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Top Business Analysis Skills To Learn in 2023 To Thrive in a Volatile Economy

With the economic landscape ever-evolving and uncertainty in the air, it pays to know which business analysis skills are essential for success. In such a business environment, having the right skills can be the difference between success and failure. As 2023 approaches, it’s more important than ever to develop the right business analysis skills that will help you stand out from competitors and thrive in these uncertain times. With new technologies and approaches emerging all the time, developing the right business analysis skills has become more important than ever before. In this article, we’ll explore the top business analysis skills you’ll need to master in order to stay ahead of the pack. Find out how you can get ahead of the curve by acquiring these valuable skills now!



What is Business Analysis?

The term ‘business analysis’ is used in many different ways, but at its core, business analysis is all about bringing positive change, improving business performance with technology adoption, Process improvement and removal of inefficiencies in the cycle. It also encompasses improvement of revenue, market reputation, user experience, understanding how businesses work and how they can be improved. It’s about finding ways to do things better, faster, or cheaper.

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Business analysts typically have strong analytical and problem-solving skills, and are able to see the ‘big picture’ while paying attention to detail. They need to be good communicators, great facilitators as well as collaborators, able to explain complex concepts in simple terms, asking the right questions and also be good listeners.

As businesses become more complex and the pace of change increases, the need for business analysts will continue to grow. If you’re thinking of a career in business analysis, or are already working as a business analyst, it’s important to stay up-to-date with the right skills, latest methods and tools.

Essential Skills for Business Analysts in 2023

As the world economy becomes increasingly volatile, businesses must be agile and adaptable to survive. Business analysts play a vital role in helping organizations in changing gears, understand and respond to change and adapt to the new business needs. In 2023, the most successful business analysts will be those who have developed the following essential skills:


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Data Analytics: With the increasing amount of tech penetration and the huge amount of data available, business analysts are expected to be skilled to interpret, analyze data, see patterns in them and come up with actionable insights from them. To be able to do all this they need to be proficient in data analytics tools and techniques such as data interpretation and visualization. They will need to be able to not only interpret and communicate the results of these analyses to key stakeholders but also present actionable insights for strategic decision making.


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Agile methodologies: Agile methodologies have proven to be effective in adapting to change, taking

frequent customer feedback and prioritizing delivery accordingly. And as a result, today more than 70% projects adopt agile methodology and their adoption will continue to grow. Business analysts need to be conversant with the principles of agile analysis and be able to work effectively within agile teams.




Financial Analysis: In a volatile economy, it is important for business analysts to understand financial analysis and be able to assess the financial impact of different business decisions. They will need to be able to evaluate investment opportunities, assess risks involved, and make recommendations based on financial data. They need to have the ability to know which are the initiatives that can help in quicker turn around for revenue and which changes can bring cost control thus making a better cash flow situation for the organization.

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Strategic Thinking: Business analyst as a role requires higher level of thinking as well as attention to details to see the opportunities of improvement. Hence, they will need to be able to think strategically about the long-term goals of the organization and be able to develop plans to achieve those goals. They will need to be able to evaluate the potential impact of various business options and make recommendations based on data and best practices.


Adaptability: The ability to adapt to changes in their environment is a critical skill for success in a volatile economy. Business analysts will need to be able to quickly respond to changing conditions, be flexible to acquire skills to perform well in their approach to solving problems.


Cross-functional Collaboration: Business analysts are the change makers bringing positive changes to the organization thereby making the organization’s process faster and better. To achieve these objectives, they will need to be able to work effectively with teams from different departments, hierarchy, backgrounds, and be able to translate technical concepts and requirements into language that is accessible to a wide range of stakeholders.

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Communication Skills: Business analysts are the ones who are required to influence stakeholders and users to come to agreement for the business decisions, and this requires being a great communicator. Effective communication is and will remain a critical skill for business analysts in 2023 and years to come. They will need to be able to clearly and effectively communicate complex ideas and data to stakeholders, and be able to negotiate and manage conflicting interests.


In conclusion, the skills that business analysts need to focus on in 2023 will continue to evolve, but the skills outlined above will likely be critical for success in a volatile economy. It’s important for business analysts to stay up-to-date about emerging trends and to continuously grow their skills and knowledge to stay ahead of the curve.

Best of BATimes: BRD Vs FRD

Documentation is the most important aspect for any BA.


