OpenAI has unveiled a groundbreaking new feature, the Code Interpreter, accessible to all ChatGPT Plus users. Check out my experiments using the 2739 edition of BRPTO’s Patent Gazette

Code Interpreter is an innovative extension of ChatGPT, now available to all subscribers of the ChatGPT Plus service. This tool boasts the ability to execute code, work with uploaded files, analyze data, create charts, edit files, and carry out mathematical computations. The implications of this are profound, not just for academics and coders, but for anyone looking to streamline their research processes. Code Interpreter transcends the traditional scope of AI assistants, which have primarily been limited to generating text responses. It leverages large language models, the AI technology underpinning ChatGPT, to provide a general-purpose toolbox for problem-solving.

What is the Code Interpreter?

The Code Interpreter Plugin for ChatGPT is a multifaceted addition that provides the AI chatbot with the capacity to handle data and perform a broad range of tasks. This plugin equips ChatGPT with the ability to generate and implement code in natural language, thereby streamlining data evaluation, file conversions, and more. Pioneering users have experienced its effectiveness in activities like generating GIFs and examining musical preferences. The potential of the Code Interpreter Plugin is enormous, having the capability to revolutionize coding processes and unearth novel uses. By capitalizing on ChatGPT’s capabilities, users can harness the power of this plugin, sparking a voyage of discovery and creativity.

Professor Ethan Mollick from the Wharton School of the University of Pennsylvania shares his experiences with using the Code Interpreter

Artificial intelligence is rapidly revolutionizing every aspect of our lives, particularly in the world of data analytics and computational tasks. This transition was recently illuminated by Wharton Professor Ethan Mollick who commented, “Things that took me weeks to master in my PhD were completed in seconds by the AI.” This is not just a statement about time saved or operational efficiency, but it speaks volumes about the growing capabilities of AI technologies, specifically OpenAI’s new tool for ChatGPT – Code Interpreter.

Mollick, an early adopter of AI and an esteemed academic at the Wharton School of the University of Pennsylvania lauded Code Interpreter as the most significant application of AI in the sphere of complex knowledge work. Not only does it complete intricate tasks in record time, but Mollick also noticed fewer errors than those typically expected from human analysts.

One might argue that Code Interpreter transcends the traditional scope of AI assistants, which have primarily been limited to generating text responses. It leverages large language models, the AI technology underpinning ChatGPT, to provide a general-purpose toolbox for problem-solving.

Mollick commended Code Interpreter’s use of Python, a versatile programming language known for its application in software building and data analysis. He pointed out that it closes some of the gaps in language models as the output is not entirely text-based. The code is processed through Python, which promptly flags any errors.

In practice, when given a dataset on superheroes, Code Interpreter could clean and merge the data seamlessly, with an admirable effort to maintain accuracy. This process would have been an arduous task otherwise. Additionally, it allows a back-and-forth interaction during data visualization, accommodating various alterations and enhancements.

Remarkably, Code Interpreter doesn’t just perform pre-set analyses but recommends pertinent analytical approaches. For instance, it conducted predictive modeling to anticipate a hero’s potential powers based on other factors. Mollick was struck by the AI’s human-like reasoning about data, noting the AI’s observation that the powers were often visually noticeable as they derived from the comic book medium.

Beyond its technical capabilities, Code Interpreter democratizes access to complex data analysis, making it accessible to more people, thereby transforming the future of work. It saves time and reduces the tedium of repetitive tasks, enabling individuals to focus on more fulfilling, in-depth work.

Here are 10 examples of how you can use Code Interpreter for data analysis:

  1. Analyzing customer feedback data to identify trends and patterns.
  2. Creating interactive dashboards and reports for business intelligence purposes.
  3. Cleaning and transforming datasets for machine learning models.
  4. Extracting insights from social media data to inform marketing strategies.
  5. Generating charts and graphs to visualize sales data.
  6. Analyzing website traffic data to optimize the user experience.
  7. Creating custom functions and scripts for specific data analysis tasks.
  8. Performing statistical analysis on survey data.
  9. Automating repetitive data analysis tasks with Python scripts.
  10. Creating custom visualizations for presentations and reports.

How to use it? Follow my own experiments

Initially, you must upload a file of your choice. Following this, you are permitted to pose queries concerning it. The interpreter for the code will generate and run a Python script to address nearly all inquiries you have. The entire process is incredibly swift. I conducted a few trials using the XML file from BRTPTO’s 2739 Patent Gazette. The experience was truly incredible. There’s no need for any understanding of coding or Python. The code interpreter simply provides the results. If you wish, you can view the Python script.

By selecting the “Show Work” option, the Python script will become visible to you.

He executed the work flawlessly!

This is the screen of the CSV file.

