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.
Data is changing the landscape and scope of business and how resources are managed within an organisation to generate maximum results. Such resources can include data, people, and time. Decision-makers who see the handwriting on the wall maximise data daily to attain sustainable business growth.
As a leader, business owner, brand manager or employee, you too can take advantage of your business data to drive and sustain growth in your company. Your experience level or place in the hierarchy doesn’t matter – data creates equal opportunity for everyone.
Have you discovered that new and innovative ways of doing business are toppling the old analogous methods? Successful startups are already leading in their industries, snatching away the top places from former big players in their niche. These became possible as business leaders discovered that effective implementation of data in decision-making processes guarantees sustainability.
When you effectively use data, you not only generate more returns but also improve your customers’ experience. Your decision-making process becomes simpler and quicker. You save more time and resources. In fact, maximising data in our business brings lasting productivity. Everybody works at an encouraging pace without burnout. With data, you can easily monitor your progress, discover loopholes, and quickly implement changes without shutting down your operation even for a second.
Business decision makers seize the opportunities presented by big data to take their companies to the next level. And stay on top of the game. But you don’t achieve sustainability while sitting in comfy offices giving orders or napping on sofas. You take action. When you take the necessary steps to transform your way of doing business combined with the right tools and strategies, the sky is only a stepping stone.
Backed by an international business bachelor and over a decade of managing businesses, leading teams, and owning a personal brand, our team has accumulated extensive experience from which you can benefit. We share everything you need to transform your business overnight using data. Find out the four (4) effective ways decision-makers, the real data-driven business leaders, maximise data for business sustainability. These steps are practical and have helped us sustain our brand.
Four (4) Ways to Ensure Sustainable Business Growth in Your Business Using Data
Implement an effective data strategy
Incorporate data analytics in business management
Focus on data as a business priority
Use data to add value to customers – “value before money.”
Building a resilient business in the 21st-century demands paying attention to your data
One clear thing about how technology changes modern business is the short-term and long-term positive results that data-driven brands generate. At least the evidence is there for you and me to see.
Take a look at Google and Microsoft, the leading figures in IT and digital technology today. Twenty-four years ago, when Google Inc. was founded by Larry Page and Sergey Brin, data was never in the big picture. Fast forward to 2015, Google began improving their service delivery by turning business data into more valuable insights to offer customers a better search and internet experience.
Today, the Google search engine alone is amazing. You can tell Google what you want and receive millions of helpful results in a few seconds. How Google and other top players transformed their businesses is not a mystery. You can see how heavily they invest in data.
The same goes for Microsoft, Amazon, IBM, Oracle, IT-Kristana Digital Technology and the like, offering a better experience to consumers globally to improve living, save costs, and conserve resources while generating huge returns. This is thanks to data science, business intelligence, data analytics, artificial intelligence, machine learning, and natural language processing. You, too, can empower your workforce, redefine your business and place your brand ahead of your competitors by maximising data in the following ways.
Implement An Effective Data Strategy for Business Sustainability
Data strategy underlines the tools, processes, and rules you define while managing, analysing, and putting your business data into practical use. If you have a business, you have data. But data is just a number, statistics and items that are raw and useless in their natural state.
You need to add a data strategy if you want to turn data into valuable ends to help your business grow. An effective data strategy helps you plan your data usage so you prioritise what matters and what doesn’t. In the presence of business analytics, APIs, ads, online survey forms and sales trackers, every business collects data, whether a startup or established. Once your company operates online and serves customers digitally, you generate data. The size and volume don’t matter, but how you manage the data is most important.
For example, suppose Company A and B generate 1GB of data daily, respectively. In that case, we can assume that both brands have equal opportunities regarding the volume of data available to each. But the deciding factor then is the implementation of the data. Whoever captures the right insights from the available data on time, using the most efficient data strategy, to enhance their mode of delivering top-notch business solutions to customers lead.
You can generate optimum insights from business data only with the right strategy.