The documentation is useful to depict the requirements and the detailed discussion about new features and change request if any. There are many different types of documents that a BA prepares. Some of the important ones are listed below –

  • Business Requirement Document (BRD)
  • User Stories
  • Use Case Specification Document
  • Functional Requirement Document (FRD)
  • Requirements Traceability Matrix (RTM)
  • Market Requirements Document (MRD)
  • Product Requirements Document (PRD)

Apart from these there are several other documents that is created by Business Analyst. It helps in understanding the business process and business events. A business events is a trigger that gives birth to the requirement. These requirements are then fulfilled by opting for IT solution.

Diagrammatically the documents can be pictured as a simple sheets of papers which contains some useful matter.

Let’s take a look at the similarities and striking differences between BRD and FRD.

Business Requirement Document

  • BRD highlights “Business Requirements” – i.e., high-level business goals of the organization developing the product or solution with the help of IT.
  • In other words it describes at very high level the functional specifications of the software
  • A formal document illustrating the requirement provided by the client
  • The requirements could be collected either by verbal or written or both
  • Created by a Business Analyst (usually) who interacts with the client
  • Entire work is executed under the supervision of the Project Manager
  • It is derived from the client interaction and requirements

The BRD is important since it is the foundation for all subsequent project deliverable, describing what inputs and outputs are associated with each process function. It describes what the system would look like from a business perspective. Following are the most common objectives of BRD –

  • To arrive at a consensus with stakeholders
  • To provide input into the next phase of the project
  • To explain how customer/business needs will be met with the solution
  • Holistic approach to business needs with the help of strategy that will provide some value to the customer

Basically, stakeholder’s requirements can be small or big. Thus it needs to be break wherever it requires and should be taken as multiple requirements.

Format of BRD –

There are many formats or templates that the organization follows. However, it depends upon the practices that is carried in the organization. For a product based company the BRD format is different as compared to service based firms. Standard format which is followed in most organizations are shown below. It is important to note that for clear understanding of the document we should include list of acronyms used.

The BRD template contains –

  • document revision
  • approvals
  • RACI chart
  • introduction
  • business goals
  • business objectives
  • business rules
  • background
  • project objective
  • project scope
  • in-scope functionality
  • out-scope functionality
  • assumptions
  • constraints
  • risks
  • business process overview (modelling diagrams for instance, Use Case and Activity Diagram)
  • legacy systems
  • proposed recommendations
  • business requirements
  • appendices
  • list of acronyms
  • glossary of terms
  • related documents

Now let’s try to dig more in FRD..

Functional Requirement Document

  • FRD highlights “Functional Requirements” i.e., functionality of the software in detail
  • Depending on the product, FRD document can be between 10 to 100 pages
  • It too describes at a high level the functional and technical specification of the software
  • Usually created by Business Analyst under the supervision of technical expert, for instance System Architect
  • In a small and medium sized organizations a BA take care of this
  • Few companies did not create FRD, instead they used BRD as it is detailed enough to be used as FRD as well
  • FRD is derived from the BRD





Actually, the process to reach the expectancy of the BRD is an FRD itself. Here an analyst after discussing with stakeholders and project manager ponder on questions like –

  • How we develop the expected requirement(s)?
  • What are the features and functionalities?
  • What are the tools and/or systems used and their dependencies?
  • How will the customer reacts when they start using the solution?
  • Any assumptions or constraints?

Most common objectives of FRD –

  • Depict each process flows for each activity interlinking dependencies
  • Holistic view for each and every requirements, steps to built
  • Estimating detailed risks for each new change request
  • Highlight the consequences if the project health is weak to avoid scope creep

The FRD should document the operations and activities that a system must be able to perform.

Format of FRD –

Likewise BRD, FRD has a somewhat different format focusing more on risks and interfaces. Although there is no such standard format that a Business Analyst should opt for. Companies belonging to different domains use their own template. For instance, you would find many points would be repeating as in BRD.

But there should be no confusion for BA to prepare this document.

The FRD template contains –

  • Introduction – It should contain Purpose, Scope, Background, References, Assumptions and constraints, document overview
  • Methodology
  • Functional Requirements
  • Modelling Illustrations – Context, User Requirements, Data Flow Diagrams, Logical Data Model/Data Dictionary, Functional Requirements
  • Other Requirements – Interface Requirements, Hardware/Software Requirements,
  • Glossary

Now the use of BRD or FRD in organizations depends on the organization policies, practices followed by the project team and stakeholders. As in my organization all projects are being hoped from Waterfall to Agile. If the stakeholders is positive with the documents then BA will design the same. But if there is a need for the continual delivery of working product then documentation will not be preferred.

