Business – Tech News & Insights

How to Implementing Agile Practices in Legal Firms

In IT firms, agile practices are commonplace for efficient project management. However, many legal teams are not as familiar with these practices and how they can be used to manage legal projects effectively. In this article, we will explore what agile practices are and how they can be applied to legal management for IT firms.

What is Agile?

Agile is a project management methodology emphasizing flexibility and collaboration in project delivery. Agile aims to break down large projects into smaller, more manageable tasks that can be completed in shorter timeframes. This approach allows teams to adapt to changes quickly, as they can adjust their work plans based on stakeholder feedback.

Agile practices are often used in software development, but they can be applied to any project requiring flexibility and collaboration, like legal firms. These practices include things like:

Planning: Agile starts with planning, where the team identifies the project’s scope and breaks it into smaller, manageable pieces. The team identifies the tasks to be completed in each sprint and determines the order in which they will be completed.
Sprint: The team then works in short sprints, typically one to four weeks, to develop and deliver small pieces of working software. Each sprint has a specific goal, and the team works to achieve that goal during the sprint.
Daily stand-up meetings: During each sprint, the team holds daily stand-up meetings to discuss progress, identify roadblocks, and plan for the next day’s work. These meetings are usually short and focused, with each team member providing a brief update on their progress.
Testing: Agile emphasizes testing throughout the development process. After each sprint, the team tests the software to ensure it works as intended and meets the customer’s needs.
Review and feedback: At the end of each sprint, the team holds a review meeting with stakeholders to demonstrate the working software and gather feedback. The team then uses this feedback to improve and adjust the project scope.
Continuous improvement: Agile teams constantly seek ways to improve their processes and products. After each sprint, the team holds a retrospective meeting to discuss what went well, what didn’t, and what can be improved in the next sprint.

What is the organization of agile requirements?

Organizing Agile requirements can be a complex process that involves breaking down larger features into smaller, more manageable pieces. User stories, epics, and tasks are common ways to organize requirements in Agile methodology. Let’s talk about each of them.

Theme: A broad category or topic represents a set of related user stories or features. Themes help to organize Agile requirements by providing a high-level view of the product and its goals. Themes can be used to group related user stories and epics, making prioritizing work and tracking progress easier. For example, a theme might be “Improved user experience,” including user stories related to better navigation, clearer messaging, and more intuitive interfaces. Themes can be used to guide product development and ensure that the team is focused on delivering features that align with the overall vision of the product. By using themes to organize Agile requirements, development teams can better communicate with stakeholders, ensure that their work is aligned with business goals, and deliver a product that meets the needs of their users.

Epics: Epics are larger user stories broken down into smaller stories. An epic may be too large to complete in a single iteration, so it is broken down into smaller, more manageable user stories. Epics capture customer requirements that cannot be captured in a single-user story. Epics are typically displayed on the same board as user stories but may be displayed in a different color or another section. The development team can then work on the individual user stories that make up the epic, ensuring that the epic is completed over time.

Story: User stories are short, simple descriptions of a feature or functionality that the customer wants. They are written from the user’s perspective and describe what they want to achieve rather than how the feature will be implemented. Each user story typically follows a simple format: As a (user), I want (feature), so that (value). This format helps ensure that the focus remains on the user and the value that the feature will provide. User stories capture customer requirements and support the development team in understanding the customer’s wants. User stories are typically written on index cards or sticky notes and are displayed on a board, such as a Kanban board or a Scrum board. The development team can then work on the user stories in priority order, ensuring that the most important features are delivered first.

Tasks: Tasks are the smallest unit of work in agile development. They are used to capture the specific work that needs to be done to implement a user story or an epic. Tasks are typically written on sticky notes or cards and are displayed on the board along with user stories and epics. Tasks help the development team understand what needs to be done to implement a user story or an epic. Tasks are typically assigned to individual team members, and the progress of each task is tracked on the board. This helps ensure that the team is progressing toward completing the user stories and epics.