A flexible data strategy puts a business in a strong position to solve slow and inefficient business processes, ensure data integrity, privacy, and quality, and provide clarity about current business needs. Just to mention a few of how a dynamic data strategy can enhance customer experience, improve the supply chain, and sustain your business in this competitive landscape.
To gain maximum results, a sustainable data strategy must adopt these six components: data, tools, analytics techniques, documenting and authoring, collaboration, and the people. An effective data strategy begins with understanding your market and consumers, gathering the right data, and utilising the perfect analytical tools to transform the data into insightful forms. When you’ve generated profitable results, you can collaborate with your team to drive more business insights that add value to your audience or customers.
Sustainability is the key here. As Stylianos Kampakis and Seren Yasar at Tesseract Academy put it in the Data Strategy Framework, “business leaders seek to implement pragmatic data strategy and business analytics to drive growth”. Discover how to maximise your business insights to make better data-driven decisions.
Incorporate Data Analytics in Managing Your Business
As mentioned earlier, a data strategy is incomplete when there are no analytical tools to turn the available data into actionable insights. Data analytics, one of the six components of a pragmatic data strategy, empowers business data. Without modern data analytics tools such as Python, R, SAS, Excel, Power BI, Tableau, Apache Spark, and so on, your efforts towards building sustainable business growth backed by data are wasted.
More flexible Business Intelligence (BI) tools like Microsoft Power BI make the gathering, processing, storing, and sharing of insights easier. It enhances collaboration within and outside a team. You can import real-time data from various sources, clean, store, use Microsoft Power BI templates, and make efficient decisions that guarantee growth and development in your company.
Adopt dynamic analytics tools to generate maximum insights from your business data
Microsoft Power Bi, SAS, Tableau, and other Python-backed analytics tools enable even citizen data scientists to handle data quickly and effectively. This is to say that incorporating data analytics in your business creates opportunities to empower a variety of innovative workforce who don’t have academic backgrounds in data-related fields but are creative enough to discern patterns in data.
Working with citizen developers, analysts and data-driven decision makers can cut costs for your company and encourage diversity. Even without a degree in data analysis, you can become a data scientist to boost your skills and secure your dream job.
With the right skills and talents to work with, you must select the best analytics tools that empower your team. Can you prepare your data for processing without flexible and power-packed technologies? The answer is NO. How can I analyse business data to make better decisions when the right analytics tools are available? Empower the right talent with the perfect tools, and they’ll do wonders.
Incorporating data analytics in managing and growing your business begins with gathering the right data. This follows with studying, interpreting, analysing, sharing, and visualising the data. Some organisations may use time-consuming manual processes instead of automated analytics software that generates more efficient results quicker and easier. Without a doubt, they suffer huge losses and miss important metrics that could have pushed their brands onward.
Make sure the tools you choose have the capabilities to unite your data sources, process the large volume of data quickly and provide meaningful results. Empowering your organisation with data analytics helps make management easier and better because you can make more effective decisions anytime. This positions your brand to solve customers’ problems with sustainable solutions that meet and exceed their expectations.
Whether you are the executive manager, chief data officer, data analyst or find yourself in-between the data decision-making, you are responsible for your company’s success. Learn how to apply data analytics tools to maximise your business data, grow a sustainable brand and enjoy maximum ROI.
Focus On Data as a Sustainable Business Priority
You already know that without data, there is no need for analytics tools. The right data must be available before you think of incorporating analytics tools to transform these data and generate profitable insights. Focus on data as one of your business priorities. You can have many KPIs to monitor while managing your company, but make data collection, processing, visualisation, and implementation your number one priority.
Prioritising data in your business comes with many benefits, such as cutting the cost of managing your brand while increasing sales and ROI simultaneously. The benefits of data prioritisation in building sustainable business growth are becoming more apparent each day in an era of Big Data, AI, and ML.
Prioritising business data enhances collaboration and ensures optimum ROI
As McKinsey reports, data-driven organisations become ten times more productive and efficient in serving customers and meeting their needs. This is because, with the correct data and analytics, you can save more time and spend your resources providing optimum solutions to customers. Instead of being distracted by unnecessary business bottlenecks here and there.
