It is with immense joy and a sense of accomplishment that I introduce you to “Predicting the Unknown – The History of AI and Data Science” – a book that has been a labor of love, passion, and unwavering dedication. As the author, I am honored to share this work with all of you, and I couldn’t be prouder of this moment.
‘Predicting the Unknown’ is more than just a book to me; it is the culmination of years of exploration, research, and a deep fascination with the enthralling world of data science, machine learning, and artificial intelligence. In these pages, I endeavor to unravel the enigmatic mysteries of uncertainty and offer a glimpse into the boundless possibilities that these cutting-edge fields hold for our future.
As the world increasingly embraces the data-driven era, uncertainty becomes an inevitable companion in the quest for knowledge and understanding. It is in the face of the unknown that true innovation flourishes, and ‘Predicting the Unknown’ aims to be your guiding light in navigating these uncharted waters.
In this book, you will embark on an extraordinary journey that will lead you through the intricate interplay between data science, machine learning, and AI. We will venture into the depths of data, exploring its hidden stories and patterns, and how it empowers us to make informed decisions in a complex world.
Machine learning, the dynamic force behind modern-day breakthroughs, has the power to uncover hidden insights from data and make predictions that transform industries and revolutionize lives. In ‘Predicting the Unknown,’ we will delve into the fascinating world of machine learning algorithms and understand their immense potential to shape the future.
Artificial intelligence, the frontier of innovation, has ceaselessly challenged the boundaries of human achievement. As we explore AI’s impact on society and daily life, we shall gain a deeper appreciation for its transformative capabilities and the ethical considerations that accompany its rapid advancement.
This book is for the curious souls, the knowledge seekers, the dreamers, and the doers. Whether you are an aspiring data scientist, a seasoned AI enthusiast, or simply someone eager to grasp the profound impact of these technologies on our world, ‘Predicting the Unknown’ promises to enlighten and inspire.
As you flip through the pages of ‘Predicting the Unknown,’ I hope you find yourself captivated by the possibilities that lie ahead. May this book ignite your curiosity, challenge your perspectives, and equip you with the tools to navigate the uncertainties of tomorrow with confidence and foresight.
I’d also like to thank everyone who attended my recent book event which you can watch below.
Data science is an integral part of business today. It can help organizations make better decisions by providing insights into their operations and customers. And it can help companies identify opportunities to up-sell, cross-sell, and boost customer retention.
More C-Suite executives, entrepreneurs, and top managers are learning about data science skills to gain a competitive edge in the market and lead the digital transformation. CEOs who have embraced data science have seen the following benefits: improved decision making, increased revenue, reduced costs and improved customer satisfaction.
Three Ways CEOs Can Lead The Digital Transformation With Data Science Knowledge:
1️⃣ CEOs need to show a strong understanding of how data works, and implementation from C-Suite leaders makes the adoption much faster.
2️⃣ Top-level management must know how data scientists are hired and the data teams’ roles.
3️⃣ Entrepreneurs and CEOs need to learn how data can help them scale faster.
If you also want to apply data and AI concepts to grow your business operations, then The Tesseract Academy’s “Executive Data Science And AI Certificate” is for you!
The program provides an introductionto data science, statistics, and analytics through lectures, case studies, and hands-on workshops. The curriculum covers topics such as data mining, predictive modeling, data strategy, business intelligence, machine learning, data maturity, cloud computing, security and privacy concerns (e.g., GDPR), social media analytics (e.g., Twitter) and more.
What the program offers?
⚡ Get 24/7 mentoring access and weekly touchpoints to help you and your team.
⚡ A Capstone project that prepares you for future data science and AI implementation.
⚡ Get certified by Accredible on completing the Capstone project.
Apply data science and AI in your business. Join the course here and enrol now.
You can also book a free call with an expert to discuss further.
Data science, AI, Blockchain and Tokenomics
The Tesseract Academy specializes primarily in data science/AI and related themes (blockchain, software development, etc.):
Reach out to us here if you are interested in our services that help decision-makers, no matter the stage of the evolution of their business
Our certificates and courses are designed for busy executives, decision makers and managers you can find them all here.
We also have free frameworks are designed by experts for non-experts who want to learn how to utilise technologies like AI, data science and blockchain.
Finally, you can check out all our upcoming events that range from data science and AI for decision makers to product management and blockchain here.
Want to improve your chances of landing a data scientist job or grow your data science career? Data science coaching can help grow your career as a data scientist. It mentors data scientists to practice new skills, build their professional network, and ace job interviews.
