise: BI Tools to Support Your Data Monetization Strategy

Wyn Enterprise empowers all your employees to have a role in monetizing your data through self-service BI tools that produce critical insights needed to hit business goals. Request your product demo or sign up for a free trial today to see how our BI platform offers a scalable, personalized experience across your enterprise.

The Top Data Monetization Strategies for 2024 was originally published in Wyn Enterprise on Medium, where people are continuing the conversation by highlighting and responding to this story.

In 2020 the use of telehealth services spiked by an astronomical 70%, according to the American Medical Association (AMA). In the wake of the COVID-19 pandemic, the value of telehealth services and their ability to fill voids in the healthcare system became especially clear.

However as more and more patients and healthcare institutions adopted the use of telehealth services, the need for the infrastructure and management tools to meet the demand and ensure efficiency and accessibility also came to light.

That’s where business intelligence (BI) tools come in.

From ensuring operational efficiency and securing electronic health records (EHR) to providing data driven insights on patient needs and care, BI tools are helping to bridge technology and healthcare to improve overall quality, and make it more accessible for more and more people.

How Business Intelligence (BI) Tools Improve Telemedicine Delivery

BI is playing a crucial role in improving the overall quality and efficiency in delivery of telehealth services, which ultimately improves patient outcomes and general satisfaction with their care.

Some of the key areas where BI tools are streamlining the delivery of telehealth services include:

Data Analysis

BI tools analyze large volumes of healthcare data, including electronic health records (EHRs), billing information, and patient demographics. This analysis helps identify trends, patterns, and correlations that can improve overall management and decision-making.

Predictive Analysis

BI tools can use historical data to predict future trends such as how often patients use telehealth services, patient volume, and the types of services telehealth patients use the most. This helps healthcare organizations with resource allocation and improves efficiency.

Real-Time Monitoring

BI tools provide real-time monitoring of telehealth services, allowing providers to track patient progress, identify technical and service issues, and adapt services as needed.

Improved Patient Engagement

BI tools can analyze patient data to personalize telehealth services, improve patient engagement and communications, and enhance the overall patient experience.

Monitor and Analyze KPIs

BI tools can monitor key performance indicators (KPIs) such as patient wait times, readmission rates, comorbidities, and medication adherence. This information helps healthcare providers target areas for improvement, and implement strategies to fine tune patient care.

Monitor and Manage Population Health

As the lingering effects of the pandemic and the explosion of chronic health problems like diabetes and heart disease have shown, the health of individuals is intertwined with the overall health of the communities in which they live.

BI tools can also analyze population health data to identify at-risk populations more closely and effectively, and develop targeted interventions. This helps to improve the health of entire communities, and potentially reduce healthcare costs as well.

Cost Savings

By identifying inefficiencies and areas for improvement, BI tools can help healthcare organizations reduce some of the administrative costs associated with delivering telehealth services.

Bridging the Gap Between Healthcare and Technology

Incorporating data-driven insights into the management and delivery of telehealthcare services can significantly improve access to remote healthcare services, especially in underserved communities and vulnerable populations such as the elderly and children.

As telehealth services become more efficient and reliable, trust among patients also improves, making them more likely to use the telehealth services available to them in the future.

BI offers analytics, real-time monitoring, and predictive modeling tools to seamlessly blend technology and healthcare in a number of ways:

Better Decision Making

Analytics and reporting capabilities provide insight into past and current data, and help healthcare providers make informed decisions about patient care and practice operations.

Real-time monitoring ensures that decisions are based on the most up-to-date information. Predictive modeling uses historical data to forecast future trends, enabling proactive decision-making and resource planning as quickly and safely as possible.

Improved Patient Care

By analyzing patient data, healthcare providers can personalize treatment plans, predict potential health issues in the short and long term, and design early intervention plans and treatment to prevent complications. Real-time monitoring allows for continuous tracking of patient health metrics, improving outcomes and patient satisfaction.

