The Tesseract Academy were delighted to recently host an event on Lean Six Sigma For Artificial Intelligence.
In this introductory webinar, you will learn about the legendary Lean Six Sigma system, from a leadership perspective, and investigate the crossroads with Artificial Intelligence. We will briefly explain the various yellow, green, and black belts certification levels of Lean Six Sigma.
At the end of this webinar, you will have an understanding about the history of L6S, AI + Machine Learning, plus how various levels of managers and entrepreneurs are benefiting across various fields. You will be able to understand the synergy of these groundbreaking technologies.
You can find the video recording of the webinar below.
Who is the speaker?
Captain Grant Mitchell Saxena, U.S.A., Retired, is a highly decorated Officer & U.S. Army Airborne Ranger Presidentially Commissioned and awarded a Bachelor’s of Science in Leadership & Management from The United States Military Academy at West Point – Class of 9/11. An early medical retirement resulted after being wounded as a young platoon leader in Baghdad, during the Iraq war in 2006. Years later in 2021, Grant became an Oxford University alumni earning International Politics graduate school and Lean Six Sigma Yellowbelt Leadership credentials.
Then in 2022, Grant achieved a Post Graduate Diploma in Law from the University of Law in The United Kingdom. Currently, he is six months away from matriculating a committed publication degree through a European University for a Doctor of Philosophy in Business Administration – Information Technology Systems and is enthrallingly writing his dissertation. Grant loves to travel internationally as an advocate, artist, guest lecturer, and journalist.
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.
The Tesseract Academy is committed to educating decision makers on topics such as data science, AI and blockchain. This is why we decided to do several surveys and research reports over the last year to look into organizational culture as well as understand the most common problems and attitudes towards data science and AI in organisations. We also looked into data-driven product development, project management and customer churn prediction. We have summed up the results of the five reports below.
Key Findings: “Data science is clearly part of the future of product management. This will lead product managers acquiring skills in basic data analytics and statistics, whereas data scientists will be expected to have a more in-depth understanding of product management and development. Organisations are looking into a future of cross-functional teams, with individuals picking up skills in related areas, instead of just overspecialising in a single domain.”
Report Summary:
The process of overseeing a product from conception through end-of-life is known as product management. Product managers are in charge of the whole lifespan of a product, which includes developing the product strategy, overseeing the product roadmap, and working with stakeholders to make sure that everything comes together as planned.
According to the product’s survey of experts, they come to know that the use of data science in product management is significant, and experts predict that this use will increase over the next years. Data analytics reveals that, of 100 respondents, 40.7% believe that data analytics is essential to the process and data science will become increasingly significant in product management, according to 29.6%. Moreover, the fact that no participant stated that data scientists should not understand product management was an intriguing finding.
Actually, about 60% think data scientists ought to be familiar with certain areas of product management. Out of 27 replies, 77.8% agreed that the product manager should be familiar with fundamental data analytics. The top three arguments for why data science would be useful in product management, according to the 26 replies we received, were as follows: creating new products (42.6%), experimenting with new features (50%), and improving current goods using data-driven insights (88.5%).
Key Findings: “Predicting customer churn is a hugely valuable proposition for any company and it is very possible to predict customer churn. There are two core deliverables: A determination of factors that contribute to churn and secondly predictive model that predicts which customers are at higher risk of churn and when they are about to churn.”
Report Summary: A significant issue in the insurance sector is customer attrition. Insurance businesses cannot overlook the significant financial repercussions of client attrition. It’s critical to comprehend what leads to client turnover. There are two sorts of churn in terms of customers leaving: active and passive. When a customer cancels their policy before it expires, this is referred to as active churn. When someone merely decides not to renew their insurance, this is known as passive churn.
Since many classification models may provide you with a probability that can be regarded as a risk score, a classification model has the benefit of being easy to comprehend. This suggests that the risk of someone churning is inversely proportional with the hazard. For this issue, survival models are more beneficial and alluring. As the name implies, survival models are widely employed in medicine to mimic patient survival.
Using a survival, we may explicitly define relative risk or the risk of one client in comparison to another, as well as this risk over time. The results shows two primary deliverables: A determination of the elements that influence churn; and prediction algorithm that foretells which clients are more likely to leave and the precise moment when they are about to do so.
Key Findings: “While the space of project management for data science and AI has evolved, there is still lots of work to be done. In order to successfully implement data science and AI projects, companies need to have the right processes in place, and the stakeholders really need to understand the scope and the deliverables of a data science project.”
Report Summary
Any firm must perform the crucial task of project management, which is made much more crucial when it comes to AI initiatives. This is so because AI initiatives are frequently complicated and include several participants with various objectives. The decision to consult with several project management specialists to get their advice on the best ways to handle data science and AI initiatives.