Data science coaching business is a growing industry. It’s an exciting time for the field of data science because there are many opportunities for new data scientists to create innovative solutions to problems in the world.
The demand for data scientists has increased rapidly in recent years. As a result, it has become more difficult for companies to find qualified candidates who can execute their projects. This is where data science coaching comes in-to provide a solution that can bridge this gap and help companies find the talent they need to succeed.
Let’s discover the four practical advantages of hiring a data science coach for individuals or your business👇
✅ Provide Career Mapping:
Career road mapping allows data scientists to attain their career goals. Data science coaches also provide practical tips and exercises for growing your stagnant career.
✅ Develop Technical Skills:
Data science coaches will help you develop the technical skills necessary for career growth. They create an actionable plan for learning new skills that increase career development chances.
✅ Professional Network Development:
A coach provides opportunities for data science professionals to develop their networks. You can boost your career with a better understanding of the trends and developments in data science through networking.
✅ Feedback On Issues:
Coaching will also help data scientists to get useful feedback on work-related issues. The coaches are experienced professionals who understand the problems faced during their careers.
Are you a Product Manager, Executive or Entrepreneur? This event will help you understand how to adopt AI and develop a good data strategy!
The event is presented by Denton Rawson is an entrepreneur and Founder of IOK Digital Ltd, which is a technology and AI consulting firm. With more than 20 years of experience in the technology industry. He has worked with some of the worlds Top Bluechip organisations in the F100 and F500 at stakeholder level: A proven track record in delivering value for organisations with technology.
Who is this event for?
The event is geared towards non-technical decision makers who are looking for clarity, and actionable insights, instead of more buzzwords and jargon.
The Tesseract Academy specializes primarily in data science/AI and related themes (blockchain, software development, etc.):
Reach out to us here if you are interested in our services that help decision-makers, no matter the stage of the evolution of their business
Our certificates and courses are designed for busy executives, decision makers and managers you can find them all here.
We also have free frameworks are designed by experts for non-experts who want to learn how to utilise technologies like AI, data science and blockchain.
Finally, you can check out all our upcoming events that range from data science and AI for decision makers to product management and blockchain here.
Data maturity is the level of sophistication of an organization in managing its data. It’s a process that requires not just a commitment and investment from the company, but also from the people who work there.
Data Maturity consists of three key stages:
Stage One: Data Governance – It’s about setting up policies and procedures for how to manage information and how to govern data access.
Stage Two: Data Quality Management- This focuses on ensuring that all information is up-to-date and accurate, as well as making sure that it conforms to company standards for formatting and naming conventions.
Stage Three: Data Analytics – This involves using analytical techniques to derive insights from raw data sets.
According to MIT reports, organizations that have not achieved data maturity waste 50% of their time on problems like unorganized data and monotonous data quality. Data maturity refers to the level of any organization in utilizing the data collected through different channels.
Here are four winning strategies that can put your business on the path to achieving data maturity
1) Identify Relevant Business Problems:
The first step toward achieving data maturity is identifying the relevant business problems that need to be solved. Data can be collected and analyzed based on these pain points.
In order to identify relevant business problems, we should first understand the company’s goals and values. We need to know what they value and what they think is important. The business problem will be different for every company, but there are some universal problems that everyone faces.
2) Aligned Data Maturity Model:
Another crucial strategy for successfully using data is to align the data maturity models with the business goals. Many organizations mistake using generic models that don’t provide the required results.
The Aligned Data Maturity Model helps organizations understand their current level of maturity and provides guidance on how to get to the next level. It also provides examples of success stories from companies that have adopted data-driven decision making, which can be used as a case study for similar organizations.
3) Empowering Your Data Team:
Create an empowered data analytics team to improve data maturity. Set different metrics and KPIs to measure the performance of your analytics team.
To empower your data team, you should first provide them with a good working environment by giving them enough resources and tools they need to do their job efficiently. You should also encourage collaboration between different departments in order to create a culture of trust and transparency, which will lead to better communication between teams. Finally, you should provide regular training sessions so that your employees can keep up with technology changes and new developments in their field.
4) Operational Excellence:
Data maturity requires using data for operational excellence. The collected data must be utilized to promote innovation and creativity for becoming a data-mature organization.
Operational Excellence is a strategic approach that seeks to improve the performance of an organization by identifying and solving the root causes of inefficiencies. The goal of Operational Excellence is to create a robust and sustainable process that improves customer satisfaction, reduces costs, and increases productivity.