Focusing on data in your company enables you to compete with big brands, especially if you’re a startup. It gives your business the right insights to have the competitive edge required to overtake the big players in your niche.
“Being data-centric helps businesses to reduce risks significantly”, says Dr Stylianos Kampakis, a data and blockchain consultant with The Data Scientist. Data-driven brands are far better and more progressive than their lagging peers. Not only does data empower your team to make decisions presently, but it also enables you to see imminent future dangers. And thwart them. Because if data inspire your business decision-making, you can implement sales forecasts to discover your customers’ yearnings, consumer behaviour and market trends.
When equipped with the right insights, you can now tailor your products, services, and solutions to meet the most critical needs of your customers. Robust data analytics tools can go to the extent of learning your clients’ shopping lifestyles to predict future needs and suggest the best solution when needed.
Especially if you are the centre of your company’s decision-making, don’t ignore the vital role of data. There is no limit to your success if you focus on deriving practical insights from data. Businesses who realised the rewards from maximising data to grow and sustain their companies are reaping huge returns now. So, why can’t you?
Use Data to Add Value to Customers – “value before money.”
What is the essence of focusing on data in your business if not to add value to your customers and business? Implementing effective data strategy and analytics into your decision-making process should gear towards bettering the lives of your audience, who rely daily on your products or services.
Provide solutions that empower your customers and meet their needs
Focus on providing lasting solutions first, not making more money. Do you work hard building a solid business because you want to make money and become rich? Acquire the costliest assets and become a celebrity brand. That could be wrong, especially in this modern competitive business landscape where your competitors are looking for the magic wand. The tiniest opportunity that’ll enable them to overtake your brand and lead.
Prioritising financial returns and ROI in your business is not actually the right thing if you’re yet to build a world-class brand. Generating returns can be your second objective but not the first. Concentrate on meeting your customer’s needs and set it as a primary business goal. Discover their pain points and customise solutions that meet and exceed their expectations. In this way, your reputation grows beyond your imagination.
Technology at your fingertips
Tesseract Academy
When you ask organisations like Tesseract Academy how it has grown sustainably over the past few years and reached a broader audience – it’s by adding value to customers, clients, and businesses who enjoy our services.
At Tesseract Academy, we help decision-makers implement and understand technology faster, easier, and better at no extra cost. Our focus is to help businesses like yours maximise data and grow sustainably. We prioritise data and focus on adding value to you and your company.
From AI to blockchain, data science to machine learning and IoT, project management to product development, organisational culture and everything in between. Our top-notch data strategy solutions can help transform the way you do business for the better. We are enabling you to achieve 100% efficiency in your organisation.
How can Tesseract Academy enhance your decision-making using data?
We combine live workshops, consulting, courses, webinars, and free frameworks to support your data-driven decision-making processes.
Whether you are an individual interested in boosting your career or a business leader seeking optimal solutions for your company, we have free and affordable courses to help enhance your understanding of modern technology. Our premium consulting solutions have helped many SMEs and startups become successful and reach their objectives. You, too, can seize this opportunity to learn from the masters.
As a leading brand using online-assisted learning and coaching, we focus on empowering you and your team to maximise data. Not only do Tesseract Academy empower you with the proper knowledge, but our experts also guide you one-on-one until you realise your objectives.
Start your journey today towards sustainable business growth in which you maximise data to increase productivity, enhance customer experience, and generate higher ROI
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.
Building a data science product is a lot like building a house from the ground up. There are several data science positions that every data science team should recruit from time to time in order to get the best possible result. Let’s focus on the major five roles coupled with abilities that the greatest data-science teams seek when hiring new members following the data science development lifecycle.
1. Data Scientist
A data scientist is responsible for designing and developing the heart of a data science application. They are in charge of providing business-relevant, actionable insights, and does it by using the capabilities of data analytics. Data scientist’s employ a variety of statistical as well as ML approaches to include intellect along with the capacity to learn continuously into solutions. The following abilities are required: Experiential data analysis, ML, statistics, and AI are all skills that data scientists possess. They are frequently familiar with programming languages like R and Python.
