Como você pode tornar os dados específicos do domínio acessíveis e utilizáveis para as partes interessadas?

To enhance data accessibility and utility for stakeholders, the initial step involves comprehending both the domain and the stakeholders. This entails delving into their objectives, requirements, challenges, and expectations, along with identifying key concepts, terms, and metrics relevant to them. Understanding how stakeholders communicate and consume data is crucial. Employing methods like interviews, surveys, personas, user stories, or scenarios aids in gathering this pertinent information. This comprehensive understanding of the domain and stakeholders enables the customization of data analysis to align seamlessly with their context and perspectives, fostering more meaningful and effective insights.

Catering required data for a stakeholders starts with understanding the need and purpose. Building a data supply mechanism with giving priority to the user's perspective is more primitive along with Standardization and dexterity of the data given to satisfy the purpose.

Simplifying the concept is so useful especially if you are trying to get investments for your project. Storytelling with clear messages with insights is what investors are looking for. You will have few minutes to show the insight so that everyone understands.

Provide stakeholders with user-friendly tools and platforms to access the data. This could be through a web-based dashboard, a mobile app, or using analytical tools such Tableau or Power BI , depending on their needs and tech proficiency.

It is necessary to define the technological tools that need to be used for data extraction. Know its pros and cons to be able to obtain the best quality of data in the best possible time.

Following stakeholder understanding, the subsequent step involves outlining data requirements and scope for domain-specific analysis. This encompasses identifying key questions or hypotheses to be addressed with the data, specifying requisite data sources, types, formats, and quality standards. Determining the volume of data for collection, storage, and analysis is vital, alongside considering ethical, legal, and security implications. Utilizing tools like data dictionaries, models, quality frameworks, or governance policies aids in precisely defining and documenting these data requirements and scope. This approach ensures clarity in objectives, adherence to standards, and consideration of ethical and legal considerations in this process.

Starting with industry standard domain models is a great way to start defining what the data model for curated layer is going to be. With that end in mind, starting with all the data sources and ensuring that the necessary data is available in them and is of quality that is sufficient is the next step. If this is not the case, reset with stakeholders on what is possible immediately and what the next steps or evolution look like. Once this is complete, we can march on with the clean and transform steps.

At ICity, defining data requirements for our e-commerce project was key. My experience showed the importance of clarity in data scope, focusing on customer behaviors, inventory levels, and sales trends. Using data models, we outlined necessary data types and sources, aligning them with our platform capabilities. We concentrated on user browsing habits, purchase histories, and product performance. This streamlined approach boosted our predictive analytics, improving inventory management and marketing strategies. The result? Enhanced sales efficiency and customer satisfaction.

If one is purposeful at the first step, defining the requirements and scope is a given. In my opinion, breaking these into 2 separate steps is unnecessary.

Utilizing a variety of tools such as Python, R or SQL, this step ensures that the data aligns precisely with the requirements and scope defined earlier. By doing so, analysts can guarantee the accuracy, completeness and consistency of the data, laying a robust foundation for domain-specific analyses that resonate with stakeholders' needs and expectations.

In the third step, the focus is on cleaning and transforming data to align with domain-specific requirements and scope. Addressing data issues, including missing values, outliers, duplicates, errors, and inconsistencies, is paramount. Additionally, data transformations such as filtering, aggregating, joining, splitting, or reshaping are applied. Various software tools, such as Excel, Python, R, or SQL, prove instrumental in executing these tasks. The ultimate aim is to guarantee data accuracy, completeness, and consistency, aligning the dataset precisely with the demands of domain-specific analysis.

At iCity, managing Domain Based Platform updates, I tackled the challenge of cleaning and transforming our extensive product data. Ecommerce data often has inconsistencies, especially in product descriptions and categories. I found using Python scripts for data cleansing extremely effective. This process automated the removal of duplicates, standardized naming conventions, and corrected errors. We also restructured data to align with our analytica l models. This approach significantly improved data quality, enhancing product management strategies and customer experience on the platform.

If you inherit the data, cleaning and transforming will be your biggest headache. Purpose driven data entry seems to be a non-existent concept in reality. Every single data set I've inherited has kept me at this step for the longest. It's important to take your time with this step. After this, the effort becomes a lot more straight forward.

Enabling users to access centralized reporting is crucial for standardizing key metrics and offering a unified view of business results. However, central teams may be insufficiently sized to address all use cases, particularly use cases close to daily operations. Therefore, it becomes important to extend access to datasets across the organization, empowering more users to conduct their analyses and generate reports and visualisagtion that contribute to supporting day-to-day operations.

The concluding step centers on visualizing and communicating data in a manner that is clear, engaging, and actionable for stakeholders. This entails selecting appropriate charts, graphs, maps, or dashboards to convey data insights effectively. Additionally, it involves incorporating labels, titles, legends, annotations, or narratives to elucidate the data story and recommendations. Tools like Tableau, Power BI, or Google Data Studio prove invaluable for creating and sharing these data visualizations. The ultimate objective is to render data easily comprehensible and usable for stakeholders, facilitating informed decision-making and meaningful engagement with the insights provided.

At cayan Academy, I led a BI project where effective data visualization in e-commerce was key. Stakeholders often found raw data challenging, so using Power BI to create intuitive dashboards was crucial. We visualized sales data, customer demographics, and product performance, making complex insights accessible to non-technical users. This approach helped identify underperforming products, informing targeted marketing strategies. The result was increased stakeholder engagement and informed business decisions, boosting sales performance.

Creating power point presentations could be an interesting way to explain what the data is saying to someone that hasn't dived deep into the world of statistical theory. However, in reality, this could become a cure for insomnia. Given the advent large language models (LLMs), it's a wiser move for an analyst to link their data to a LLM in a way that their management can interact with a ChatGPTesque chatbot to ask questions in simple English about what they are trying to gauge where the LLM will output the relevant metrics along with any desired explanations. Why use the same old dashboards and charts when you can get them to ask a chatbot any question their heart may desire?

Data is always in need of its context and background so as to process it to get meaningful actionable points. Making each stakeholder informed about his corresponding relation with why,what and how they have to perceive the data is inevitable along with making data accessible in the form and unit that the stake holder need.

Ensuring domain-specific data is accessible and usable for stakeholders hinges on timely and appropriate data delivery. This involves aligning DataOps practices with the unique needs of each stakeholder, providing them with relevant, tailored data when they need it most for informed decision-making.

Purpose driven work leads to many automation possibilities. Automations save time. This is one reason why purpose driven people make the big bucks.