Optimized Resource Allocation

Analytics and predictive modeling help healthcare organizations optimize resource allocation by forecasting the demand for specific resources and services, identifying inefficiencies, and improving processes and operations. Real-time monitoring ensures that resources are allocated based on current needs, while also allowing providers to effectively plan for the future.

Increasing Accessibility

Telehealth and remote monitoring technologies provided by BI tools expand access to healthcare, especially in underserved or remote areas where wait times and access to follow up care can be difficult to come by for both children and adults.

Patients can receive care without the need for in-person visits, improving accessibility, convenience, and even affordability in some cases.

Drive Continuous Improvement

By continuously analyzing healthcare data and monitoring care outcomes, healthcare providers can continuously identify areas for improvement and implement changes to enhance the quality and efficiency of care.

BI tools are helping to transform healthcare delivery to more and more people, making it more personalized, efficient, and accessible for everyone.

Conclusion

In conclusion, the remarkable surge of telehealth services in 2020, driven by a 70% increase, showcased its pivotal role during the COVID-19 pandemic. As the healthcare landscape adapted to this shift, the need for robust infrastructure and efficient management tools became evident. Enter Business Intelligence (BI) tools, acting as the catalyst to elevate telehealth services.

BI tools play a crucial role in enhancing the quality and efficiency of telehealth delivery. From analyzing vast datasets to predictive analytics, real-time monitoring, and personalized patient engagement, BI tools bridge the gap between healthcare and technology. They offer a strategic approach, focusing on better decision-making, improved patient care, optimized resource allocation, increased accessibility, and driving continuous improvement.

As we navigate the evolving healthcare landscape, the integration of BI tools into telehealth services not only ensures efficiency and cost savings but also brings about a transformative impact on patient outcomes. This data-driven approach not only makes healthcare more personalized but also accessible, paving the way for a future where quality healthcare is within reach for everyone. Business Intelligence tools are at the forefront of this healthcare revolution, driving continuous improvement and making healthcare more efficient, personalized, and accessible for all.

The Impact of Business Intelligence in Telemedicine was originally published in Wyn Enterprise on Medium, where people are continuing the conversation by highlighting and responding to this story.

AI-driven tools have swept the world by storm, with a universal presence in all modern business operations. However, every AI tool relies on data insight. Business intelligence and data analytics are vital for the growth and quality of an AI’s learning model.

How BI and AI Compare

Business intelligence (BI) and artificial intelligence (AI) compare by bringing together the best of human intuition and displaying it in different ways. There are similarities and overlaps between these two types of data intelligence systems, however, there are also fundamental differences that keep them separate.

How BI and AI are Applied

Artificial intelligence is the use of a computer system to compare patterns in data and mimic human responses and displays of the pattern. Business intelligence is similar in that it collects data, but, rather than mimic the human response and display that data, business intelligence collects obscure data across a system and then presents it to a human analyst in a way that simplifies it and optimizes it for direct-to-human interaction.

Why BI and AI’s Goals Differ

Simply put, business intelligence is like a good news broadcast: it only tells you the facts about what data exists in the system. AI, however, is like a friendly consultant and suggests what to do with the data collected.

While the goals of the two types of intelligence are different, they pack a punch by being paired together.

Where BI Meets AI

When a business wants to harness an AI’s power to get ahead in its operations, it has to feed the learning model the right kind of data to get the job done. To do justice to your team’s expectations, the business intelligence gathering team needs to produce stellar data visuals and a crystalline path through that data. Making the data make sense is imperative. Data analytics is the AI’s school, and without clear visuals, the AI’s interpretation of data may even misinterpret business goals or brand messaging.

Why AI is Used for Automation

Before you go further, you may be wondering, what is the purpose of automating database processes with AI? While machine learning and AI can feel a bit overdone across the spectrum, the reason for advancing business operations with AI centers around efficiency. Data science professionals have noted that technological processes for housing and distributing data are evolving. Many monolithic data systems are overwhelmed with an abundance of ad hoc reporting demands.