Operational Excellence is a systematic approach to identify and solve the root cause of inefficiencies. The goal is to create a robust process that improves customer satisfaction, reduces costs, and increases productivity.
Data is the new oil. All organisations will eventually have to become data literate and learn how to extract value from data. This is why understanding data capabilities is key.
This course, designed by Syed Sameer Rahman (voted as one of the top UK leaders in data) and the Tesseract Academy, is designed around a unique data maturity framework, which can help you assess your organisation’s capabilities, and decide on the best next steps.
The Data Science Maturity Framework is a 5-level process that helps you understand the current state of your organization’s data science capabilities.
You can find out more about who this course is for as well as enrol here.
Are you a Product Manager, Executive or Entrepreneur? This event will help you understand how to adopt AI.
Are you any of the following?
A product manager or product owner?
An entrepreneur
Work in a startup or a scale-up
A manager in a bigger organisation?
If yes, then it is quite likely that sooner or later you are going to have to deal with data science.
As more and more companies adopt AI and data science, it is inevitable that those who don’t are simply left behind. Those who do adopt AI, see massive gains in efficiency.
We are excited to announce that we will be launching The Executive Data Science And AI Certificate!
The certificate is designed to help those who are interested in learning how datascience and AI can be applied in business but have no intention of learning proper technical skills. It is designed for individuals with at least one year of work experience in a business environment. It also includes a capstone project that allows students to apply their knowledge and skills in a real-world setting.
Get in touch or register your interest here. Also feel free to book a free call with a data expert.
Data science, AI, Blockchain and Tokenomics
The Tesseract Academy specializes primarily in data science/AI and related themes (blockchain, software development, etc.):
Reach out to us here if you are interested in our services that help decision-makers, no matter the stage of the evolution of their business
Our certificates and courses are designed for busy executives, decision makers and managers you can find them all here.
We also have free frameworks are designed by experts for non-experts who want to learn how to utilise technologies like AI, data science and blockchain.
Finally, you can check out all our upcoming events that range from data science and AI for decision makers to product management and blockchain here.
Artificial Intelligence (AI) can take your business to the next level. AI vendors do not just develop and implement AI tools, but they also provide insights and consulting services for companies who want to implement AI in their business.
However, many companies have difficulty finding the right AI vendor for their business needs. This can lead to delays in the implementation of AI technology for improving major business functions.
The best AI vendors are the ones that have a strong understanding of the company’s business needs and provide a customized solution.
Do you want to discover the five crucial tips to find the perfect AI vendor that fits your business needs?
Here are five points to keep in mind:
📍 Value For Business:
Choose the right AI vendor by learning how AI adds value to your business. AI is not a replacement for human skills, but it does add value to your business. AI can be used for optimisation and automation, and it can be used to generate content. It can also provide insights into customer behaviour, increase the efficiency of operations and help with the decision-making process.
The key to maximizing value is understanding how AI can be used in the context of your specific business objectives.
📍 Testimonials And Case Studies:
The previous testimonials for AI vendors or the case studies provided by them based on previous work are crucial for finding the right AI vendor.
📍 Technology Platforms:
Consider what technology platforms are used by the AI vendor for providing the AI solutions. Open source programs will improve the flexibility and scalability of your AI program in the future.
📍 Customized Or Off-the-shelf Solution:
If your business is trying to solve a unique problem and no off-the-shelf AI solutions are available, then try customized AI solutions through your vendor. The best AI vendors are the ones that have a strong understanding of the company’s business needs and provide a customized solution.
If a company is looking for a customized AI solution, they can find an AI vendor that can provide it. The experts will know the best way to create the custom AI solution and how to integrate it into the company’s system. They will also know how to train the machine learning algorithm that will be used in the solution so that it provides accurate results. Alternatively, if you have enough data and resources, you can create your own custom AI solutions from scratch.
📍 Learn About The Team:
Find out more about the team at your potential AI vendor regarding their previous experience with AI solutions.
As we know choosing the right AI/data science vendor is no easy task. A wrong choice can cost you months in time and millions in money. That’s why we created the following infographic to help businesses out and help them on their AI journey.
This free course, designed by Syed Sameer Rahman (voted as one of the top UK leaders in data), is designed around a unique data maturity framework, which can help you assess your organisation’s capabilities, and decide on the best next steps.
You can find out more about the course and enrol here.
In this report we will examine how the Tesseract Academy data science team helped an organisation extract useful insights from HR data on sensitive topics such as gender and racial bias. You can read it here.