How to create User Stories in Law firms?

Even with a powerful productivity tool like a kanban board, it is simple to become overburdened by the many tasks we must complete at any given time. Agilists avoid these task-based activity traps by altering their perspective on the work that has to be completed. They begin by stating what problem needs to be solved and why rather than specifying what work needs to be done and what features it has to have. In reality, asking yourself, “what is the problem I am trying to address” can effectively overcome challenges or mental hurdles. But when describing issues that need to be resolved, Agile practitioners frequently employ a series of open-ended words known as a “User Story.” A User Story, in essence, is a summary of a specific consumer requirement and the factors that led to it. A simple example of a User Story is:

To be able to solve_______(problem)________________, I need to __________(plan of action)____________________, so that I can __________(desired result)____________________.

A user story for a lawyer working on a case could be: “as the representing counsel for the case, I need to divide the research of the case in such a way that each of my associates can focus on different parts of the case dealing with different questions of law.”

Though multiple agile methodologies exist, Scrum is the most widely used. So let’s talk about Scrum.

What is Scrum?

Scrum is an Agile framework used for managing complex projects. It was first introduced in the 1990s as a way to increase productivity and improve the quality of software development. Scrum is based on Agile principles, prioritizing flexibility, collaboration, and responsiveness.

The Scrum framework consists of three roles: the Product Owner, the Scrum Master, and the Development Team. The Product Owner is responsible for defining and prioritizing the product backlog, a list of features and requirements that must be completed. The Scrum Master ensures that the Scrum framework is followed and that the team works efficiently. The Development Team is responsible for completing the items in the product backlog.

How Does Agile Scrum Work?

Agile Scrum methodology breaks down complex projects into smaller, manageable tasks. The process begins with a product backlog and a prioritized list of features and requirements that must be completed. The team then defines the tasks required to complete each item in the product backlog.

Each sprint begins with a sprint planning meeting, where the team decides what tasks will be completed during the sprint. The team then works on these tasks during the sprint, with daily stand-up meetings to ensure everyone is on track. At the end of the sprint, the team presents the completed work during a sprint review meeting.

The sprint retrospective meeting takes place after the sprint review meeting. The team reflects on the previous sprint and identifies ways to improve their process in the next sprint.

What are the Benefits of Agile Scrum Methodology?

Agile Scrum methodology offers several benefits for organizations looking to manage complex projects efficiently. Here are some of the key advantages:

Increased Flexibility: Agile Scrum methodology allows teams to adapt to changing requirements quickly, which is particularly important in today’s fast-paced business environment.

Improved Collaboration: Agile Scrum methodology promotes collaboration between team members, leading to better communication, more innovative solutions, and a greater sense of ownership over the project.

Faster Time-to-Market: Agile Scrum methodology allows teams to deliver high-quality software quickly and efficiently, which can help companies get their products to market faster.

Higher Quality: Agile Scrum methodology emphasizes continuous testing and integration, which can result in higher-quality software and fewer bugs.

This video was extracted from this website.

How can Scrum be adapted in Law Firms?

Scrum can be adapted and applied to other fields, such as law firms. In a law firm, Scrum can be implemented by forming cross-functional teams that consist of lawyers, paralegals, and support staff. The team can then work together in short sprints to accomplish specific tasks, such as drafting legal documents, conducting research, or preparing for a trial. During these sprints, the team can hold daily stand-up meetings to discuss progress, identify roadblocks, and adjust their approach. Using Scrum in a law firm allows the team to collaborate more efficiently, increase transparency, and deliver higher-quality legal services to clients. Below is a video talking about this topic.

This video was extracted from this website.

What are the Best Scrum Tools for Agile Project Management?

The article below talks about the best Scrum tools that you can use for implementing Agile Project Management.