How to Train Your AI

To get the best results from training and AI, you need a clear definition of what the AI’s purpose is and what you want to train it to do. The AI training process may be different for a generative AI, or a tool that creates text and images than it would be for the kind of machine learning tool that would automate an email flow, a customer experience feature, or some other component of e-commerce in a digital business.

Different Learning Types

AI learns in a variety of ways, and different learning techniques are emerging all the time, says the Data Science Dojo. These include as following:

• Supervised learning: IBM explained supervised learning as the category of machine learning and artificial intelligence where datasets are labeled to train algorithms to classify data and predict accurate outcomes with it.
• Unsupervised learning: this type of learning is the inverse of supervised learning, training the AI on unlabeled data instead.
• Reinforcement learning: with this type of machine learning, the agent learns by interacting with an environment.

Gathering Data Visualization

As you determine the best method for your AI model to train under, you’ll have to think ahead about how to prepare data visualization appropriately. Think of it like collecting lesson materials.

Importance of Clean Visuals

As you carry out AI training, your process will be iterative and will require a hands-on approach to keep data visuals and testing visuals clean. For example, machine learning professionals advise new trainers to keep test sets and validation sets separate so that visuals will be clean.

Designing the Dashboard

When you’re ready to display the data for machine learning, it will be time to select an interactive dashboard model design. Interactive data analytics dashboards take on a variety of forms, but the objective is similar for each one: keep the visuals simple, and easy to engage with.

When creating a final dashboard design, incorporating the following elements will advance the efficiency of the completed tool:

• Highlight Key Performance Indicators: Remember, the goal of business intelligence is to show the facts about data in a system, and the goal of artificial intelligence is to produce outcomes or make suggestions. Showing KPIs in an AI-training dashboard will train the AI to make highly metric-influence suggestions.
• Make It Interactive: Adding a splash of color, or interesting display designs can go a long way in making a data dashboard interactive.
• Build To Scale: AIs require an abundance of high-quality data. Building a business intelligence platform to grow with the AI model is important as your business operations will need room to breathe without breaks.

Quick Ethics Note:

Building on those foundational elements is key to a great AI learning model dashboard. It’s also worth mentioning baking ethics in any data you display. Using data responsibly in a visualization built for training an AI model is essential to ensuring the outcomes an AI generates are also ethical.

Why Use Wyn

The value of cutting a clear path through sometimes weedy data is unmatched. Yet, that value can also be cost-ineffective, which pressures many businesses to continue using outdated technologies for data visualization.

As business processes forge ahead with machine learning automation, a business that doesn’t have optimal data visualization is certain to be left behind in tech obsoletion, unless that business can find a cost-effective way to update data collection to suit the demands of modern reporting.

Wyn Enterprises solves the pain point of cost efficiency by eliminating hidden fees. With dashboards deployable in mere minutes, Wyn cuts through a lot of billable hours costs and saves time and efficiency in data reporting, all of which is essential for scaling to modern use case scenarios.

Why Business Intelligence Models Matter When Training AI was originally published in Wyn Enterprise on Medium, where people are continuing the conversation by highlighting and responding to this story.

As a leader, your organization relies on YOU to make sound decisions that support employees, customers, and, ultimately, the company’s performance goals. Of course, this is no simple task. Like many of today’s decision-makers, you may find yourself in a tough position when it comes to gathering and activating business intelligence (BI):

• You’re overwhelmed with vast amounts of data presented
• You’re confused with reports as the data lacks any context
• You struggle to quickly convert raw data into insights

It’s time to evolve past these traditional ways of reporting that just throw metrics in your face for you to interpret. The future of BI is decision-centric dashboards that give you smarter insights faster for better decisions that drive business success.