Here is the Python code generated by the Code Interpreter to create the CSV file:

import pandas as pd
import xml.etree.ElementTree as ET

# Load the XML file
tree = ET.parse("/mnt/data/Patente_2739_04072023.xml")
root = tree.getroot()

# Function to extract data from XML into a dictionary
def extract_data(root):
    data = []
    for despacho in root.findall('despacho'):
        row = {}
        row['despacho_codigo'] = despacho.find('codigo').text
        row['despacho_titulo'] = despacho.find('titulo').text
        row['despacho_comentario'] = despacho.find('comentario').text if despacho.find('comentario') is not None else ""
        processo_patente = despacho.find('processo-patente')
        row['processo_numero'] = processo_patente.find('numero').text
        row['processo_data_deposito'] = processo_patente.find('data-deposito').text if processo_patente.find('data-deposito') is not None else ""
        row['processo_concessao'] = processo_patente.find('concessao').text if processo_patente.find('concessao') is not None else ""
        titulars = processo_patente.findall('titular-lista/titular')
        row['titular_nome_completo'] = "; ".join([titular.find('nome-completo').text for titular in titulars])
        row['titular_endereco_pais'] = "; ".join([titular.find('endereco/pais/sigla').text for titular in titulars])
    return data

# Extract data from XML
data = extract_data(root)

# Convert to DataFrame
df = pd.DataFrame(data)

# Save DataFrame to CSV
df.to_csv('/mnt/data/patente.csv', index=False)

df.head()  # Display the first few rows of the DataFrame

# Extract distinct application/patent numbers
distinct_application_numbers = df['processo_numero'].unique()

# Convert to DataFrame
df_distinct_application_numbers = pd.DataFrame(distinct_application_numbers, columns=['processo_numero'])

# Save DataFrame to CSV
df_distinct_application_numbers.to_csv('/mnt/data/distinct_application_numbers.csv', index=False)

df_distinct_application_numbers.head()  # Display the first few rows of the DataFrame

This video can demonstrate the capabilities of the Code Interpreter.

You can find more information on the official Open AI site by clicking here.


Code Interpreter is a powerful tool that is making data analysis accessible for everyone with ChatGPT Plus. By allowing users to run code snippets within their chat sessions, it enables them to perform a wide range of data analysis tasks quickly and easily. Whether you’re analyzing customer feedback data or creating custom visualizations for presentations and reports, Code Interpreter has something to offer everyone.

Code Interpreter invites us to consider how we can leverage such advancements across various sectors impacted by AI. Indeed, Code Interpreter signifies the dawn of a new era in artificial intelligence and computational capabilities. So why not give it a try today?

That’s it for today!


Wharton professor sees future of work in new ChatGPT tool | Fortune

The Future of Data Analytics: An Introduction to Microsoft Fabric

Microsoft Fabric, launched on May 24-25 of 2023 at the Microsoft Build event, is an end-to-end data and analytics platform that combines Microsoft’s OneLake data lake, Power BI, Azure Synapse, and Azure Data Factory into a unified software as a service (SaaS) platform. It’s a one-stop solution designed to serve various data professionals including data engineers, data warehousing professionals, data scientists, data analysts, and business users, enabling them to collaborate effectively within the platform to foster a healthy data culture across their organizations​​.

What are the Microsoft Fabric key features?

Data Factory – Microsoft’s Azure Data Factory is a powerful tool that combines the simplicity of Power Query with Azure Data Factory’s scale. It provides over 200 native connectors for data linkage from on-premises and cloud-based sources. Data Factory enables the scheduling and orchestration of notebooks and Spark jobs.

Data Engineering – Leveraging the extensive capabilities of Spark, data engineering in Microsoft Fabric provides premier authoring experiences and facilitates large-scale data transformations. It plays a crucial role in democratizing data through the lakehouse model. Moreover, integration with

Data Science – The data science capability in Microsoft Fabric aids in building, deploying, and operationalizing machine learning models within the Fabric framework. It interacts with Azure Machine Learning for built-in experiment tracking and model registry, empowering data scientists to enhance organizational data with predictions that business analysts can incorporate into their BI reports, thereby transitioning from descriptive to predictive insights.

Data Warehouse – The data warehousing component of Microsoft Fabric offers top-tier SQL performance and scalability. It features a full separation of computing and storage for independent scaling and native data storage in the open Delta Lake format.

Real-Time Analytics – Observational data, acquired from diverse sources like apps, IoT devices, human interactions, and more, represents the fastest-growing data category. This semi-structured, high-volume data, often in JSON or Text format with varying schemas, presents challenges for conventional data warehousing platforms. However, Microsoft Fabric’s Real-Time Analytics offers a superior solution for analyzing such data.

Power BI – Recognised as a leading Business Intelligence platform worldwide, Power BI in Microsoft Fabric enables business owners to access all Fabric data swiftly and intuitively for data-driven decision-making.

What are the Advantages of Microsoft Fabric?