The Tesseract Academy specializes primarily in data science/AI and related themes (blockchain, software development, etc.):
Reach out to us here if you are interested in our services that help decision-makers, no matter the stage of the evolution of their business
Our certificates and courses are designed for busy executives, decision makers and managers you can find them all here.
We also have free frameworks are designed by experts for non-experts who want to learn how to utilise technologies like AI, data science and blockchain.
Finally, you can check out all our upcoming events that range from data science and AI for decision makers to product management and blockchain here.
Data strategy is the process of gathering, analyzing and interpreting data. It is also the process of determining how to use data to create value for your business. Data strategy helps you build a competitive advantage by using data to make better decisions, identify new prospects and stay ahead of your competition. Data Strategy can help businesses understand their customers better and leads to better customer experience. Additionally, it can guide them on how to improve the way they manage their products and services as well as identify new opportunities for growth.
Tips for a successful data strategy
1 – Analyze the right things
If you’re in the business of producing money and have limited resources, limit your attention to indicators that are denoted by a currency sign. Can’t seem to connect it to money? Forget about it.
2 – Determine what is important
Nowadays, you can measure almost everything, particularly if you use digital technology. That does not imply that you should do so. Concentrate on gathering data that will inform a statistic that you can alter.
3 – Stop clogging up inboxes with unnecessary emails
If you are presently sending out a slew of pre-written reports, halt. If no one notices after two weeks, you may utilize the time to do more sensible things with the resource rather than regurgitating papers that are seldom changed.
4 – Keep things as basic as possible
Instead of telling them about your laborious efforts, drill down to one or two figures that will have an impact on the choice you want them to make. Keep it someplace safe in case you be asked a question. The Very Important Person just needs to view the number at the bottom of the spreadsheet, which informs what you propose they do.
5 – Embrace the concept of automation
If it is possible to automate, do so. If you can create alerts for unusual outcomes, do so. Instead of spending time extracting and manipulating data to get to the outcomes, spend your time performing clever thinking about the data.
6 – Make use of the resources available to you
Don’t be intimidated by the prospect of just utilizing Excel. Excel isn’t going away, even though there are constantly new and exciting toys, visualizes, and fashionable languages to learn. With tactical Googling, you may do almost any kind of study you choose.
7 – Ensure that your reporting is credible
Do you want to make money from your website? Verify the accuracy of your tagging. The good news is that there are several tools and organizations that may assist you, and the result will be increased confidence and robustness from your data.
8 – Add a sprinkle of salt to taste
However, digital analytics is less accurate than expected. Customers switch devices mid-transaction, phones switch from mobile data to Wi-Fi, customers stop and call your helpline instead, bots react weirdly, and so on. Digital analytics should be seen as trend indicators rather than financial accounts with the same degree of accuracy. The point is not to ignore them, but to not be bothered about slight deviations.
9 – Tracking conversions
Your analytics software makes digital sales funnels straightforward. But sales funnels are vital for both online and offline businesses. The till is believed to measure purchases, but how much of your footfall really buys is unknown. Assign someone to keep track of the door-to-door visitors. Examine how this reading impacts you by repeating it daily or weekly. Take a standard self-portrait. The conversation rate is computed by dividing sales by customers who cross the barrier. Modify it and track it again.
Implementing a data strategy
How to implement a good data strategy?
1. Make a Plan and Get Buy-In
A data strategy begins with a proposal that garners support from throughout the company. Executive buy-in is required to acquire permission and resources to execute the plan. Getting buy-in from colleagues at all levels of your business is critical to a successful deployment. To achieve executive buy-in, illustrate how the approach will benefit the firm. Your report’s economic reasoning will be key here. It may also illustrate how rivals use data to gain an edge.
Give examples and statistics to support your assertions. Remember that gaining buy-in takes time. A data strategy may need multiple revisions to persuade stakeholders that it is desirable and viable.
2. Create a Data Supervision Team and Allocate Data Ascendancy Roles
This is time to put together a team to handle your data. A group of senior managers and department heads who understand the importance of data as well as the company’s technological and organizational capabilities, opportunities, and restrictions have been selected. Employees from various areas of the firm should be on the team, not only techies. Your in-house people should be assessed and if necessary, recruited from outside to fill in any gaps in your data governance team’s knowledge or expertise. Data strategy development and implementation. There will be a data management team responsible for assigning resources, developing, and updating policies, and reacting to data-related issues that arise.
After forming your team, assign data governance duties to them. Determine who is responsible for ensuring standards compliance, installing technology, and informing personnel of policy changes at this point of the process Establishing clear lines of authority for each member of the team helps everyone feel more invested in the project’s success.