Decision-Centric Dashboards Explained

Far more sophisticated than traditional tools that just show performance or activity data (typically as a spreadsheet report), decision-centric dashboards include context around your data through visualizations and granular insights. These dashboards help users quickly make informed decisions through some basic features:

• Key Performance Indicator (KPI) analysis: Users can monitor performance for metrics relevant to their role, such as revenue, manufacturing efficiency, or insurance claims data, to spot trends, patterns, or anomalies.
• Visualization: Users can view their data as a visual chart or graph to quickly find trends or patterns that could lead to potential opportunities.
• Drill-down functions: Users can interact with the dashboard to find more granular insights on summary reports. For example, take a sales report that shows the total revenue generated for the year, then click further to see a breakdown of the revenue by month, customer profile, or product line.
• Real-time data reporting: Dashboards continuously update as their data sources, such as CRMs, ERPs, or other information systems, generate or update data records.
• Dashboard customization: Through self-service tools, users can select which metrics they want to track, choose data visualization options, and set up tailored notifications that meet their BI objectives.

Decision-centric dashboards are interactive by nature. They allow companies to boost BI initiatives with more flexible reporting that aligns with the user’s specific goals and context that helps leaders better understand their data.

Why Decision-Centric Dashboards

Whether you’re using it for sales, customer service, manufacturing, product development, or any other function, decision-centric dashboards can revolutionize how you do BI at your company. Here are some of the perks:

• Reporting is supplemented with visualizations and granular insights — — letting businesses activate their data faster to get ahead of the competition.
• Users can customize and interact with their dashboard for reporting flexibility to meet any business need or objective.
• BI insights are backed with data (often super granular), not emotions or high-level information, for better decision-making capabilities.

In an era where smart insights are everything, decision-centric dashboards are the catalyst that lets businesses succeed while staying ahead of the curve.

Tricks of the Trade: How to Maximize BI with Decision-Centric Dashboards

As you begin your journey to adopting decision-focused dashboard tools, remember to use these best practices to ensure you get reliable BI that can maximize your performance potential.

First, tailor your dashboards accordingly with the right KPIs and data sources. If you’re an IT director, for example, you don’t need to overwhelm yourself with sales or accounting data. Stick strictly to data sources, such as an IT Service Management System or network monitoring tool, that lets you find insights relevant to your position.

Next, give yourself unparalleled visibility with an intuitive dashboard design. As the decision maker, you’re the primary person who needs to understand and interpret their data. Only use data visualizations you’re comfortable with to easily spot new opportunities that enhance your operation.

Finally, while you might be excited to launch your new dashboard and impress your C-Suite colleagues with new ideas, remember the impact insufficient data (out-of-date, duplicated, etc) can have on decisions. Make sure your organization has a robust data governance policy that ensures up-to-date, accurate, and quality information is stored in your sources and, in turn, presented on your dashboard.

How Actionable BI Dashboards Improve Organizational Outcomes was originally published in Wyn Enterprise on Medium, where people are continuing the conversation by highlighting and responding to this story.

For Part 1 of this series, please click here.

And for Part 2 of the series, click here.

Once you understand the foundations of business intelligence and how to interpret data through visualizations, there are plenty of practical applications for BI in the workplace — whether you’re in sales, marketing, human resources, finance or management (or any other department that works with data).

With traditional BI, business users would send a request to the IT team and typically wait several hours or days to get a response. But BI has come a long way since then. Today’s BI platforms are designed for regular business users — not just data analysts — so they’re user friendly and provide access to near-real-time insights.

For example, self-service BI is “an approach to data analytics that enables business users to access and explore data sets even if they don’t have a background in BI or related functions such as data mining and statistical analysis,” according to TechTarget. That means you can query, analyze and visualize data on your own, without having to get the IT team involved.

There’s also embedded BI, which embeds analytic capabilities directly into applications you already use in the workplace, which means you don’t have to toggle between different applications — it’s just part of your regular workflow. It’s also easy to create and share reports, so colleagues and departments can coordinate their efforts — based on the same data — rather than operate in silos.