Unified Platform: Microsoft Fabric provides a unified platform for different data analytics workloads such as data integration, engineering, warehousing, data science, real-time analytics, and business intelligence. This can foster a well-functioning data culture across the organization as data engineers, warehousing professionals, data scientists, data analysts, and business users can collaborate within Fabric​​.

Multi-cloud Support: Fabric is designed with a multi-cloud approach in mind, with support for data in Amazon S3 and (soon) Google Cloud Platform. This means that users are not restricted to using data only from Microsoft’s ecosystem, providing flexibility​.

Accessibility: Microsoft Fabric is currently available in public preview, and anyone can try the service without providing their credit card information. Starting from July 1, Fabric will be enabled for all Power BI tenants​.

AI Integration: The private preview of Copilot in Power BI will combine advanced generative AI with data, enabling users to simply describe the insights they need or ask a question about their data, and Copilot will analyze and pull the correct data into a report, turning data into actionable insights instantly​​.

Microsoft Fabric – Licensing and Pricing

Microsoft Fabric capacities are available for purchase in the Azure portal. These capacities provide the compute resources for all the experiences in Fabric from the Data Factory to ingest and transform to Data Engineering, Data Science, Data Warehouse, Real-Time Analytics, and all the way to Power BI for data visualization. A single capacity can power all workloads concurrently and does not need to be pre-allocated across the workloads. Moreover, a single capacity can be shared among multiple users and projects, without any limitations on the number of workspaces or creators that can utilize it.

To gain access to Microsoft Fabric, you have three options:

  1. Leverage your existing Power BI Premium subscription by turning on the Fabric preview switch. All Power BI Premium capacities can instantly power all the Fabric workloads with no additional action required. If you already have a Power BI Premium subscription, you can simply turn on the Fabric preview switch. This means you can enable Microsoft Fabric’s capabilities as part of your existing Power BI Premium subscription without having to do anything else. All the capacities you have with your Power BI Premium subscription can be used to power the full range of workloads in Microsoft Fabric. In other words, you can use your existing Power BI Premium resources to run all of the data and analytics tasks that Microsoft Fabric can handle.
  2. Start a Fabric trial if your tenant supports trials. If you’re not sure about committing to Microsoft Fabric yet, you can start a trial if your tenant (an instance of Azure Active Directory) supports it. A trial allows you to test the service before deciding to purchase. During the trial period, you can explore the full capabilities of Microsoft Fabric, such as data ingestion, data transformation, data engineering, data science, data warehouse operations, real-time analytics, and data visualization with Power BI.
  3. Purchase a Fabric pay-as-you-go capacity from the Azure portal. If you decide that Microsoft Fabric suits your needs and you don’t have a Power BI Premium subscription, you can directly purchase a Fabric capacity on a pay-as-you-go basis from the Azure portal. The pay-as-you-go model is flexible because it allows you to pay for only the compute and storage resources you use. Microsoft Fabric capacities come in different sizes, from F2 to F2048, representing 2 – 2048 Capacity Units (CU). Your bill will be determined by the amount of computing you provision (i.e., the size of the capacity you choose) and the amount of storage you use in OneLake, the data lake built into Microsoft Fabric. This model also allows you to easily scale your capacities up and down to adjust their computing power, and even pause your capacities when not in use to save on your bills​​.

Microsoft Fabric is a unified product for all your data and analytics workloads. Rather than provisioning and managing separate compute for each workload, with Fabric, your bill is determined by two variables: the amount of compute you provision and the amount of storage you use.

Follow the capacities that you can buy in the Azure portal:

Check out this video from Guy and Cube which breaks down the details on pricing and licensing.

How to activate the Microsoft Fabric Trial version?

Step 1

Login to Microsoft Power BI with your Developer Account

You will observe that asides from the OneLake icon at the top left, everything looks normal if you are familiar with Power BI Service.

Step 2

Enable Microsoft Fabric for your Tenant

Your Screen will Look like this

So far, we’ve only enabled Microsoft Fabric at the tenant level. This doesn’t give full access to Fabric resources as can be seen in the illustration below

So, Let’s upgrade the Power BI License to Microsoft Fabric Trial

For a smoother experience, You should create a new Workspace and add Microsoft Fabric Trial License as can be seen below

As you can see, while creating a new Workspace, you can now Assign Fabric Trial License to it. Upon creation, we are able to take full advantage of Microsoft Fabric

This video by Guy and Cube explains the steps for getting the Microsoft Fabric Trial.


Microsoft Fabric is currently in preview but already represents a significant advancement in the field of data and analytics, offering a unified platform that brings together various tools and services. It enables a smooth and collaborative experience for a variety of data professionals, fostering a data-driven culture within organizations. Let´s wait for the next steps from Microsoft.

That’s it for today!

