IT Solutions
for Your Industry
With over a decade of experience working within various aspects of the Healthcare industry, we have found these are the top concerns we’ve continually been engaged in addressing.
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Data Integration and Standardization: With data being stored in disparate systems and formats, it’s difficult to integrate and analyze. We’ll help with data standardization to ensure that the data is consistent and can be effectively compared across different sources.
Privacy and Security: With the increasing amount of sensitive patient data being collected, privacy and security concerns are a top priority. We shall assist to implement robust security measures to protect patient information.
Predictive Analytics: The ability to predict health outcomes and identify high-risk patients is crucial for improving patient care and reducing costs. However, healthcare organizations need to ensure that their predictive models are accurate and reliable.
Real-Time Analytics: Real-time data analytics is critical to be able to quickly identify and respond to emerging health trends and outbreaks.
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Patient Engagement and Experience: We help providers use analytics to improve patient engagement and experience. This includes analyzing patient feedback to identify areas for improvement, and using predictive models to personalize care plans based on individual patient needs.
Regulatory Compliance: Being heavily regulated, we’ve helped healthcare organizations ensure that their data analytics practices are in compliance with regulations such as HIPAA and GDPR.
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Overall, the healthcare industry is facing a number of challenges when it comes to analytics, but we help companies to invest in new technologies and processes to improve their capabilities and better serve their patients.
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In the automotive industry we’ve helped our customers address some of these pressing issues:
Predictive Maintenance: Developing models to predict when a vehicle or machine will require maintenance, reducing downtime and increasing efficiency.
Customer Behavior Analysis: Ability to use data to understand customers' buying habits, preferences, and opinions, helping manufacturers tailor their products and marketing strategies to the customers' needs.
Supply Chain Optimization: Tracking and analyzing every aspect of the supply chain to ensure timely delivery of parts and components, reducing costs and improving efficiency.
Quality Control: Analyzing data to ensure the quality of parts and products, reducing defects and improving reliability.
Environmental Impact Analysis: Measuring and reducing the environmental impact of the automotive industry, including reducing emissions and improving fuel efficiency.
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These are the top pressing issues we can help you address:
Data quality: Manufacturers often struggle to access and analyze quality data due to disparate data sources, inconsistent data formats, and lack of data governance.
Data integration: Integrating data from various sources, including IoT devices, legacy systems, and external data sources, can be challenging.
Data security: Protecting sensitive data from breaches and cyber-attacks is a growing concern in the manufacturing industry.
Skills gap: There is a shortage of skilled data scientists and analytics professionals, leading to difficulty in implementing advanced analytics solutions.
ROI justification: Many manufacturers struggle to justify the return on investment of analytics initiatives, which can make securing funding difficult.
Infrastructure challenges: Outdated IT infrastructure, limited cloud integration, and hardware limitations can hinder advanced analytics initiatives.
Adoption challenges: Resistance to change and lack of buy-in from stakeholders can make it difficult to implement new analytics solutions in the manufacturing industry.
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These are the top pressing issues we can help you address:
Data management and quality: We know a vast amount of data is produced, making it challenging to manage, organize, and analyze. Issues with data quality, consistency, and completeness can impact the accuracy and reliability of the analytics.
Integration of multiple data sources: Institutions often use a wide range of data sources, including market data, customer data, and financial data. Integrating this data from various sources into a cohesive analytics system can be challenging.
Data privacy and security: The industry is heavily regulated, and data privacy and security are critical concerns. Analytics solutions must be designed to protect sensitive data and comply with relevant laws and regulations.
Real-time analytics: With the rise of digital technologies and automation, the industry is increasingly reliant on real-time analytics. However, processing large volumes of data in real-time can be challenging and requires advanced analytics capabilities.
Talent and skills: The industry requires professionals with a combination of technical and business skills to manage and analyze data effectively. However, finding qualified and experienced professionals can be difficult, this is where we can help fill the gap and training existing staff along the way.
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Bring a solutions provider, we have experience with all 3 major cloud companies, and therefore are platform agnostic. We bring the right solution to solve the issue at hand which is compatible with your company’s technology adoption roadmap.
So how can we help:
Lack of skilled talent: Filling in the gap for skilled analytics professionals
Data security and privacy: Knows the controls which needs to be put in place specific to an industry requires experience – which we have.
Real-time analytics: Real-time analytics is a critical requirement for many industries, but it remains a significant challenge, therefore knowing which cloud providers solutions work is critical.
Scalability: Companies are dealing with massive data volumes, and the ability to scale up analytics platforms to handle these volumes requires experience with platforms like
Azure Synapse Analytics, or Google Big query.
Data quality: Ensuring data quality remains a challenge for many technology companies, and it can significantly impact the accuracy of analytics insights.
Extracting actionable insights: Technology is the easy part, but deriving insights that are actionable and can help drive business outcomes – this is our sweet-spot!
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These are some of the challenges we’ve identified and are focused in helping our clients overcome. One of company's founders has also written, produced and directed his own movie – therefore has first-hand experience!
Measuring success: Success in the entertainment industry is not always easy to define, as it can mean different things for different stakeholders, such as the audience, producers, and talent. Measuring the success of a movie or TV show is also challenging because of factors like release dates, marketing, and competition.
Data collection: Collecting accurate and relevant data on the entertainment industry can be difficult, as there are many players involved, including studios, distributors, and exhibitors. Additionally, some data may be confidential or not publicly available, making it harder to analyze and compare.
Predictive analytics: Forecasting audience demand and box office performance is crucial for the entertainment industry, but it's difficult to predict consumer behavior accurately. Factors like changing consumer preferences, competition, and macroeconomic trends can all impact demand.
Piracy: Piracy remains a major challenge for the entertainment industry, as it impacts revenue and makes it harder to track and analyze audience behavior.
Rapidly changing technologies: The entertainment industry is heavily influenced by technology, and new innovations like streaming platforms, virtual reality, and social media are changing the way people consume and interact with entertainment. Keeping up with these changes and understanding their impact on the industry is a challenge for analysts.
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We are General Services Award certified, and have worked as a sub-contractor for a prime for a number of Department of Transit and Transportation state agencies (New Jersey, Idaho, Louisiana, Tennessee, Wisconsin and Arizona)
Data Integration: Government agencies typically have massive amounts of data from multiple sources, but integrating this data is a complex task as the data is often stored in different formats, languages, and databases.
Data Quality: Ensuring the accuracy and consistency of data across multiple sources is essential for effective decision-making, but maintaining data quality is challenging for government agencies.
Privacy and Security: Government agencies are responsible for collecting and storing sensitive information, and ensuring the privacy and security of this data is crucial. As the volume of data grows, it becomes increasingly difficult to protect against breaches and cyber-attacks.
Data Analysis: Analyzing data and deriving insights can be challenging for government agencies, particularly when data is unstructured or incomplete.
Data Governance: Establishing data governance policies and practices, including data standards, data ownership, and data management protocols, is critical for ensuring that data is used effectively and appropriately.
Funding: Many government agencies have limited budgets and resources for data analytics, which can constrain their ability to hire skilled analysts and invest in advanced technologies.
Policy and Regulatory Compliance: Government agencies must comply with a range of policy and regulatory requirements, which can impact their ability to access and analyze data.
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