The Best Scrum Tools of 2023 for Agile Project Management

10 Real-World ideas to implement Agile Methodology in Law Firms:

1. Contract Review and Negotiation

Agile methodology can be applied to contract review and negotiation by breaking the process into smaller, more manageable tasks. Teams can plan their work in short sprints, with each sprint focused on completing a specific set of tasks. This approach allows teams to adapt to changes quickly and incorporate feedback from stakeholders in real-time and can be broken down into the following tasks:

• Identify the key terms and provisions of the contract
• Determine the scope of the review
• Identify potential issues and risks
• Provide recommendations for changes and negotiation points
• Collaborate with stakeholders to finalize the contract

2. Litigation Support

Agile methodology can also be applied to litigation support. By breaking down the process into smaller tasks, legal teams can plan their work in short sprints and adjust their plans as needed based on feedback from stakeholders and can be broken down into the following tasks:

• Collect and organize relevant documents and evidence
• Conduct legal research to support the case
• Draft pleadings, motions, and briefs
• Coordinate with experts and witnesses
• Collaborate with the legal team to prepare for hearings and trials

3. Legal Project Management

Agile methodology can be used for legal project management by breaking down projects into smaller tasks and planning work in short sprints. This approach allows teams to monitor progress and adjust plans as needed and can be broken down into the following tasks:

• Define the scope of the project and the deliverables
• Break the project down into smaller tasks
• Estimate the time and resources needed for each task
• Assign tasks to team members
• Monitor progress and adjust plans as necessary

4. Intellectual Property Management

Agile methodology can be applied to intellectual property management by breaking down the process into smaller tasks such as research, analysis, and drafting. This approach allows teams to collaborate more effectively and deliver high-quality work in shorter timeframes and can be broken down into the following tasks:

• Conduct research to identify existing intellectual property
• Analyze the strengths and weaknesses of the intellectual property
• Draft patent applications, trademark applications, and copyright registrations
• Conduct trademark and patent searches
• Coordinate with foreign counsel to file international applications

5. Due Diligence

Agile methodology can be used for due diligence by breaking down the process into smaller tasks such as document review and analysis. This approach allows teams to prioritize work, collaborate more effectively, and adapt to changes quickly and can be broken down into the following tasks:

• Define the scope of the due diligence review
• Identify the documents and information to be reviewed
• Conduct a review of the documents and information
• Identify potential issues and risks
• Provide recommendations for addressing the issues and risks

6. Legal Research and Writing

Agile methodology can be applied to legal research and writing by breaking down the process into smaller tasks such as research, analysis, and drafting. This approach allows teams to collaborate more effectively and deliver high-quality work in shorter timeframes and can be broken down into the following tasks:

• Conduct legal research to support a legal opinion or memorandum
• Analyze the legal issues and provide recommendations
• Draft legal documents, including opinions, memoranda, and briefs
• Collaborate with team members to finalize the document
• Incorporate feedback from stakeholders

7. Regulatory Compliance

Agile methodology can be used for regulatory compliance by breaking down the process into smaller tasks such as research, analysis, and drafting. This approach allows teams to collaborate more effectively and adapt to changes quickly and can be broken down into the following tasks:

• Conduct research to identify applicable regulations and laws
• Analyze the impact of the regulations and laws on the business
• Draft policies and procedures to comply with the regulations and laws
• Conduct training on the policies and procedures
• Monitor compliance and update policies and procedures as necessary

8. Data Privacy and Cybersecurity

Agile methodology can be applied to data privacy and cybersecurity by breaking down the process into smaller tasks, such as risk assessment and compliance. This approach allows teams to collaborate more effectively and adapt to changes quickly and can be broken down into the following tasks:

• Conduct a risk assessment to identify potential risks and vulnerabilities
• Develop a plan to address the risks and vulnerabilities
• Draft policies and procedures to protect data privacy and cybersecurity
• Conduct training on the policies and procedures
• Monitor compliance and update policies and procedures as necessary