However, documentation will remain a valid artefact of any project in distant future.

Published: 06/12/2017

Add some UMPH to your UML

In the world of analysis, at least one thing is true: if you like diagrams, you have probably come to be close with Unified Modeling Language (UML). UML Diagrams are helpful to show flows and relationships of information. This helps to illustrate data points, structure and interactions. Here are some ways to enhance their effectiveness when dealing with stakeholders at all levels:


Clean Connectors

Showing directions and connections is helpful, but connectors crossing over can quickly turn a diagram into a confusing web. Because UML Diagrams can vary in complexity and granularity, crossing streams can be unavoidable. Lines should cross as minimally as possible, but if they do have to cross, they should show as a “hop”. Most programs default to this feature when connectors go through each other without a particularly specified intersection. If connectors are not looking separated enough and have too many unnecessary hops, consider the component or class layout being used and see if the objects/items can be better sequenced.

Connector types should also be utilized where necessary to show the nature of interactions, whether it is to illustrate direction or dependencies. This can allow for some detail that could be taken out of the explanation pieces of your documentation.




Know your Domain

UML diagrams have a specific best-case utility and are strongest when they are describing and modeling objects and displaying aspects of system views. Alternatively, Business Process Modeling Notation (BPMN) helps to show or describe the business process. Understanding how the two differ and how to best use them in your modeling can help to optimize the effectiveness of your documentation and communication. Depending on the project, UML diagrams may be helpful in creating documentation to detail functionality and object interaction. BPMN diagrams can be useful communication aids, especially since they can tailor to the audience; for example, a BPMN that shows a high-level view of the process may be best to use in presentations to individuals that have executive governance in a project.


Speaking of Communication…

Using the correct pieces of UML diagrams can also help to keep any text requirement of information appropriately concise. This means avoiding having to describe your intention of objects or classes to a reader, and instead, simply using the standardized shapes and connectors to represent the information being illustrated. A good UML should have minimal text detail in the itemized areas, and the rest of the information conveyed through the standardized language UML provides.

10 Principles for Working with Processes

Process: “a series of actions or events performed to make something or achieve a particular result, or a series of changes that happen naturally” Source: Cambridge Dictionary

When used correctly, process modelling is an invaluable activity, and along with process maps can be a powerful way of communicating of what is happening or should happen. At its simplest it helps us to decompose a process into a sequence of steps, with a defined start and end, and understand the various events that trigger specific actions. They can also help us to identify the users (‘actors’) and what their involvement is.

This provides the basis for analysis and optimization.

However, it can be easy to fall down the path of over-complication, especially when it comes to drawing up a process. Meaning that instead of being helpful, they can be time consuming and not fit for purpose.


Therefore, here are my set of 10 principles for working with processes, whether that be through a discovery activity to define an ‘as-is’ or through a design phase to build up a set of potential ‘to-be’ processes.

  1. Understand the purpose and why, before anything else — what are the models going to be used for? Is it to share with others to seek a consenus view on how something works? Is it to enable you to perform analysis activities off the back of? Is it to identify to a list of users (‘actors’) in an existing process?
  2. Consider your audience, and use notation frameworks sparingly. Notation frameworks such as UML and BPMN, can be helpful in the right circumstances. Especially as a ‘behind the scenes’ analytical aid. But, bear in mind, they often confuse many who haven’t had the same training.
  3. Focus on ‘just enough’, don’t let perfection be the enemy of good. Low-fi is generally fine, share early. Iterative process modelling is often the best form.
  4. Think about accessibility, when sharing process maps—not everyone may have a Visio or Lucid license. Consider the best tool so that everyone who needs to access it, can access it. If in doubt, export it as a PDF before sharing.
  5. Levels, know when you need them and when you don’t — you don’t need to model every level every time. However, you may need to understand something at a higher level first, before you can break it down further. All goes back to the purpose!
  6. Beware relying on previously documented processes — Beware of re-using or relying on the information in a previously modelled processes unless you have a robust process library, that is regularly maintained and with stringent change control. Processes have a shelf life!
  7. Consider sample size, like you would with any other type of research — there are documented processes, and then there are workarounds that users and customer actually do. Not everyone may approach or engage with it in the same way, so consider how many people you should speak to, in the same way you would with any other type of research activity.
  8. Talk to users who ‘do’ the processnot just the person who ‘owns’ the process. Expectations vs reality are often very different.
  9. Obsess over the events that trigger a process. They might be automated, such as triggered at a set time or upon a specific action being completed. They could be manual, triggered by an interaction from a user. Whatever, they are — invest time in understanding what they are, how they work and assess whether they’re helpful.
  10. Reference your contributors — its theirs, not yours — whether they’ve helped you to to understand how a current process works, or if they’ve been involved in designing an improved or new process. Not only is it polite, to reference those who you’ve collaborated with along the way, it‘s a helpful record when looking back. It may also prompt others, to suggest additional people who should be involved.