Implementing Data Governance in Power BI: A Step-by-Step Guide

As data plays a crucial role in decision-making and data-driven insights, organizations require a robust data governance framework to manage and monitor their data assets. Power BI offers various features and tools that aid in implementing data governance and ensuring data accuracy, reliability, and security.

As data becomes increasingly critical to organizations of all sizes and industries, managing this data effectively and securely becomes just as important. A crucial aspect of data management is data governance, which is defining and enforcing policies, procedures, and standards for data management. This article will explore data governance basics, how to implement it in Power BI, and the advantages of using Power BI Premium.

What is Data Governance?

Data governance is the set of processes, policies, and standards organizations use to manage their data effectively. It encompasses everything from data quality and security to data privacy and retention. Effective data governance is crucial for organizations to ensure that their data is accurate, secure, and accessible. In addition, it helps organizations make informed decisions, reduce risks associated with poor data quality, and maintain compliance with legal and regulatory requirements.

How to Implement Data Governance in Power BI

Power BI provides various features and tools to help implement data governance. These include Dataflows, Datamarts, Sensitivity labels, Endorsement, Discovery, and Row-Level-Security(RLS). Dataflows allow organizations to connect, clean, and transform data, while Datamarts provide a centralized data repository. Sensitivity labels help to classify and protect sensitive data, while Endorsement allows organizations to enforce data quality standards. Finally, Discovery helps organizations manage, monitor, and understand their data assets. Let’s explain each of them.


dataflow is a collection of tables created and managed in workspaces in the Power BI service. A table is a set of columns used to store data, much like a table within a database. You can add and edit tables in your dataflow and manage data refresh schedules directly from the workspace in which your dataflow was created.

As data volume grows, so does the challenge of wrangling that data into well-formed, actionable information. We want data ready for analytics to populate visuals, reports, and dashboards, so we can quickly turn our volumes of data into actionable insights. With self-service data prep for big data in Power BI, you can go from data to Power BI insights with just a few actions.

When to use dataflows

Dataflows are designed to support the following scenarios:

Create reusable transformation logic that many datasets and reports inside Power BI can share. Dataflows promote the reusability of the underlying data elements, preventing the need to create separate connections with your cloud or on-premises data sources.

Expose the data in your Azure Data Lake Gen 2 storage, enabling you to connect other Azure services to the raw underlying data.

Create a single source of truth by forcing analysts to connect to the dataflows rather than connecting to the underlying systems. This single source gives you control over which data is accessed and how data is exposed to report creators. You can also map the data to industry standard definitions, enabling you to create tidy curated views, which can work with other services and products in the Power Platform.

Suppose you want to work with large data volumes and perform ETL at scale; dataflows with Power BI Premium scale more efficiently and give you more flexibility. Dataflows support a wide range of cloud and on-premises sources.

Prevent analysts from having direct access to the underlying data source. Since report creators can build on top of dataflows, it might be more convenient for you to allow access to underlying data sources only to a few individuals and then provide access to the dataflows for analysts to build on. This approach reduces the load to the underlying systems and gives administrators finer control of when the systems get loaded from refreshes.

    You can use Power BI Desktop and the Power BI service with dataflows to create datasets, reports, dashboards, and apps that use the Common Data Model. You can gain deep insights into your business activities from these resources. Dataflow refresh scheduling is managed directly from the workspace in which your dataflow was created, just like your datasets.

    Click here to learn how to create a Dataflow in Power BI.


    Datamarts are self-service analytics solutions that enable users to store and explore data in a fully managed database.

    When to use Datamarts

    Datamarts are targeted toward interactive data workloads for self-service scenarios. For example, suppose you’re working in accounting or finance. In that case, you can build your data models and collections, which you can then use to self-serve business questions and answers through T-SQL and visual query experiences. In addition, you can still use those data collections for more traditional Power BI reporting experiences. Datamarts are recommended for customers who need domain-oriented, decentralized data ownership and architecture, such as users who need data as a product or a self-service data platform.

    Datamarts are designed to support the following scenarios:

    Departmental self-service data: Centralize small to moderate data volume (approximately 100 GB) in a self-service fully managed SQL database. Datamarts enable you to designate a single store for self-service departmental downstream reporting needs (such as Excel, Power BI reports, and others), thereby reducing the infrastructure in self-service solutions.

    Relational database analytics with Power BI: Access a datamart’s data using external SQL clients. Azure Synapse and other services/tools that use T-SQL can also use datamarts in Power BI.

    End-to-end semantic models: Enable Power BI creators to build end-to-end solutions without dependencies on other tooling or IT teams. Datamarts eliminates managing orchestration between dataflows and datasets through auto-generated datasets while providing visual experiences for querying data and ad-hoc analysis, all backed by Azure SQL DB.

    Click here if you want to know how to create a Datamart.

    Sensitivity labels

    A Sensitivity label is an information icon that users can apply in the Power BI Desktop or the Power BI Service. They are essentially digital stamps that can be applied to a resource to classify and restrict critical content when shared outside Power BI.