9. Contract Management

Agile methodology can be used for contract management by breaking down the process into smaller tasks such as contract drafting, review, and analysis. This approach allows teams to collaborate more effectively and adapt to changes quickly and can be broken down into the following tasks:

• Draft contracts based on legal requirements and business needs
• Review and analyze contracts to identify potential issues and risks
• Negotiate contract terms with stakeholders
• Collaborate with stakeholders to finalize the contract
• Monitor compliance with the contract terms

10. Alternative Dispute Resolution

Agile methodology can be applied to alternative dispute resolution by breaking down the process into smaller tasks such as research, analysis, and drafting. This approach allows teams to collaborate more effectively and adapt to changes quickly and can be broken down into the following tasks:

• Conduct legal research to support the case
• Analyze the legal issues and provide recommendations for resolving the dispute
• Draft settlement agreements and other legal documents
• Coordinate with stakeholders to negotiate a settlement
• Collaborate with the legal team to finalize the settlement

Using agile practices, the legal team can plan their work in short-term sprints, with each sprint focused on completing a specific set of tasks. The unit can meet daily to discuss progress and identify roadblocks that can be addressed in real-time.

Another way agile practices can be applied to legal management is using Kanban boards. Kanban boards are visual tools that help teams manage their work by showing the status of each task. Teams can use Kanban boards to track the progress of legal projects, identify bottlenecks, and prioritize work.

Agile practices can improve communication and collaboration between legal teams and other stakeholders. By breaking legal projects into smaller tasks, legal teams can update stakeholders regularly and incorporate feedback in real-time.

Conclusion

In summary, agile practices can be a valuable tool for legal management in IT firms. By breaking legal projects into smaller, more manageable tasks, legal teams can adapt to changes quickly, improve stakeholder communication, and increase collaboration. Using agile practices, legal teams can improve their efficiency and deliver high-quality work in shorter timeframes.

That’s it for today!

Sources used for the creation of this article:

https://www.stackfield.com/blog/legal-management-it-firms-107
https://www.prolawgue.com/agile-methodology-for-lawyers-beginners-guide/
https://www.kartalegal.com/insight/what-is-agile-in-the-law
https://www.wrike.com/project-management-guide/faq/what-is-scrum-in-agile/

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.

Dataflows

A 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

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.

Endorsement

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:

Data Quality: Define data quality and accuracy standards to ensure that the data used is reliable and trustworthy.
Data Security: Implement security measures to ensure that sensitive data is protected and only accessible by authorized users.
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.
Data Ownership: Assign ownership of data sources and ensure that data owners are responsible for maintaining the accuracy of their data.
Data Stewardship: Designate data stewards responsible for maintaining data quality and ensuring compliance with data management policies.
Data Access Control: Implement access controls to ensure that only authorized users can access sensitive data.
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:

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

Creating Dataflows for cleaning and transforming data.
Implementing Sensitivity labels to classify and protect sensitive data.
Using Datamarts for centralizing data and improving data management.
Enforcing data quality standards with Endorsement.
Monitoring data assets with Discovery.
Implementing data privacy and security with Power BI Premium.
Improving report refresh times and performance with Power BI Premium.
Sharing reports and dashboards with a larger audience with Power BI Premium.
Utilizing Power BI Premium’s increased capacity for large datasets.
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.

Conclusion:

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:

Familiarize yourself with different digital marketing channels.
Learn how to create effective campaigns using different digital marketing tools.
Understand how to measure and analyze your results to optimize your campaigns.
Stay up-to-date on the latest trends and technologies in digital marketing.
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.

Conclusion

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!

The Future of Legal Content Interpreting: How NLP Can Help?

Legal content is dense, complicated, and often less than straightforward. As such, it’s crucial to understand what you’re reading and make decisions based on that information. That’s where natural language processing (NLP) comes in.

What is NLP?

