In today's data-driven landscape, the ability to harness and analyze vast amounts of information has become a cornerstone of successful businesses across industries. As organizations increasingly rely on data to inform their decisions, the demand for autonomous data platforms—systems that automate data collection, processing, and analysis—has surged. This growing need presents a lucrative opportunity for entrepreneurs looking to enter the tech space. However, launching a business in this realm requires more than just technical expertise; it demands a keen understanding of the market, a clear vision, and strategic planning. In this article, we will explore the essential steps to establish a successful autonomous data platform business, from identifying your niche and building a robust technological infrastructure to developing a sustainable business model and navigating the competitive landscape. Whether you’re a seasoned entrepreneur or a newcomer to the tech industry, this guide will provide you with the insights you need to embark on your journey.

The global market for autonomous data platforms is experiencing significant growth, driven by the increasing demand for data-driven decision-making and the need for businesses to manage vast amounts of data efficiently. As organizations across various sectors recognize the value of real-time data analytics, the market is projected to expand substantially over the next few years. According to industry reports, the autonomous data platform market was valued at several billion dollars in 2023, with expectations to grow at a compound annual growth rate (CAGR) of over 20% through the end of the decade. This growth can be attributed to several factors, including the rise of artificial intelligence and machine learning technologies, which are enhancing the capabilities of data platforms to automatically manage and analyze data without human intervention. Key sectors driving this market include finance, healthcare, retail, and telecommunications, where organizations are increasingly relying on data platforms to streamline operations, improve customer experiences, and gain competitive advantages. The proliferation of cloud computing solutions has also played a crucial role, enabling businesses to adopt autonomous data platforms with greater ease and scalability. Moreover, the growing emphasis on data privacy and security is prompting companies to invest in robust data management solutions that can autonomously address compliance requirements and protect sensitive information. As more businesses embark on digital transformation initiatives, the demand for autonomous data platforms is expected to surge, presenting a lucrative opportunity for entrepreneurs and startups entering this space. In summary, the robust growth of the autonomous data platform market signifies a ripe opportunity for new entrants looking to establish businesses in this domain. By leveraging cutting-edge technologies and addressing the evolving needs of organizations, entrepreneurs can capitalize on the expanding demand for automated data management solutions.

Identifying the target market is crucial for the success of an autonomous data platform business. The ideal customers for such platforms often belong to various sectors that require advanced data analytics, automation, and efficient data management.
1. Large Enterprises: Corporations with substantial data needs, such as finance, healthcare, retail, and manufacturing, are prime candidates. These organizations often struggle with data silos and require platforms that can automate data integration, cleansing, and analysis to derive actionable insights.

2. Data-Driven Startups: Startups that prioritize data analytics to inform their business strategies and product development can benefit significantly from autonomous data platforms. These companies often seek cost-effective solutions that allow them to scale their data operations without the overhead of hiring extensive data teams.
3. Government and Public Sector: Various government agencies are looking to leverage data for better decision-making and public services. Autonomous data platforms can help these organizations manage vast amounts of data efficiently and ensure compliance with regulations.
4. Educational Institutions: Universities and research organizations that generate large datasets through research projects can utilize autonomous data platforms for data management and analysis, streamlining their research processes and enhancing collaboration.
5. Consulting Firms and Agencies: Businesses that provide consulting services often require robust data solutions to deliver insights to their clients. Autonomous data platforms can help these firms analyze data quickly and effectively, enabling them to provide high-quality service.
6. E-commerce and Retail Companies: With the explosion of online shopping, e-commerce businesses need to analyze customer behavior, inventory levels, and sales trends. Autonomous data platforms can enhance their ability to make data-driven decisions in real-time.
7. Technology Companies: Firms in the tech sector, particularly those focused on AI, machine learning, and data science, require powerful data management solutions to support their product development and research efforts. By understanding these segments, entrepreneurs can tailor their marketing strategies, product features, and customer support to meet the specific needs of their target market. Additionally, recognizing the unique challenges and pain points within each segment will enable businesses to position themselves effectively and differentiate their offerings in a competitive landscape.