    Click here if you want more information about implementing sensitivity labels.


    Power BI provides two ways to endorse your valuable, high-quality content to increase its visibility: promotion and certification.
    Promotion: Promotion is a way to highlight the content you think is valuable and worthwhile for others to use. It encourages the collaborative use and spread of content within an organization.
    Any content owner and member with write permissions on the workspace where the content is located can promote the content when they think it’s good enough for sharing.
    Certification: Certification means that the content meets the organization’s quality standards and can be regarded as reliable, authoritative, and ready for use.
    Only authorized reviewers (defined by the Power BI administrator) can certify content. Content owners who wish to see their content certified and are not authorized to certify it themselves must follow their organization’s guidelines about getting their content certified.

    Click here to learn how to endorse your content in Power BI.

    Dataset Discovery

    The Power BI dataset discovery hub empowers Power BI and Microsoft Teams users to discover and re-use organizational and curated datasets and answer their business questions in Power BI or Excel. The hub will empower data owners to manage their assets in a central location.

    Click here to learn more about dataset discovery.

    Row-Level-Security (RLS)

    Row-level security (RLS) with Power BI can be used to restrict data access for given users. Filters restrict data access at the row level, and you can define filters within roles. In the Power BI service, members of a workspace have access to datasets in the workspace. RLS doesn’t restrict this data access.

    Click here to learn more about Row-level security

    What Is Self-Service in Power BI?

    Self-service business intelligence (BI) is a data analytics method that allows business users (e.g., business analysts, managers, and executives) to access and explore datasets without experience in BI, data mining, and statistical analysis. Users can run queries and customize data visualization, dashboards, and reports to support real-time data-driven decision-making.

    Power BI offers robust self-service capabilities. You can tap into data from on-premise, and cloud-based data sources (e.g., Dynamics 365, Salesforce, Azure SQL Data Warehouse, Excel, SharePoint), then filter, sort, analyze, and visualize the information without the help of a BI or IT team.

    Using the Power Query experience, business analysts can directly ingest, transform, integrate, and enrich big data in the Power BI web service. The ingested data can then be shared with other users across various Power BI models, reports, and dashboards.

    How vital is Self-Service in Power BI?

    In many businesses, productivity and agility suffer due to a lengthy process for BI-related data requests. For example, when Alice asks Bob a question, Bob has to wait for the BI/IT team to pull the data. This can take several weeks and multiple meetings, slowing the decision-making process.

    But with Power BI self-service, Bob can quickly retrieve real-time data, and Alice can immediately drill down into relevant datasets during the first meeting. This results in a more efficient discussion and a potential solution that can be implemented immediately.

    The significance of Power BI self-service goes beyond just real-time insights, collaboration, and data reuse. It helps business users develop the habit of relying on data when making decisions. Without easy access to data analytics, they may rely on instincts or experience, leading to suboptimal outcomes. But with real-time data at their fingertips, users can make data-driven decisions, establishing a pattern of data-informed decision-making.

    Implementing Effective Data Governance in a Power BI Self-Service Environment

    Data Governance is critical in implementing a self-service culture in Power BI as it provides a framework for defining, maintaining, and enforcing data management policies. The following are critical components of a data governance plan in Power BI:

    1. Data Quality: Define data quality and accuracy standards to ensure that the data used is reliable and trustworthy.
    2. Data Security: Implement security measures to ensure that sensitive data is protected and only accessible by authorized users.
    3. Data Lineage: Define the lineage of the data sources used in Power BI to ensure that the data can be traced back to its source.
    4. Data Ownership: Assign ownership of data sources and ensure that data owners are responsible for maintaining the accuracy of their data.
    5. Data Stewardship: Designate data stewards responsible for maintaining data quality and ensuring compliance with data management policies.
    6. Data Access Control: Implement access controls to ensure that only authorized users can access sensitive data.
    7. Data Auditing: Implement auditing and monitoring processes to track changes to the data and ensure compliance with data management policies.

    By implementing these key components, organizations can establish a strong foundation for a self-service culture in Power BI while ensuring that the data is secure, accurate, and trustworthy.

    Maximizing Your Data Governance with Power BI Premium

    From scalability to security, Power BI Premium offers a range of features that can help organizations manage their data more effectively. With dedicated capacity, IT departments can ensure consistent performance for their teams. Advanced security features also guarantee data privacy and protection. Follow below the ten advantages of implementing data governance with Power Bi Premium:

    1. Scalability: Power BI Premium can handle large amounts of data and high concurrent usage.
    2. Dedicated Capacity: Dedicated resources for Power BI Premium ensure consistent performance.
    3. IT Governance: IT departments can centrally manage and govern Power BI deployments.
    4. Data Privacy & Security: Advanced security features ensure data privacy and protection.
    5. Shared Workspaces: Teams can collaborate on data and reports in a secure environment.
    6. Unrestricted Data Sources: Power BI Premium supports a broader range of data sources than Power BI Pro.
    7. Dynamic Row-Level Security: Secure access to sensitive data can be managed dynamically.
    8. On-Premises Data Connectivity: Power BI Premium supports connectivity to on-premises data sources.
    9. Long-Term Data Retention: Power BI Premium enables organizations to retain data for extended periods.
    10. Lower TCO: Power BI Premium can provide lower total ownership costs than purchasing individual Power BI Pro licenses.