3. Characterize the Data Types and Sources
Next, decide what data to gather and how to acquire it. How much data you require depends on your company objectives? As a publisher, you may tailor your terms according to the interests and posting preferences of your audience. Monitoring which articles certain reader groups often click on may help you figure this out. You might also have a peek at the social media profiles of your target audience to see what they find interesting and post about. Internet marketing may also be used to acquire new customers. Demographic information from online shoppers may help you get there. Third-party data matching these demographics may be purchased and used to target ads at specific individuals. If they are like your current clients, they are more likely to buy from you.
4. Plan data collection and distribution goals
Goal setting is an important component of data strategy development. Ascertain long-term and short-term objectives, as well as overarching and task-specific goals. Your data should ultimately support your company’s goals. Achieve your objectives by describing how data may help each department. Your organization’s five-year plan should include a description of how data will benefit the company. The company’s strategy should be in line with its objective. The use of data may be targeted by each department. This way, the data management staff can have a better understanding of how the organization uses data.
5. Plan your data strategy
After setting objectives, prepare for achieving them. These strategies will form your data strategy’s roadmap. Every goal you establish should be accompanied with a strategy. These plans should contain who owns the objective, the procedure and technology used, the cost, the time frame, and the expected result. These plans should also be flexible enough to be adjusted if something doesn’t function as planned or if circumstances change.
6. Organize and store data
Your data strategy should include storage besides business strategies. These features of data managing are critical in determining data actionability and shareability. Data storage is a basic technological skill, although how it is stored varies greatly across companies. When planning your storage needs, think about how your storage strategy will affect data sharing and consumption. The way you arrange data affects its accessibility, comprehension, and usage. Your storage choice also impacts how easily departments may exchange data. Creating a data storage and organizing strategy should ultimately make data more accessible, shareable, and actionable for those who need it.
7. Get Consent and Start Using Your planned strategy
This business plan should contain all methods and resources needed to fulfil the company’s data objectives, such as capital investments, new hiring, procedures, and organizational structures. After corporate leadership approves your plan, you can start executing and developing it. This will be a continuing effort. Regularly assess your tactics and the success of your firm in achieving your goals. As data becomes increasingly valuable to enterprises of all sizes, the need for a data strategy grows. You need a robust data strategy in order to maximize the value of your data.
Product strategy is a process that enables organizations to develop, deliver, and sustain products that meet customer needs and achieve business objectives. It is important to have an efficient product strategy in place because it will help you avoid the pitfalls of sub-optimal decisions.A robust product strategy can be considered the backbone of any product design. The product strategy plays a crucial role in deciding who the customers of the product will be and how they will use a certain product.Product strategy also includes the different aspects like what actions are required to develop a product and how everyone involved in the product creation contributes to the final product.Here are two essential tips that will help organizations create an efficient product strategy:
1) Identifying the Audience:
What is the most common reason for the failure of products in the market? The biggest reason is that the product was created without a complete understanding of the target audience.
Therefore, it is vital that the needs of the target audience are clearly understood. This is done by conducting market research, analyzing data and conducting interviews with potential customers. The product strategy team then creates a set of personas that represent different customer types and their needs.
Define customer personas based on the research to see if the personas fit the kind of product created.
2) Clear Problem Definition:
Most product decisions are based on bringing value to your customers. Another crucial factor in creating an efficient product strategy is the problem definition.
Once a set of personas are developed, the team needs to define the problems that these personas have, the pain points they experience and what they need to be successful in their daily lives. This information can then be used to determine which features are essential for the product to succeed in its market segment and which features can be eliminated as unnecessary or too expensive to implement.
The product you create must target a specific problem and solve it for the customers.
The Data Product Framework for Startups
This is a framework for any entrepreneur or startup that is thinking about how to productise their data, and increase their valuation through data products. It presents a simple 3-step process that can help you clarify how to make the most out of your data strategy and your product strategy.
Product strategy is the art and science of understanding customer problems and aligning the organization around creating desirable outcomes for customers and your business.
In a world full of tech, building tech products that can challenge and win competition has become more and more difficult and requires a strong and sound strategy in order to create impact on the market.
Are you an entrepreneur or a startup founder?
Are you a CEO or a decision maker?
Are you planning to ideate, develop and bring new tech products to market?
Or increase the impact that your existing tech products have?
If yes, this check out the recording below from the Tech Product Strategy 101 event which we held on 2nd March 2022.