2. Data Architect
The function of the data architect is truly important in the building industry. They learn about the ambitions of proprietors as well as assess the viability of land purpose options for them. They build the groundwork for the remainder of the production team to develop by translating customer requirements into architectural drawings and ensure that the house is practical, safe, and environmentally friendly and that it fulfills its promise. If a corporation wants to protect its investment in data science and analytics, it needs an architect like a translator. It is the translator’s job to understand the user’s business needs and aid in the selection of the most appropriate projects. They make the needs understandable for the data science team. To ensure that the final product can be used by customers, their contributions will continue throughout the project and will be crucial. Data translators are subject matter experts with strong analytical skills. Due to their extensive knowledge of statistics, they are exceptional team managers and correspondents. These experts are used to working with spreadsheets like Microsoft Excel.
3. Information Designer
Working in collaboration with the architect and engineers, an interior designer creates a space that is both useful and visually beautiful. They outline the purposes for which the area will be utilized and sketches up preliminary designs, iterating on them in order to generate precise layouts then identify the type of constructing resources to be used in the construction. This job role ensures that the data science mix is both useful as well as visually appealing. The information designer begins by creating mockups and detailed design prototypes, working their way up to the information architecture. They put up the data visions accessible with determining the appropriate kind of graphs, interactivity, as well as graphic design to employ in conjunction with them. The designer is a superb storyteller who uses statistics to convey stories. Knowledge and abilities required: These information design professionals are well-versed in all elements of the interface along with visual layout. They make use of design kits such as Adobe Illustrator, Sketch as well as experimental meditation technologies like PowerBI, to create their designs.
4. Data Engineer
Work environments for data engineers are diverse, but they always revolve around the development of systems that gather, handle, and turn raw data into useable information that is then interpreted by data scientists and business analysts. In the end, they want to make data easily available so that enterprises may use it to analyze and enhance their own performance and efficiency. Data engineering is not usually considered an entry-level position. Many data engineers, on the other hand, begin their careers as software engineers or business intelligence analysts.
5. Construction Manager/Data Science Manager
A Construction Manager is in charge of overseeing the project and ensuring that all pledges made to the homeowners are met. They are in charge of the schedules, ensure that the quality is maintained, and controls the funds. Their responsibility is to guarantee that all positions not only carry out their obligations but also work effectively together as a team. They deal with workplace concerns, maintain employee morale, and ensure that the workplace is safe. The same is true for a Data Science Manager who oversees a data science team and is responsible for getting all of the jobs organized while also enabling them to do their finest work. They follow through on all client obligations as well as keeps all lines of communication open. Data manager’s make certain that high-quality products are delivered on schedule. Their primary responsibilities include change management and ensuring business users are on board with the solution when it is implemented. Managers of data science projects need to be capable of managing change as well as excellent project managers. Data scientists need a thorough perception of both corporate assessment and data science methodologies. To achieve their objectives, they make use of project management tools like Microsoft Project.
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
The degree to which an organization makes use of the data that it generates is referred to as data maturity. The more they do with their data, the more data mature they are, and as a result, the higher they rank on the maturity scale. Therefore, an organization that employs modern business intelligence and analytics tools to analyze its data may be deemed significantly more mature than an organization that depends only on spreadsheets to carry out reporting tasks can be considered far more mature. Many analysis and data pioneers who are now dominating in their professions are wonderful examples of extensive data maturity. The data-driven approach used by firms such as Airbnb, Uber, and Netflix have resulted in the designation of data companies rather than conventional rivals in the hospitality, transportation, and entertainment sectors being more appropriate terminology.
Business data maturity may be divided into five levels
1. Introduction To business Because there are no formal BI & Analytics tools or standards in place to enable this, reporting is restricted to activities that are important for business operations. Spreadsheets are used as the main reporting tool, and reporting is confined to tasks that are critical for business operations.