    10 Effective Strategies for Implementing Data Governance in Power BI

    1. Creating Dataflows for cleaning and transforming data.
    2. Implementing Sensitivity labels to classify and protect sensitive data.
    3. Using Datamarts for centralizing data and improving data management.
    4. Enforcing data quality standards with Endorsement.
    5. Monitoring data assets with Discovery.
    6. Implementing data privacy and security with Power BI Premium.
    7. Improving report refresh times and performance with Power BI Premium.
    8. Sharing reports and dashboards with a larger audience with Power BI Premium.
    9. Utilizing Power BI Premium’s increased capacity for large datasets.
    10. Improving collaboration and data sharing with Power BI Premium’s multi-user authoring feature.

    Video talking about Building a Data Governance Plan for Your Power BI Environment.


    Data governance is an essential aspect of data management, helping organizations to ensure that their data is accurate, secure, and accessible. Power BI provides several features to help organizations implement data governance, including Power BI Premium, dataflows, and Datamarts. With these features, organizations can automate collecting and transforming data, reduce the risk of manual errors, and maintain compliance with legal and regulatory requirements. Whether you’re just starting to explore Power BI or are already using it to manage your data, implementing data governance is a crucial step toward effective data management.

    It’s very interesting to look at the Power BI adoption roadmap.

    Matthew Roche’s Blog from Microsoft is a massive reference to Data Culture and Governance. This guy explains everything about Dataflows here.

    If you have any questions discussed in this post or need help, feel free to contact me at this link.

    That’s it for today!

      These 5 Tech Skills Will Be In Demand In 2023

      With changing technology available in 2023, having a list to show you the top five tech skills in demand maximizes your chances of landing a good job. If you want to stay on the cutting edge of technological changes in the job market, these skills are a must-have to give you an edge over other people applying for jobs.

      A blog article about the top in-demand tech skills for jobs in the future. It briefly describes all five skills and how you can hone them to be more marketable.

      The skills that will have the most significant demand in 2023 are more than computers – an organization’s management has a big say in the skill set that their employees should know, and these skills can change from year to year. Find out what you need to add to your resume if you want to apply for one of the hottest jobs on the market!

      With technology advancing rapidly, the skills needed to succeed in those fields are likewise shifting. And what are those skills? Let’s find out!

      What Will Be Future Jobs In 2023?

      In 2023, the most in-demand jobs will likely be in artificial intelligence (AI), big data, and cloud computing. These three areas are experiencing the most rapid growth and are expected to continue for the foreseeable future.

      AI is already being used in various ways, such as to create personal assistant applications, improve search engine results, and target online ads. The potential uses for AI are virtually limitless, and as its capabilities continue to increase, so will the number of businesses and industries adopting it.

      Big data is another area with a lot of potentials. Companies are just beginning to scratch the surface of what they can do with all the data they collect. Currently, it is mainly used for marketing purposes. Still, it could be used to predict consumer behavior, improve product design, or identify new business opportunities.

      Data Communicator/ Storyteller

      As technology continues to evolve, so do the skills that employers are looking for in their employees. In the coming years, one of the most essential skills in demand is communicating data effectively.

      With the ever-increasing amount of data collected and stored, it is becoming more and more difficult for businesses to make sense of it all. That’s where data communicators come in. Data communicators are experts at taking complex data sets and communicating them in a way that is easy to understand.

      Not only do they need to be able to understand and interpret data, but they also need to be able to tell a story with it. The best data communicators can take data and turn it into an engaging story that can help organizations make better decisions.

      If you have strong communication skills and are interested in working with data, then a career as a data communicator may be right for you!

      Data Analyst: A data analyst analyzes, processes, and interprets data to find trends, patterns, and insights. Data analysts use their skills to help organizations make better decisions by providing them with actionable information.

      Data storytellers use various communicative methods, such as written communication and visualizations, to convey insights. Tools like PowerBI, QlikView, MicroStrategy, Google Data Studio, and Tableau help them find the most effective and accurate ways of conveying information.

      To be a successful data analyst, you must have strong analytical and problem-solving skills. You must also be able to effectively communicate your findings to others. See below some Data Communicator and Storyteller skills.

      Data visualization: Data communicators and storytellers should be skilled in creating visualizations that clearly and effectively communicate data insights. This includes choosing the appropriate chart or graph type, using adequate labeling and formatting, and selecting an appropriate color scheme.