When considering the launch of an autonomous data platform business, selecting the right business model is crucial for long-term success and sustainability. Various models can be employed, each with its own advantages and challenges. Here are some prominent business models to consider:
1. Subscription-Based Model: This model involves charging customers a recurring fee for access to the platform. It can be structured as monthly or annual subscriptions, providing a steady revenue stream. Tiered pricing can be implemented, offering different levels of service and features based on customer needs. This model works well for businesses targeting enterprises that require ongoing data management and analytics services.

2. Freemium Model: In this approach, basic features of the platform are offered for free, while premium features require a subscription or one-time payment. This model allows potential customers to experience the platform's value before committing financially. It can be an effective way to build a user base, but careful balance is needed to ensure that enough value is offered in the free version to attract users, while still incentivizing upgrades.
3. Pay-As-You-Go Model: This model charges customers based on their actual usage of the platform, making it appealing for businesses that prefer flexibility and scalability. Customers pay for the data storage, processing power, or specific features they utilize, which can lower the barrier to entry for small and medium enterprises. However, revenue can be unpredictable, so it’s essential to implement robust usage tracking and billing systems.
4. Enterprise Licensing Model: Targeting larger organizations, this model involves offering a comprehensive package that includes licensing the platform for internal use. This can come with a one-time fee or annual maintenance costs. It often includes customization, support, and training services, making it suitable for companies with specific data needs. This model can lead to significant revenue through fewer, larger contracts.
5. Partnership and Reseller Model: In this approach, the platform is integrated into complementary products or services offered by other companies. Partnerships with established firms can provide access to their customer base and enhance credibility. Alternatively, a reseller model can allow third parties to sell your platform, expanding market reach. It’s essential to choose partners whose values align with your business for mutual benefit.
6. Data Monetization Model: If the platform collects and analyzes large volumes of data, monetizing this data can become a revenue stream. This could involve selling aggregated insights to third parties or providing analytics services. However, careful attention must be paid to privacy regulations and ethical considerations when handling data.
7. Consulting and Professional Services Model: Offering consulting services alongside the platform can create additional revenue opportunities. This could include implementation support, data strategy consulting, or training sessions for client teams. Such services can enhance customer satisfaction and loyalty, ensuring that clients derive maximum value from the platform. Each of these business models presents unique opportunities and challenges. Choosing the right one will depend on factors such as target market, competitive landscape, and the specific value proposition of your autonomous data platform. A combination of these models may also be a viable strategy to diversify revenue streams while catering to various customer needs.

The competitive landscape for autonomous data platform businesses is both dynamic and multifaceted, reflecting the rapid evolution of data management technologies and the increasing demand for automation and efficiency in data handling. As organizations seek to leverage data for strategic insights, the market has witnessed a surge in startups and established players aiming to capitalize on this trend. Key competitors in the space can be categorized into several segments:
1. Established Tech Giants: Major technology companies like Amazon, Google, and Microsoft dominate the cloud services market with their robust data solutions. These firms offer comprehensive platforms that integrate data storage, processing, and analytics, often bundled with AI and machine learning capabilities. Their extensive resources, market presence, and ability to innovate quickly pose significant challenges for new entrants.

2. Specialized Data Platform Providers: Companies such as Snowflake, Databricks, and Cloudera provide specialized solutions focused on data warehousing and data lakes. These platforms often emphasize performance, scalability, and ease of use, attracting organizations looking for tailored solutions. New businesses must differentiate themselves by offering unique features, better user experiences, or cost-effective pricing models.
3. Startups and Innovators: The startup ecosystem is vibrant, with numerous companies exploring niche areas within the autonomous data space, such as data integration, self-service analytics, and automated data governance. These agile firms often leverage cutting-edge technologies like AI and machine learning to provide innovative solutions that challenge the status quo.
4. Open Source Solutions: The rise of open source frameworks and tools has also transformed the competitive landscape. Projects like Apache Kafka, Apache Spark, and others allow organizations to build their own data platforms with a high degree of customization. While this presents a cost-effective alternative to proprietary solutions, it also creates competition for businesses that offer fully managed services.
5. Emerging Trends: Recent trends, such as data privacy regulations and the growing importance of ethical AI, are shaping the competitive environment. Companies that prioritize compliance, security, and ethical considerations in their data handling practices may gain a competitive edge. Additionally, the integration of data platforms with other enterprise systems, such as CRM and ERP, is becoming a focal point for differentiation. To succeed in this competitive landscape, new autonomous data platform businesses need to focus on innovation, customer-centric solutions, and strategic partnerships. Understanding the unique needs of target markets and maintaining agility in development and deployment will be crucial in carving out a niche in this crowded and rapidly evolving field.