NLP, or natural language processing, is a field of computer science and artificial intelligence concerned with the interactions between computers and human (natural) languages, mainly how to program computers to process and analyze large amounts of natural language data.

NLP interprets legal content through algorithms that identify relevant information from unstructured text. This process can extract key phrases, concepts, and entities from a document for further analysis. Additionally, NLP can generate summaries of legal documents or identify critical issues within a document.

Why Use NLP for Interpreting Legal Content?

There are many reasons to use natural language processing (NLP) when interpreting legal content. First, NLP can help you to identify the essential information in a document. This is especially helpful when dealing with long and complex legal documents. Second, NLP can help you understand the text’s meaning by extracting key concepts and ideas. This is extremely helpful in understanding the implications of a legal document. Finally, NLP can help you to identify relationships between different concepts in the text. This can help identify issues that may not be immediately apparent.
Overall, NLP can be a very helpful tool when interpreting legal content. It can help you to identify the most crucial information, understand the meaning of the text, and identify relationships between different concepts.

Applications of NLP in the Legal Field

There are many potential applications for NLP in the legal field. Here are a few examples:

Automated contract analysis: NLP can automatically analyze contracts and identify critical provisions, such as parties, obligations, and termination clauses. This can save time and improve accuracy compared to manual contract review.
Legal research: NLP can quickly search large volumes of legal documents (e.g., court opinions) for relevant information. This can save time and improve accuracy compared to traditional keyword search methods.
Sentiment analysis of legal documents: NLP can be used to analyze the sentiment of legal documents, such as court opinions, to identify positive or negative feelings towards specific individuals or entities. This information could be helpful for lawyers when making strategic decisions about cases.
Predictive analytics for litigation: NLP can predict the outcome of litigation based on past cases with similar facts and circumstances. This information could be helpful for lawyers when deciding whether to settle a claim or take it to trial.
Automated document summarization: NLP can automatically summarize legal documents, such as court opinions, to save time and improve accuracy. This information could be helpful for lawyers who need a quick case overview.
Entity extraction from legal documents: NLP can automatically extract entities, such as names of people and organizations, from legal documents. This information could be helpful for lawyers when they need to find information about specific individuals or entities quickly.

Let’s explore some real examples extracted from John Snow LABS.

What is John Snow LABS company?

John Snow Labs, an AI and NLP for a healthcare, legal, and finance company, provides state-of-the-art software, models, and data to help healthcare, legal, and life science organizations build, deploy, and operate AI projects. Click here to go to their LinkedIn page.

They have a model called “Spark NLP for Legal” to work on Legal documents. Let’s deeply in.

Introducing Spark NLP for Legal

What’s in the Spark NLP for Legal?

State-of-the-art software + pre-trained legal-specific models

One of the most common uses of NLP is Entity Recognition. Let’s try using a Portuguese document.

If you want yourself try this example, click here. Also, you can look at the Python code on Google Colab here.

Another exciting use of NLP technology is to extract relations between parties in an agreement. Look at the example below.

This model returns something like this to organize and save the insights.

If you want yourself try this example, click here.

You can save this information with each document and use it to analyze and predict insights. This tool is so powerful and is available to work in multiple languages. In addition, you can look at other attractive models in John Snow LABS in healthcare and finance.

Conclusion

NLP is a powerful tool that can be used for various tasks, including the interpretation of legal content. In this article, we’ve looked at how NLP can be used to interpret legal documents and how it can be used to improve the accuracy of translations. We hope this has given you a better understanding of how NLP can be used in the legal industry and how it can benefit your business.
If you found this article helpful, please share it with your network! And if you have any questions or comments, please feel free to leave them below.

Let’s share more articles talking about Spark NLP For Legal:

Spark NLP For Legal 1.0.0: Over 300+ new state-of-the-art models in multiple languages!

Legal NLP 1.1.0 for Spark NLP has been released

Legal NLP 1.2.0 for Spark NLP has been released!

That’s it for today!