The majority of participants said that they are not utilizing any methodology when asked whether they are using any particular methodologies created for data science and AI. Few people appear to be using CRISP-DM and the Team Data Science Process. Only 29.4% of respondents confidently say “Yes” when asked if the present project management approaches are sufficient for data science initiatives. The existing strategies seem to be lacking something. This may be because software development differs from data science and AI in several ways, making it challenging for approaches created for one field to transfer directly to another.
The fact that 88.2% of participants said that the current project management strategies for AI and data science should be enhanced further supports. The participants provided a range of answers when asked about the largest problem in project management for AI and data science. Among the explanations given were: Executives can’t fully understand data science since it is obscure; ensuring that deadlines are adhered to Data strategy and data quality; Results in data science experiments cannot be guaranteed; Establishing KPIs.
Despite the fact that the field of project management for data science and AI has advanced, more work still needs to be done. Companies must have the appropriate processes in place and stakeholders must truly get the scope and deliverables of a data science project in order to effectively deploy data science and AI initiatives.
Tesseract Report: Organisational Culture in the Post-Covid World, the 4-day Workweek, and Hybrid Work
Key Findings: “Trends like hybrid working, are here to stay. Some others, like the 4-day workweek are popular, but it’s not clear whether they can be implemented successfully. It’s clear that organisational culture plays a huge role in the success of any organisation, especially in attracting and retaining top talent.”
Report Summary
The computer industry and a large portion of professional life were shaken by COVID-19. Hybrid working, remote working, and the 4-day work week are just a few of the new trends that seem to have evolved. It appears that the majority of respondents think that the CEO (45.8%) or the c-suite is responsible for shaping organizational culture. 37.5% of respondents indicated that midlevel managers may guide organizational culture. Most responses to questions concerning organizational culture’s advantages for three organizations centre on three primary points: A higher rate of staff retention (80%); more satisfied workers (68%); Top talent attracting (56%). Out of 25 replies, 56% strongly disagree with retaining competition advantage, 24% agree, and 12% are impartial.
Almost, 48% of respondents who were questioned about the role of culture in employee wellbeing agreed that it was a very significant component. Only 28% of respondents were indifferent on the topic, therefore it appears that roughly 60% of respondents think their company’s organisational culture is helping with post-COVID rehabilitation. The vast majority of participants appear to be employed in a hybrid environment. Of the 25 replies, 68% were hybrid, 16% were remote exclusively, and 16% were office only.
Most individuals would want to work hybrid if given the option, and it appears that 80% of people think that this would also be the case in the future. At least 50% of the respondents would like to work full-time for 4 days per week, even though the bulk of them work full-time (more than 80%) for 5 days per week. Only 40% of respondents think that most businesses will provide a 4-day workweek alternative. Perhaps working just four weeks is insufficient for many businesses. Only 8% of people think that a company’s culture does not have a significant impact on its present financial situation.
Key Findings: “The majority of employees believe that their organisation is not data literate enough and that in order to advance they will need to become more data literate in the future. Data ethics was also a concern and as AI becomes more and more important in our society, we will have to make sure that it is used ethically.”
Report Summary:
Two of the most crucial skills for any organization to possess are data literacy and data science. However, organizations find it challenging to acquire these abilities since both data literacy and data science present unique difficulties. The first issue is the discrepancy between the demand from businesses and the availability of qualified data scientists. Because there aren’t enough professionals in this industry, businesses must contend with one another for these limited resources. The absence of data literacy and data science training is the second problem. Sixty-six percent of participants (66.6%) think they understand data literacy well or completely.
However, more than 50% of the participants said their firm or organization isn’t data literate enough when asked about it. Some of the key justifications offered for why the relationship between the product team and data science is problematic include the following: Data scientists don’t understand the product; product people don’t understand data science; corporate culture. The vast majority of interviewees expressed their dissatisfaction with the management of AI programmers’. This was principally caused by two factors.
The first is that traditional methods like AGILE do not work well with data science. The second problem is that the company’s data scientists are hesitant to follow a methodology. On the overall growth of the firm, both of these might have a big effect. The majority of responders said that their present organizational culture was heavily data-driven. Still, 27.8% of respondents claimed that their organization’s culture is not at all data-driven.
All participants (100%) agreed that having a data-driven organization is necessary to remain competitive and outperform competitors.
If you have any questions or suggestion, feel free to get in touch. We provide both consulting services, as well as online and in-person workshops on all the aforementioned topics, specialising in decision makers with no technical knowledge. We also offer a range of free courses, webinars and frameworks to assist anyone on their AI or blockchain journey. Whether you are a CEO, an entrepreneur or a manager, the Tesseract Academy can help you and your organisation fully understand and implement data science and AI.
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.
AI has the potential to revolutionize the way we manage projects. AI can be used in a variety of ways for project management. It can be used for scheduling and time management, monitoring and reporting, as well as for data gathering and analysis.