Starting an autonomous data platform business involves navigating a complex landscape of legal and regulatory requirements. These requirements vary significantly depending on the geographical location of the business, the nature of the data being processed, and the industry in which the platform operates. Here are key considerations to keep in mind:
1. Data Protection and Privacy Laws: The most significant regulatory framework to consider is data protection laws, which govern how personal data is collected, processed, and stored. For businesses operating in the European Union, compliance with the General Data Protection Regulation (GDPR) is essential. The GDPR mandates strict guidelines on obtaining consent, data subject rights, and data security measures. Similarly, in the United States, various laws such as the California Consumer Privacy Act (CCPA) impose requirements on data handling practices, including transparency and consumer rights.

2. Industry-Specific Regulations: Depending on the sector your autonomous data platform serves, there may be additional regulations to comply with. For instance, if you are dealing with healthcare data, you must adhere to the Health Insurance Portability and Accountability Act (HIPAA) in the U.S. Financial services may require compliance with the Gramm-Leach-Bliley Act (GLBA) or the Payment Card Industry Data Security Standard (PCI DSS).
3. Intellectual Property Considerations: Protecting your technology and data assets is crucial. This may involve securing patents for innovative algorithms, trademarks for branding, and copyrights for software code. Understanding intellectual property laws will help you safeguard your proprietary technologies while also ensuring that you do not infringe on existing patents or copyrights.
4. Licensing and Permits: Depending on your location, you may need specific licenses or permits to operate your business legally. This can include business licenses, software licenses, and any industry-specific approvals that may be required to ensure compliance with local regulations.
5. Data Security Regulations: With increasing concerns over data breaches and cyber threats, implementing robust data security measures is not only vital for protecting sensitive information but also often a legal requirement. Regulations may require businesses to adopt specific security protocols, conduct regular risk assessments, and report data breaches within a stipulated timeframe.
6. Terms of Service and User Agreements: Clearly defining the terms of service and user agreements is crucial for protecting your business and managing liability. These agreements should outline user rights, data usage policies, and any limitations of liability. It's advisable to have these documents reviewed by legal professionals to ensure compliance with applicable laws.
7. International Considerations: If your autonomous data platform services clients across borders, you must consider international laws and treaties regarding data transfer and processing. Compliance with regulations such as the GDPR's cross-border data transfer rules is essential to avoid legal repercussions.
8. Ongoing Compliance and Monitoring: Legal and regulatory requirements are subject to change, making ongoing compliance a necessity. Establishing a compliance program that includes regular audits, staff training, and updates to policies and procedures can help ensure that your business remains in line with evolving regulations. By addressing these legal and regulatory requirements from the outset, you can build a solid foundation for your autonomous data platform business, minimizing risks and enhancing trust with your clients and users. Consulting with legal experts who specialize in technology and data protection is advisable to navigate this complex landscape effectively.

When embarking on the journey of starting an autonomous data platform business, securing adequate financing is crucial for developing your technology, hiring talent, and scaling your operations. Here are several financing options to consider:
1. Bootstrapping: Many entrepreneurs start by funding their business with personal savings or revenue generated from initial sales. This approach offers full control over the business without the pressure of external investors. However, it may limit the speed at which you can grow, especially in a technology-driven field that requires significant upfront investment.