When it comes to practical applications for BI in the workplace, the world is your oyster. Not only can you use BI to ask questions and query data, but you can set key performance indicators (KPIs) to track progress in meeting your goals — including what isn’t working.

Say, for instance, a marketing campaign doesn’t seem to be producing the expected results. You could do a deep-dive into the data to figure out what’s going on and course-correct if necessary. If an anomaly is impacting your business — such as a major supply chain disruption or natural disaster — you could adjust your tactics to rapidly respond to those conditions.

BI Use Cases for Sales and Marketing

If you’re launching a new product or service, you’ll want to know which marketing campaigns are driving the best results. For example, you may be wondering if certain campaigns are landing better with certain customer segments, or if you need to do more advertising over certain social media platforms. That’s where BI can help, especially when used in conjunction with customer relationship management (CRM) systems.

Customer Segmentation: This allows you to segment customers with shared characteristics, such as demographics, socio-economic status or buying behavior, so you can more effectively tailor product offerings and marketing campaigns to the right customers at the right time.

Ecommerce Personalization: You can use customer segmentation for A/B testing to personalize marketing campaigns and improve customer engagement and conversion rates. For example, you could offer personalized perks to your most loyal customers.

Churn Analysis: You can also identify customers who’ve stopped doing business with your company or are at risk of leaving. By identifying sources of customer dissatisfaction, you can implement retention strategies — and then measure whether those strategies are successful.

Optimizing Product Placement: By analyzing a customer’s preferences and purchase history, you may be able to discover patterns in buying behavior that can then be used to optimize promotions, cross-selling, upselling and even product placement.

Sentiment Analysis: By analyzing customer sentiment from surveys, reviews, social media and other unstructured data sources, you can gain insight into how your brand is perceived. You can then use those insights to improve products, services or marketing strategies.

Other BI Use Cases

From optimizing your supply chain to hiring employees, BI has the potential to transform almost any department in your organization. It can be used to improve strategic planning and decision-making processes, boost organization efficiency and employee productivity, and help retain customers, increase sales and reduce costs.

Here are some common use cases and how BI can help:

Supply Chain Optimization: Using BI, users can look for patterns and trends to make data-driven decisions about inventory needs and measure their success over time.

Inventory Management: Using BI, users can look for patterns and trends to make data-driven decisions about inventory needs and measure their success over time.

Quality Control: Users can analyze sensor data on the shop floor or production line to optimize workflows, predict future maintenance needs and ensure products meet quality standards.

Fraud Detection: BI can be used to identify potential risks, flag anomalies and detect suspicious transactions to help prevent fraudulent activities.

Employee Performance: Line-of-business managers can use BI to improve the performance of their teams, while HR managers can use it to streamline recruitment and retention.

These are just some of the potential use cases for BI — the sky is the limit.

BI isn’t just for data analysts, and it’s not just for large multinational corporations. Find out how Wyn can empower users with self-service BI and interactive dashboards to quickly find important insights and make impactful business decisions.

BI Data Insights for Non-Tech Users, Part 3: Application and Impact was originally published in Wyn Enterprise on Medium, where people are continuing the conversation by highlighting and responding to this story.

Business Intelligence (BI) and smart tools are playing a pivotal role in transforming the efficiency and successful outcomes of clinical trials. They’re helping to streamline everything from trial design and the patient recruitment process, to how data is collected, analyzed, and presented to regulators and sponsors for approval.

More and more, healthcare organizations are recognizing the value of data-driven decision-making and analytics capabilities, and are leveraging BI solutions for better outcomes.

The Role of Business Intelligence (BI) in Designing and Executing Successful Clinical Trial

BI is revolutionizing clinical trials by providing unique data-driven insights that help to enhance trial design, optimize patient recruitment and retention, and improve how trials are run and managed.