AI has a lot of potential to make project management more efficient and effective, while reducing human error.
The workshop will be hosted by three experts in their field:
Dr Joseph Mallia is an experienced project leader, manager, and enterprise architect. He currently is manager in software development at The Central Bank of Malta.
Zane Harvey is an adjunct Professor of Computer Science at Capitol Technology University. His specialties include Data Engineering, ETL Pipelines, Data Consolidation, and Big Data Storage. His past work and clients include a portfolio of US Government Laboratories, Fortune 500 Companies, as well as small to medium sized private businesses in the USA.
Dr Stylianos Kampakis, CEO Tesseract Academy, is a data scientist with more than 10 years of experience. He has worked with decision makers from companies of all sizes: from startups and solo entrepreneurs (which in total have raised more than $50million in funding) to organisations like, the US Navy, Vodafone and British Land.
Who is this for?
Project managers
Founders/CEOs
Data Scientists
Software Developers
Entrepreneurs
What will be taught?
Explain the differences in project management between data science and traditional software development.
A unique lean project management framework for data science.
AGILE data science.
The participants will also have the opportunity to bring over their own challenges and problems which the instructors will help them with.
Various project management tools (to be discussed with the participants)
How will the workshop be conducted?
The workshop consists of four 2-hour sessions
It will be conducted on zoom
The workshop will be conducted over 4 days
We try to keep our groups small, so the course can be more interactive and engaging. This means that once you book a ticket, there is some flexibility as to the time and date
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.
Businesses may utilize data products to help them make better choices and procedures. It is possible for non-data scientists to do many types of analysis on large datasets with the help of data products with an intuitive user interface that incorporates techniques from the field of data science. The most important factor in a company’s adoption of data products is the ability to make educated business choices based on data. Data-driven analysis of consumers, internet visitors, surveys, and other data assets provides tremendous economic value in terms of enhancing services or goods.
Examples of Data Products
Customer predictive analytics and financial terminals like the Bloomberg Terminal are just a few examples of data products that are often used. However, to have an influence on a business, good data applications do not have to be at the corporate level. For reasons of data protection, integrity, and flexibility, many companies create their own proprietary data products, much like how no challenge prop firms create simplified access models for traders.
Importance Of Data Products
The right data can help you acquire new customers, increase revenue, and improve the lifetime value of your customers. Brand reputation, growth, and client acquisition may all be negatively impacted using incorrect data. Importance of data products is prescribed below:
1. It starts with distributed ownership
Data products make it simple to adopt a distributed ownership model, unlike older, centralized methods. As a result, there are no longer any boundaries between data experts and business stakeholders who know how to utilize data. It gives domain specialists the authority to act as data product managers, in charge of ensuring high standards of quality, reliability, and efficiency, as well as overseeing the implementation of new features. In addition, when data products are given via a corporate data exchange, you may preserve a degree of central governance and control.
2. Trust is built via the use of data products
Trust is a major issue for businesses when it comes to their data. Users of corporate data have little trust in its quality and accuracy. With data products, you can collect high-quality data and make it simple to locate, understand and consume the goods you’ve curated.
3. Provide easy self-service to a wider audience
Non-technical consumers may engage with data via data products. Quickly assess whether a data product fulfills their needs with the use of a self-service, digital storefront search, preview, and preliminary filtering and analysis. This expedites the collection of the necessary information.
4. Cycles of value production should be used instead of one-off activities
When combined or modified, data products, often maintained via a corporate data interchange, may serve as the foundation for new bespoke data products. This technique may either be carried out by a single person or a group of people. When a new data product is exchanged in data exchange, it benefits an infinite number of business stakeholders in both circumstances.
5. Increased opportunities are available when considering the whole ecosystem
Additionally, managing data as a commodity gives you a basis to increase access to your extended company, which includes your suppliers and even customers. Data product management Using policy-driven data product management allows you to keep track of who has access to each product and what they have done with it. It’s also possible to make use of third-party data by transforming it into data products.
6. Operate more efficiently using data
Lifecycle management is straightforward for data products. Creating, publishing, modifying, and distributing them all follow the same steps. A user who subscribes to a data product via an enterprise data exchange is automatically sent to their selected destination and is updated on a scheduled or event-driven basis through a totally automated data pipeline when they do so. The entire information of consumption is also available for future optimization by product managers. Businesses are on the lookout for data apps that are purpose-built to address a particular issue. The greater the degree of adaptability and customization, the more valuable an employee is to a company.
How can data products help businesses?
Economic resources may be freed up if a firm has exceptional data products embedded within its strategy and culture. When staff spends a great deal of time and effort gathering, cleaning, and organizing data, this is a frequent scenario. Financial analysis, for example, maybe labor-intensive and time-consuming to do manually. Using Tableau Prep, financial analysts may speed up this process and focus on uncovering more insightful information. A wide range of sectors and jobs may benefit from the product’s adaptability.