2. Friends and Family: Raising money from friends and family can be a quick way to gather initial funds. This option often involves less formal agreements and can help you get off the ground quickly. However, it’s important to manage these relationships carefully and maintain transparency about the risks involved.
3. Angel Investors: Angel investors are individuals who provide capital in exchange for equity or convertible debt. They often have experience in the tech industry and can offer not only funds but also mentorship and networking opportunities. Targeting angel investors who have a background in data technology can add significant value to your business.
4. Venture Capital: If your autonomous data platform shows strong growth potential, you might consider seeking venture capital (VC) funding. VCs typically invest larger sums of money and expect higher returns, often requiring equity stakes and a say in business decisions. The right VC can provide not just funding but also strategic guidance and connections within the industry.
5. Government Grants and Subsidies: Many governments offer grants, subsidies, or low-interest loans to support tech startups. Research available programs in your region that focus on innovation, technology, and data science. These funds often come with fewer strings attached compared to equity financing, allowing you to retain more control over your business.
6. Crowdfunding: Platforms like Kickstarter, Indiegogo, or specialized equity crowdfunding sites allow you to raise funds from a large number of people. This approach not only helps in securing financing but also validates your business idea and builds a community of early adopters. However, a successful crowdfunding campaign requires a solid marketing strategy and a compelling pitch.
7. Strategic Partnerships: Forming alliances with established companies in related sectors can provide access to funding and resources. These partnerships may involve co-development agreements or joint ventures, allowing you to leverage existing technologies and market presence.
8. Incubators and Accelerators: Joining a startup incubator or accelerator can provide seed funding, mentorship, and resources to help scale your business. These programs often culminate in a demo day where startups pitch to a room full of investors. They can also offer valuable networking opportunities and access to a community of entrepreneurs. By carefully considering these financing options and aligning them with your business model and growth strategy, you can set a strong foundation for your autonomous data platform business. Each option has its pros and cons, so it’s essential to evaluate which best fits your vision and operational needs.

When launching an autonomous data platform business, effective marketing and sales strategies are essential to differentiate your offering in a competitive landscape and to attract potential clients. Here are several key strategies to consider:
1. Identify Target Audience: Understanding your target audience is crucial. Start by defining the key industries and businesses that would benefit most from an autonomous data platform. This could include sectors like finance, healthcare, retail, and technology. Develop detailed buyer personas to tailor your marketing efforts and sales pitches to the specific needs and pain points of these audiences.

2. Content Marketing: Content marketing plays a vital role in establishing authority and educating potential customers. Create high-quality, informative content that addresses common data challenges and showcases how your platform can provide solutions. This could include blog posts, whitepapers, case studies, webinars, and video tutorials. Regularly sharing insights on industry trends and best practices can position your business as a thought leader in the data space.
3. Search Engine Optimization (SEO): Optimize your online presence by implementing SEO strategies to improve visibility on search engines. Focus on keywords related to autonomous data platforms, data automation, and analytics. A strong SEO strategy will help attract organic traffic to your website, making it easier for potential customers to discover your offerings.
4. Social Media Engagement: Leverage social media platforms to connect with your audience and promote your content. Platforms like LinkedIn, Twitter, and Facebook can be effective for sharing updates, engaging with industry conversations, and running targeted advertisements. Use social media to highlight customer success stories and share insights to foster community engagement.
5. Partnerships and Alliances: Form strategic partnerships with complementary businesses, such as cloud service providers, data analytics firms, or consulting companies. Collaborating with established players can enhance your credibility and expand your reach. Consider co-marketing initiatives or bundled services that provide additional value to customers.
6. Free Trials and Demonstrations: Offering free trials or live demonstrations of your platform can significantly lower the barriers to entry for potential customers. This allows them to experience the benefits firsthand before making a commitment. Ensure that your onboarding process is smooth and that you provide ample support during the trial period to maximize conversion rates.
7. Customer Testimonials and Case Studies: Showcasing success stories from existing customers can build trust and credibility. Collect testimonials and develop case studies that highlight the specific challenges faced by clients and how your platform helped them achieve their goals. This social proof can be a powerful tool in persuading new customers to adopt your solution.
8. Targeted Email Campaigns: Utilize email marketing to nurture leads and keep your audience informed about your platform's features, updates, and industry insights. Segment your email list based on user behavior and interests to deliver personalized content that resonates with different segments of your audience.
9. Trade Shows and Industry Events: Participate in relevant trade shows, conferences, and industry events to showcase your platform and network with potential clients. These events provide opportunities for face-to-face interactions, allowing you to demonstrate your product and gather valuable feedback directly from users.
10. Continuous Improvement and Feedback Loop: Implement a feedback loop to gather insights from your users and prospects. Regularly assess the effectiveness of your marketing and sales strategies, and be willing to adapt based on data-driven insights. Continuous improvement will help you stay relevant and responsive to market needs. By effectively leveraging these marketing and sales strategies, your autonomous data platform business can build a strong presence in the market, attract a loyal customer base, and drive sustainable growth.