2. Detailed Description BI & Analytics are still in the early phases of deployment and are used to generate reports on activity levels and trends.
3. Consideration BI & Analytics are used not just to report on what is occurring, but also to prepare for the future, via the use of tools such as scenario planning.
4. Predictive Analytics Analytics is used to anticipate what will happen in the next 5, 10, or even twenty years, as well as to identify the primary drivers of current and future trending patterns.
5. Prescriptive Guidelines Users will no longer be required to enter variables into the system in order to forecast future results. Instead, Machine Learning and Artificial Intelligence make it possible to discover concerns before they are ever taken into consideration by decision-makers.
Importance of data integrity
Every organization generates data, and it is a valuable resource. It comes from a variety of sources, including the web, our mobile phones, payment systems, surveys or social media. The use of data is becoming more important as a business asset for organizations, and it has even been referred to as the “money of the twenty-first century”. However, it is not merely possessing data that makes it an asset; it is what we do with it that determines its value. And it is at this point that the concept of data maturity is brought into play.
The Data maturity framework
The Data Science Maturity Framework is a 5-level framework that helps you understand the maturity of your organization’s data science capabilities. This framework was designed by Sameer Rahman (voted in the top 100 data scientists in the UK) and the Tesseract Academy. You can access it via the free frameworks section on our website. Also feel free to get in touch if you have any questions.
Implications of AI For Project Management + What You Need To Know About Data Products
The rapid advancements in Artificial Intelligence (AI) play a critical role in different business settings. So it comes as no surprise that various tasks related to Project Management are also being transformed with the help of AI.
AI can be used to automate many project management tasks such as scheduling, resource allocation, and risk identification. This is done with the help of AI project management tools that are are now available to help us plan and execute our projects in an efficient and effective manner.
One of the most common uses of AI in project management is scheduling. It helps in predicting how long it will take to complete a task and what resources are required for its completion. AI helps in resource allocation by allocating resources based on their skillsets, availability and location. It also helps in risk identification by predicting possible risks before they occur so that they can be mitigated before they become a problem.
Here are three major implications of AI and how it can transform Project Management at any organization.
1) Role of AI in Administrative Tasks:
Administrative tasks and functions like planning, meeting, and daily updates can be given to AI tools for improved management. A report by KPMG showed a 15% improvement in productivity when organizations used AI tools.
2) Latest AI Systems Can Help Keep Projects Within Budget:
Project Managers required various data and calculations to estimate budget and time durations for projects in the past. But with AI-powered analysis tools, it has become relatively simple to make future projections. Now Project Managers can make decisions regarding future projects with more confidence.
3) Collect Unique Insights From Projects:
A key advantage of using AI technology in project management is that unique insights collected from the data will help address different risks involved in projects. These insights can further help in improving the decision-making capabilities of Project Managers.
One of the fastest ways to increase a company’s valuation is through data products. Unlocking data products can confer multiple benefits such as:
Additional monetisation streams.
Increased valuation.
Building unique competitive advantages.
Who is this for?
This lesson is perfect for an executive or entrepreneur of a startup or a scale-up who wants to understand how data science can positively affect a valuation. This is extremely valuable for those who are fundraising, or are in a very competitive marketplace, and are looking for new monetisation streams for their business.
I was reading recently a very interesting article on O’Reilly’s blog: Designing great data products. I thought it would be good to summarise it and add some of my own thoughts, since it touches upon some of the themes that I am also covering in my work.
The blog discusses the issue of using data science to create products. One of the main issues in the design of data products, is that data scientists, quite often, do not have good business understanding. At the same time, people responsible for coming up with the products, e.g. product managers, might not be very familiar with the possibilities and limitations of machine learning.
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.
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 is going to talk about this framework, which can be applied in AI, but also related disciplines such as software development
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.
On this webinar we discuss topics such as:
What is data strategy?
Why is it important?
What can a company do to best deal with data strategy in the era of big data?
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.