The ability to adequately leverage trial data is crucial to the overall success of any clinical trial, and ultimately helps to advance medical research, the development of new medical devices, medications, and the overall quality of healthcare services.

Some of the areas where BI is driving impact in clinical trial management include:

1. Data Driven Trial Design and Decision Making

BI tools provide data analytics in real time, allowing stakeholders to make informed decisions. For example, by leveraging historical data researchers and trial organizers can identify trends, patterns, and potential risks, which helps in designing more efficient and successful trials.

Predictive analytics help to anticipate potential issues and challenges in the trial design, allowing researchers to make proactive adjustments as needed.

Smart tools also help researchers design better trials by analyzing past trials and identifying methodologies that have worked in the past, and areas that could use improvement. The ability to learn from past failures and design flaws in previous trials is an invaluable tool for researchers working on new trials.

2. Patient Centric Approach to Recruiting and Retention

BI tools make it easier to understand patient behavior and demographics, by providing insights which can be used to improve communication and drive patient engagement.

BI tools can streamline the process of identifying eligible trial participants by analyzing electronic health records (EHRs) and other relevant data sources.

Analytic capabilities provide a better understanding of the target patient population, and help to identify and vet eligible candidates for new and ongoing clinical trials.

Predictive modeling and analytics also help trial organizers identify recruitment challenges, and design better strategies to recruit the best participants.

In addition to recruitment, BI also helps trial organizers understand dropout rates and devise strategies to improve patient retention.

3. Manage and Optimize Trial Resources

BI tools help predict costs more accurately by providing insights into how financial resources are being allocated, making it easier to set and stay within budget.

Smart tools also make it easier to analyze and identify inefficiencies, and better manage resources like schedules, budgets, personnel, and trial participants.

How BI Tools Drive Continuous Improvement in Clinical Trials

Data analytics capabilities and reporting tools are the gems in the business intelligence crown. The ability to accurately synthesize and understand trial data helps to make individual clinical trials more successful, and improve how they’re conducted in the future.

Measuring Performance Metrics

BI tools help determine and measure key performance indicators (KPIs) to track results and overall success in clinical trials. KPI data is also valuable for continuous improvement and fine tuning in future trials.

With real time monitoring, BI provides insights into performance and allows for intervention in case of problems like limited enrollment or deviations from the trial protocols.

Risk Mitigation and Compliance

Smart tools make it easier than ever for clinical trials to comply with regulatory requirements by providing a comprehensive overview of trial data, and the ability to generate accurate and timely reports in real time.

Automated reporting tools help streamline the regulatory submission process, reducing the risk of errors and delays.

BI tools help to identify potential risks in clinical trials in the early stages of a trial, making it easier to make adjustments. It also supports compliance by monitoring trial protocols and regulatory requirements, reducing the risk of encountering regulatory issues.

Clinical trials are crucial for advancing medical science, developing new treatments, and improving healthcare outcomes. They involve collaboration among researchers, healthcare professionals, regulatory bodies, and, most importantly, participants who voluntarily contribute to the progress of medical knowledge.

The integration of data using smart tools and business intelligence can significantly improve the effectiveness of clinical trials by optimizing trial design, enhancing patient recruitment, and providing valuable insights for better decision-making throughout the life cycle of the trial.

A data-driven approach ultimately contributes to the advancement of medical research, and the development of innovative new treatments.

The Impact of Business Intelligence on Clinical Trials was originally published in Wyn Enterprise on Medium, where people are continuing the conversation by highlighting and responding to this story.

For Part 1 of this series, please click here.

Business intelligence is about turning data into insights. So now that you have all of this data — from sales and marketing data to customer engagement and behavioral data — how do you actually turn that into insights?

Ideally, you want to be able to find trends, patterns, outliers, anomalies and other insights that can help you make data-driven business decisions and give your organization a competitive edge. But to do that, you need to be able to ‘visualize’ the data.