When setting up an autonomous data platform business, operations and logistics play a critical role in ensuring seamless functionality and service delivery. The following components are essential to consider for effective operations and logistics management. Infrastructure and Technology Stack: The backbone of an autonomous data platform is its technology infrastructure. Selecting the right cloud service providers, data storage solutions, and processing frameworks is crucial. Consider using a hybrid cloud approach to balance performance, cost, and security. Implementing scalable solutions enables the platform to handle varying workloads efficiently. Additionally, adopting containerization technologies like Docker and orchestration tools like Kubernetes can enhance deployment and management of applications. Data Management and Integration: Central to the operations of an autonomous data platform is robust data management. Establish processes for data ingestion, cleaning, transformation, and storage. Implementing ETL (Extract, Transform, Load) tools can streamline data integration from various sources. Ensure that your platform supports real-time data processing capabilities to meet the demands of dynamic business environments. Furthermore, maintain strict data governance policies to comply with regulations and ensure data quality. Automation and Orchestration: Automation is the heart of an autonomous platform. Utilize machine learning algorithms to automate data analysis and decision-making processes. Develop orchestration workflows that can manage complex data pipelines and tasks without human intervention. This will not only improve operational efficiency but also reduce the potential for errors and delays. Monitoring and Maintenance: Continuous monitoring of the platform’s performance is necessary to identify and resolve issues proactively. Implement monitoring tools that provide insights into system performance, data flow, and user engagement. Regular maintenance schedules should be established to update software, patch vulnerabilities, and optimize resources. This ensures the platform remains secure and efficient over time. Supply Chain and Resource Management: Efficient logistics involves managing the supply chain of resources required for the platform. This includes hardware procurement, data storage solutions, and network infrastructure. Build relationships with reliable vendors to ensure timely delivery and quality of service. Additionally, manage human resources effectively, hiring skilled personnel with expertise in data science, engineering, and IT support. Customer Support and Feedback Loop: Establish a robust customer support system to assist clients using the platform. Providing responsive support services can significantly enhance customer satisfaction and retention. Create a feedback loop where users can voice their experiences and suggestions, allowing for continuous improvement of the platform based on real-world usage. By focusing on these operational and logistical components, an autonomous data platform business can position itself for success, delivering high-quality, reliable services to its clients while effectively managing internal processes.

When launching an autonomous data platform business, effective human resources (HR) and management strategies are crucial for building a sustainable and innovative organization. Here are key considerations to keep in mind: Recruitment and Talent Acquisition The success of an autonomous data platform relies heavily on the expertise of its workforce. Start by identifying the critical roles needed in areas such as data engineering, machine learning, software development, and data science. Create comprehensive job descriptions that clearly outline the skills and experiences required. Utilize various recruitment channels, including tech job boards, social media, and networking events, to attract top talent. Consider implementing a rigorous selection process, including technical assessments and cultural fit interviews, to ensure candidates align with the company’s vision and values. Culture and Team Dynamics Fostering a positive and collaborative work environment is essential for innovation and productivity. Establish a culture that encourages creativity, continuous learning, and open communication. Promote diversity and inclusion, as varied perspectives can lead to more innovative solutions. Organize team-building activities and encourage cross-functional collaboration to strengthen relationships among employees. A supportive culture not only helps retain talent but also enhances overall team performance. Training and Development Given the rapid advancements in data technology, ongoing training and skill development are vital. Develop a comprehensive onboarding program for new hires to ensure they understand the company’s objectives, values, and the technology stack being used. Implement continuous learning opportunities, such as workshops, online courses, and industry conferences, to keep the team updated on the latest trends and technologies in data management and analytics. Encouraging employees to pursue certifications in relevant tools and methodologies can also enhance the company’s expertise. Performance Management Establish a performance management system that aligns employee goals with the company’s objectives. Regularly set clear, measurable goals and provide constructive feedback through performance reviews. Recognize and reward outstanding performance to motivate employees and encourage a high level of engagement. Utilize key performance indicators (KPIs) to assess both individual and team contributions, helping to identify areas for improvement and investment. Work-Life Balance and Employee Well-Being Promote a healthy work-life balance to prevent burnout and maintain high employee morale. Flexible work arrangements, such as remote work options and flexible hours, can help accommodate diverse employee needs. Additionally, consider implementing wellness programs that focus on mental and physical health, which can lead to increased productivity and job satisfaction. Leadership and Management Style Effective leadership is crucial in guiding the organization towards its goals. Leaders should embody the values of transparency, empathy, and adaptability. Facilitate regular communication between management and staff to ensure alignment and address any concerns. Empower team members by allowing them autonomy in their roles while providing the necessary support and resources for success. A participative management style can foster a sense of ownership among employees, driving innovation and commitment to the organization’s mission. By strategically addressing these HR and management aspects, you can create a robust foundation for your autonomous data platform business, attracting top talent and fostering an environment conducive to growth and innovation.