      Writing: Writing clearly and concisely is essential for communicating data insights to a wide range of audiences. This includes explaining complex concepts in simple terms and using appropriate language for the audience.

      Storytelling: Data communicators and storytellers should be skilled in using storytelling techniques to engage and inform their audience. This includes understanding how to structure a story, use compelling narratives to convey data insights, and use visual aids to support the story.

      Presentation skills: Data communicators and storytellers should be skilled in presenting data insights effectively, whether in person or online. This includes understanding how to use visual aids, engage with the audience, and adapt the presentation to different audiences and contexts.

      Data literacy: Understanding and interpreting data is essential for data communicators and storytellers. This includes understanding key concepts such as statistical significance and being able to critically evaluate data sources and methods.

      If you are interested in a career that combines your love of numbers with your communication skills, then a career as a data analyst may be the perfect fit for you!

      UX Design / Web Development

      User experience (UX) design and the closely related field of user interface (UI) design will become increasingly valuable skills as businesses worldwide transform into tech companies. No matter your role on a team, you’re expected to know how to use technology. UX is what makes technology work for everyone, even when they don’t have coding knowledge. This becomes even more important in low-code/no-code environments, where businesses can build applications without hiring an engineer. Enterprises realize that good experiences lead to more engaged customers and employees. This isn’t just a trend that helps designers—it will help business owners retain their customers and make their employees happier going through their daily tasks.

      The field of web development is constantly changing, with new technologies and trends always emerging. But some core skills will always be in demand. If you’re looking to get into web development, or move up in your career, make sure you have these skills:

      1.HTML and CSS: These are the foundation languages of the web. Every website is built with HTML and CSS, so if you want to be a web developer, you need to know them inside out.

      2.JavaScript: JavaScript is a programming language that helps make websites interactive. It’s used to add features like menus, forms, and animations.

      3. Web Standards: Websites must be built using web standards to work correctly on all devices and browsers. This includes proper code structure and formatting, semantic markup, and ensuring your CSS is compatible with different browsers.

      4. Responsive Design: With more people than ever accessing the internet on mobile devices, websites must be designed to be responsive – that is, they look good and work well on any screen size. This means using flexible layouts, media queries, and other techniques to ensure your site looks great on any device.

      5. User Experience (UX): A good user experience is essential for any website or app. As a web developer, you must understand how users interact with websites and design your sites accordingly. This includes things

      Cyber Security

      Cyber security is one of the most in-demand tech skills of the future. With the increasing amount of data being stored and shared online, companies are looking for ways to protect their information from cyber attacks. As a result, the demand for cybersecurity professionals is expected to grow.

      Information extracted from this article.

      Cyber security specialists are responsible for developing and implementing security measures to protect computer networks and systems from unauthorized access or damage. They may also be required to monitor network activity for suspicious activity and respond to incidents when they occur.

      Here are some Cybersecurity skills.

      Network security: Involves protecting networks, devices, and data from unauthorized access or attacks. This includes understanding how to secure networks and devices, as well as how to detect and respond to security threats.

      Security protocols: Cybersecurity professionals should be familiar with various security protocols, including encryption, access control, and authentication, to protect data and systems from cyber threats.

      Risk assessment and management: Cybersecurity professionals need to be able to identify potential security risks and implement strategies to mitigate them. This includes understanding how to conduct risk assessments and develop risk management plans.

      Security incident response: When a security incident occurs, it is important for cybersecurity professionals to respond quickly and effectively. This includes understanding how to identify the cause of an incident, contain it, and restore affected systems.

      Compliance: Cybersecurity professionals must be familiar with relevant laws, regulations, and industry standards to ensure that their organization complies with all relevant requirements. This includes understanding data protection laws and industry-specific regulations.

      To succeed in this field, you must have strong technical skills and be up-to-date on the latest security threats. You will also need to be able to think creatively to develop new solutions to address evolving security challenges.

      Digital Marketing

      Digital marketing is one of the most in-demand tech skills today. With the rise of online marketing and the growth of the digital economy, businesses are increasingly looking for candidates with strong digital marketing skills.

      There are several reasons why digital marketing skills are in high demand. First, the growth of the internet and mobile devices has made it easier for businesses to reach their target audiences through digital channels. Second, as more businesses move into the online space, they need skilled marketers to help them navigate the complex world of digital marketing. Finally, as traditional advertising channels become less effective, businesses are turning to digital marketing to reach their customers and grow their business.

      Many skills are essential for developing a solid foundation in digital marketing. Here are five key skills that can help you succeed in this field:

      Data analysis and interpretation: Digital marketing relies heavily on data to guide strategy and measure the effectiveness of campaigns. Therefore, analyzing and interpreting data accurately is a crucial skill.