Easy-to-use features are critical when creating a data application that can be widely accepted and scaled. Data products, like beta tests, should be enhanced based on input from users who are really using the program. You may save money and time by analyzing and improving your company’s operations with the use of data. Regardless matter the size of the organization, waste has a negative impact. A waste of time and money, it eventually affects the bottom line.
For instance, poor advertising selections may be one of the costliest mistakes a firm can make. However, with data indicating how various marketing channels work, you can discover which ones give the biggest return on investment and concentrate on them. Alternatively, you might investigate the reasons why other channels are underperforming and attempt to enhance their performance. This would enable you to boost your advertising spending without increasing the number of leads you produce.
The Data Product Framework for Start-ups
A Data Product Framework is a set of steps and standards to help you design, build, and launch a data-driven product. It provides a 3-step process that can help entrepreneurs build the right data product strategy from scratch. It can be used for any kind of business, but it’s especially useful for start-ups and small businesses.
You can access it via the free frameworks section on our website. Also feel free to get in touch if you have any questions.
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.
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.
Analytical decision-making is becoming more important as the world becomes more complex, the amount of data available grows, and companies are desperate to remain on top of their game. Managers may now better understand their firm, forecast market changes, and manage their risks thanks to data analytics.
Data analytics and AI decision-making go hand in hand, with data analysis AI allowing firms to use datasets to make quicker, more accurate, and more consistent judgments. When compared to people, AI has the ability to evaluate enormous datasets in seconds without mistakes, allowing your staff to concentrate on other tasks.
So, how can data analytics help companies make decisions?
Making the Most of Our Customers’ Behaviors:
Businesses have collected a plethora of consumer data as the emphasis on serving the client has grown in recent years. Firms must use this information to change their products, services, and purchase experiences in order to remain competitive. Managers may get a better grasp of their consumers’ purchasing behaviors and preferences by doing thorough market segmentation. A sophisticated and predictive analytical model may be used by a telecom business, for example, to minimize customer churn and analyze the efficacy of marketing initiatives.
To make these insights more accurate, businesses can use tools like Usercentrics to manage consent for cross device tracking. This ensures analytics platforms can unify customer data across multiple devices, unlocking the full benefits of connected customer journeys – from accurate attribution and reduced ad waste to stronger personalization and more reliable ROAS measurement.
Aside from providing useful consumer insights, pattern data may be utilized to guide marketing expenditures. As a result, marketers are better able to reallocate their resources. Business analytics helps managers gather competitive knowledge on market situations, target customers more effectively, and improve procedures.
Using Data to Drive Performance:
Consumer data and chances for immediate monetary gain absorb most of an organization’s attention, but it is as important to work on increasing efficiency and effectiveness. With the use of data and analytics, businesses can reduce waste and streamline processes. Dashboards, for instance, may reveal data correlations and give managers with precise insights for performing activities such as cost assessments, peer benchmarking, and price segmentation.
Organizations may use business analytics to better recruit, retain, and grow their workforce. In Supply Chain, data analytics is delivering a distinct advantage. Many top data analytics companies in India are helping businesses identify key areas of improvement, such as inventory control or channel management, enabling managers to make more informed, data-driven decisions.
Analytical Risk Management:
Organizations now face a substantial threat from both structured then unstructured data, such as blogs then social broadcasting platforms. Analytics may help companies better detect, analyze, and forecast the risk they are exposed to. Managers must perceive risk analytics as an enterprise-wide strategy besides build mechanisms for integrating data from all levels and activities of the business.
Businesses may include risk into their strategic decision-making process by establishing a uniform baseline for risk assessment and management. The use of sophisticated data models improves the consistency of risky business decisions, enhances data quality, and enhances the ability to respond swiftly to a wide range of data demands.
The Conclusion
Data-driven disruption in the corporate world necessitates a dual perspective from company leaders. To begin with, we must treasure high-risk and profitable opportunities, such as intensifying into new marketplaces or rethinking their commercial strategies. As a second step, they must ensure that their decision-making process incorporates analytics. Analytical changes will help organizations to get an advantage in the digital disruption race and maintain their leadership position.
AI is driving the need for change in project management. AI and data science need to be managed just like any other software and R&D activity, but they also require additional tasks, such as managing the data scientists, expectations and risks.
The simplified project management framework is a great and easy way to think of the project management process. It cuts down on the time needed to manage a project and also helps with tracking tasks and projects more easily. The framework provides an air-tight overview of the entire process, from start to finish. It will be obvious what tasks need to be done when they need to be done by, and who’s responsible for them.
On this event Dr Joseph Mallia who is an experienced project leader, manager, and enterprise architect, discusses this framework in depth, which can be applied in AI, but also related disciplines such as software development