In summary, embarking on the journey to establish an autonomous data platform business requires careful planning, a deep understanding of the technology landscape, and a clear vision for addressing market needs. By focusing on building a robust architecture that integrates advanced analytics and machine learning, you can create a solution that not only enhances data management but also drives actionable insights for your clients. Additionally, prioritizing security, scalability, and user experience will set your platform apart in a competitive market. As you navigate the challenges of development, marketing, and customer engagement, staying adaptable and responsive to industry trends will be key to your success. With the right strategy and execution, you can position your business as a leader in the rapidly evolving field of data management and analytics, empowering organizations to harness the full potential of their data.

A business plan is a critical tool for businesses and startups for a number of reasons
Business Plans can help to articulate and flesh out the business’s goals and objectives. This can be beneficial not only for the business owner, but also for potential investors or partners
Business Plans can serve as a roadmap for the business, helping to keep it on track and on target. This is especially important for businesses that are growing and evolving, as it can be easy to get sidetracked without a clear plan in place.
Business plans can be a valuable tool for communicating the business’s vision to employees, customers, and other key stakeholders.
Business plans are one of the most affordable and straightforward ways of ensuring your business is successful.
Business plans allow you to understand your competition better to critically analyze your unique business proposition and differentiate yourself from the mark
et.Business Plans allow you to better understand your customer. Conducting a customer analysis is essential to create better products and services and market more effectively.
Business Plans allow you to determine the financial needs of the business leading to a better understanding of how much capital is needed to start the business and how much fundraising is needed.
Business Plans allow you to put your business model in words and analyze it further to improve revenues or fill the holes in your strategy.
Business plans allow you to attract investors and partners into the business as they can read an explanation about the business.
Business plans allow you to position your brand by understanding your company’s role in the marketplace.
Business Plans allow you to uncover new opportunities by undergoing the process of brainstorming while drafting your business plan which allows you to see your business in a new light. This allows you to come up with new ideas for products/services, business and marketing strategies.
Business Plans allow you to access the growth and success of your business by comparing actual operational results versus the forecasts and assumptions in your business plan. This allows you to update your business plan to a business growth plan and ensure the long-term success and survival of your business.

Many people struggle with drafting a business plan and it is necessary to ensure all important sections are present in a business plan:Executive Summary
Company Overview
Industry Analysis
Consumer Analysis
Competitor Analysis & Advantages
Marketing Strategies & Plan
Plan of Action
Management Team
The financial forecast template is an extensive Microsoft Excel sheet with Sheets on Required Start-up Capital, Salary & Wage Plans, 5-year Income Statement, 5-year Cash-Flow Statement, 5-Year Balance Sheet, 5-Year Financial Highlights and other accounting statements that would cost in excess of £1000 if obtained by an accountant.

The financial forecast has been excluded from the business plan template. If you’d like to receive the financial forecast template for your start-up, please contact us at info@avvale.co.uk . Our consultants will be happy to discuss your business plan and provide you with the financial forecast template to accompany your business plan.

To complete your perfect autonomous data platform business plan, fill out the form below and download our autonomous data platform business plan template. The template is a word document that can be edited to include information about your autonomous data platform business. The document contains instructions to complete the business plan and will go over all sections of the plan. Instructions are given in the document in red font and some tips are also included in blue font. The free template includes all sections excluding the financial forecast. If you need any additional help with drafting your business plan from our business plan template, please set up a complimentary 30-minute consultation with one of our consultants.

With the growth of your business, your initial goals and plan is bound to change. To ensure the continued growth and success of your business, it is necessary to periodically update your business plan. Your business plan will convert to a business growth plan with versions that are updated every quarter/year. Avvale Consulting recommends that you update your business plan every few months and practice this as a process. Your business is also more likely to grow if you access your performance regularly against your business plans and reassess targets for business growth plans.