      Content creation and management: Compelling, relevant content is crucial for attracting and retaining customers. This includes writing copy for websites and social media and creating visual content such as images and videos.

      SEO: Search engine optimization (SEO) involves optimizing a website and its content to improve its ranking in search engine results pages. This includes researching and using relevant keywords and ensuring that a website is mobile-friendly and has fast loading times.

      Advertising: Digital marketing includes advertising on platforms such as Google and social media. This includes understanding how to create and target ads and measuring their effectiveness.

      Social media marketing: Social media is a powerful tool for connecting with customers and building brand awareness. Developing expertise in social media marketing involves understanding how to create and manage social media profiles and creating and sharing content that resonates with specific audiences.

      If you’re looking to start or enhance your career in tech, developing solid digital marketing skills is a great place to start. Here are some tips to get you started:

      1. Familiarize yourself with different digital marketing channels.
      2. Learn how to create effective campaigns using different digital marketing tools.
      3. Understand how to measure and analyze your results to optimize your campaigns.
      4. Stay up-to-date on the latest trends and technologies in digital marketing.
      5. Get experience by working on projects for real businesses or organizations.

      Artificial Intelligence

      Artificial intelligence plays a crucial role in the skills I mentioned before, specifically the power to work alongside AI in a manner that is commonly described as “augmented working.” Data communicators have tools that suggest the most effective forms of visualization and storytelling to communicate their insights. Cyber security professionals can use AI to analyze network traffic and spot potential attacks before they cause damage. UX designers use AI-assisted user behavior analytics to determine which features and functionality should be emphasized electronically. Finally, digital marketers have many AI tools for predicting audience behavior and developing copy and content.

      In recent years, there has been a lot of hype surrounding artificial intelligence (AI). And with good reason – AI has the potential to revolutionize several industries, from healthcare and finance to manufacturing and logistics.

      But what does AI entail? And what skills do you need to get a job in this field?

      Here’s a quick overview of AI, along with some of the most in-demand AI jobs and skills:

      What is artificial intelligence?

      At its core, artificial intelligence is all about using computers to simulate or carry out human tasks. This can involve anything from understanding natural language and recognizing objects to making decisions and planning actions.

      There are different types of AI, but some of the most common are machine learning, deep learning, natural language processing, and computer vision.

      AI jobs in demand

      As AI continues gaining traction, the demand for AI-related jobs is rising. According to Indeed, job postings for AI roles have increased by 119% since 2015. And LinkedIn’s 2018 Emerging Jobs Report found that roles related to machine learning are among the fastest-growing jobs in the US.

      Some of the most in-demand AI jobs include:

      Data Scientist: A data scientist is a professional responsible for collecting, analyzing, and interpreting large amounts of data to identify trends and patterns. They use statistical methods, machine learning techniques, and domain knowledge to extract valuable insights from data and communicate their findings to stakeholders through reports, presentations, and visualizations.

      Machine Learning Engineer: A machine learning engineer designs, builds and maintains machine learning systems. They work closely with data scientists to understand the requirements of a machine-learning project and use their programming skills to implement and deploy machine-learning models. They may also be responsible for evaluating these models’ performance and making necessary improvements.

      Research Scientist: A research scientist is a professional who conducts research in a particular field, such as computer science, biology, or physics. They may work in academia, government, or industry and use a variety of methods, including experimentation, simulation, and data analysis, to advance the state of knowledge in their field.

      Data Analyst: A data analyst is a professional responsible for collecting, processing, and analyzing data to support decision-making and strategic planning. They may use various tools and techniques, such as SQL, Excel, and statistical software, to manipulate and visualize data and communicate their findings through reports and visualizations.

      Business Intelligence Analyst: A business intelligence analyst is a professional responsible for collecting, analyzing, and interpreting data to support business decision-making. They may use various tools and techniques, such as SQL, Excel, and business intelligence software, to extract and analyze data from various sources and present their findings to stakeholders through reports, dashboards, and visualizations.

      Let’s see the Bernard Marr video on Youtube about these skills.

      Video extract from this Forbes article.


      As the world progresses, so too does the technology we use. It’s crucial to stay ahead of the curve and learn new skills that will be in demand in future years. The skills listed in this article will be in high demand in 2023, so start learning them now! Who knows, you might even be able to get a head start on your competition.

      The tech industry is constantly evolving, so it’s essential to stay ahead of the curve. The skills listed in this article will be in high demand in 2023, so start learning them now! You might even be able to get a head start on your competition.

      As technology rapidly evolves, keeping your skills up-to-date is essential to stay ahead. The five tech skills mentioned in this article will be in high demand in 2023, so if you don’t have them already, now is the time to start learning. With these skills under your belt, you’ll be well-positioned to take advantage of the many opportunities coming your way in the next few years. Do you have any of these tech skills? Are there other skills you think will be in high demand in 2023? Let us know in the comments below!

      That’s it for today!