Think of an Excel spreadsheet, with endless rows and columns of data points. The data is there, but trying to make sense of it requires time, patience and, oftentimes, an extra-large cup of coffee (or two). But pulling that data into a bar graph or pie chart can help us understand the bigger picture.

A BI platform can rapidly sift through enormous amounts of data — much faster than a mere mortal can — and turn it into any number of visualizations. This is ideal for non-technical users and democratizes access to business intelligence, often eliminating the need for data analysts for everyday tasks.

What is Data Visualization?

It’s important for users to be able to access data, but it’s equally important that they understand how to interpret that data. That’s where data visualization comes in. Seeing data come to life in colorful, interactive visuals can help us tell a story with that data.

This isn’t just important for the CEO or line-of-business manager — it’s important to any employee working with data.

“Data visualization is one of the steps of the data science process, which states that after data has been collected, processed and modeled, it must be visualized for conclusions to be made,” according to TechTarget.

For example, a marketing team could use visualizations to track campaigns and metrics, such as open rates, click-through rates and conversion rates. Logistics companies could use them to determine alternative shipping routes during a storm, while healthcare providers could use them to track illness mortality rates in certain regions or age groups.

Data Visualization Techniques

Common visualization techniques include pie charts, bar graphs and maps, but there are many other options that can help users visualize data. But, it’s important to pair the right data sets with the right visualization technique (which could require some basic user training).

For example, “you need interpretative skills and an appreciation of which graphics will provide what kinds of information,” according to an article in the Harvard Data Science Review. “There is so much that can be varied: the variables displayed, the types of graphics, the sizes of graphics and their aspect ratios, the colors and symbols used, the scales and limits, the ordering of categorical variables, the ordering of variables in multivariate displays.”

If you’re looking to compare variables within or between groups, then a bar or column chart might make sense. If you want to show the composition of data, then a pie chart, donut chart or treemap (which displays related hierarchical data in ‘nested’ rectangles) might make more sense.

But there are many other types of visualizations to choose from.

If you want to explain the relationship between different data sets, consider a scatter plot (which shows the relationship between variables) or a bubble chart or cloud (which displays circles of data on a two-dimensional plot).

Or, if you want to show how data has changed over time, you could opt for a line chart or area chart. To plot geographical data, you could choose a map or a heat map (a geospatial visualization that displays data as different colors).

The Role of Dashboards

Another important component of data visualization is the dashboard, which provides an overview of data from different sources all in one place — like the dashboard of a car. Dashboards are highly customizable and interactive so, for example, you can pull together various business metrics and visualizations, like graphs and charts, to tell a story with the data.

Automatic dashboards can track key performance indicators (KPIs), so you can monitor, measure and analyze related and relevant data such as organizational performance. Basically, it makes it easier to see the big picture, so you can spot trends, patterns and outliers — or dig deeper into the data if it brings up additional questions.

A Few Things to Keep in Mind

While there are a lot of upsides, it’s important to note that data visualizations are only as good as the data behind them. As a starting point, you need to be working with clean, high-quality data. If a visualization is based on inaccurate or biased data, then any conclusions drawn from that visualization will also be inaccurate or biased.

Many data visualizations are fairly straightforward. But in some cases, users may need to interpret data and infer conclusions, so it’s possible they could make inaccurate assumptions or misinterpret data. For example, they may need to consider what data has not been included, which could potentially skew the results. That’s why it can be helpful to provide training and resources to users in choosing and interpreting visualizations.

Ultimately, data visualizations help to ‘curate’ data into a form we can easily understand and share with others. This can help us see ‘hidden’ relationships between data sets and bring insights to the surface. Stay tuned for Part 3 of Data Insights 101 to learn more about practical applications and impact!

BI Data Insights for Non-Tech Users, Part 2: Data Vizualization and Interpretation was originally published in Wyn Enterprise on Medium, where people are continuing the conversation by highlighting and responding to this story.