Lastly, remember processes are different to customer journey maps, service maps, business capabilities. Don’t be fooled into thinking that you don’t need to understand processes, if you have a good grasp on the customer journey or business capabilities. They provide different thinking and perspectives, and will uncover different information. Especially in discovery settings, processes are the closest you can get to understanding what is actually happening for all users involved. They also consider both visible and invisible triggers and events.

Why Business Agility Starts with Strong Data Governance

Data is now one of the most valuable assets corporations own, yet, it is still often treated as a waste product.

As data increasingly guides decision-making and digital advances automate business processes, so the integrity of data assets becomes more important. All organizations, regardless of size or sector, need to shift from viewing data as an inconvenient cost center to an asset that is reliable, accessible, secure and usable. To ensure organizations can proactively manage their data in all its disparate guises and locations, they need a structured approach with defined rules and policies governing how it is handled and stored.

The Case for Data Governance

Bad governance is costly. Poor data management impairs an organization’s ability to conduct business, whether that involves managing customers, delivering timely products, spotting market opportunities or operating as efficiently as possible.

Gartner predicts that through 2025, 80 percent of organizations looking to advance their digital business will fail because of their outdated approach to data and analytics governance. If that estimate is correct, businesses are actively missing opportunities and putting themselves at risk.

What Barriers Do Businesses Face?

Faced with increased internal demand for answer to complex questions combined with external pressures to demonstrate value and ROI, organizations are turning to data for the answers. However, harnessing data to deliver actionable insights starts with improved data stewardship. Furthermore, amid a rapidly changing regulatory climate, data management and governance are also critical to ensure businesses don’t fall foul of their regulatory obligations.


Establishing Good Data Governance

Good data governance is about creating a system of data management that addresses all these challenges. Few data and analytics leaders would disagree with the push factors for data governance, but market unpredictability and the sheer pace of innovation has made it harder to benchmark data governance efforts and establish best practice. Having invested in data and analytics, business and IT leaders now urgently need to ensure good governance in order to meet their goals of operational efficiency, increased growth or improved CX.

Establishing a data governance framework is a multidisciplinary exercise. It requires strategic input, data engineering, cloud management, data privacy and compliance expertise. Whether a business is setting up data governance systems from scratch or amending existing processes, there are four guiding principles that can help them wherever they are on their digital maturity journey.

  1. Align Data Governance to Business Goals

The most common pitfall for data governance projects is the failure to match the mission statement to business goals. It may sound obvious but quantifying the business value of the any information transformation required is essential to success. Too often data governance has been perceived as something carried out by the data people, far removed from the rest of the business. However, when business and IT leaders fail to align their thinking at the strategy and goal-setting stage, they risk ending up with tactics and metrics that do not reflect business goals.

Example: metrics that measure data transformation (“X data points de-duped, Y data points corrected”) rather than linking data quality or data management to customer experience.

  1. Establish a Data Governance Blueprint

Does the business have a holistic view of the information requirements by each business process and stakeholder? Amid shifting regulatory requirements and growing data volumes, it is easy for a data governance framework to get outdated. Focus on establishing clear stewardship of data, information, business and security risk. Once data requirements are understood, identify a data management roadmap to organize, maintain and sustain the underlying data so it can deliver the capabilities identified in your blueprint.

  1. Leverage Technology and Automation

Improving data integrity at speed is achievable through smart tool selection. In the burgeoning RegTech sector, for example, there are many examples of solutions that address the need to comply with regulations such as GDPR, KYC and anti-money laundering (AML); bolster security using biometrics, 2FA, blockchain and AI; and automate unnecessary, expensive manual interventions in the data management lifecycle.

  1. Embrace a Cross-functional Approach

Formalizing cross-functional teams for data governance is an important first step to strong data stewardship. Balanced decision-making, which involves weighing up (IT, security, compliance) risks against business opportunities, is the hallmark of true multidisciplinary collaboration, but it only happens if everyone’s committed to the same goals. Training and education are good for raising awareness of issues around data integrity, but don’t go far enough. The next step should be to involve teams in data accountability, transparency and ethics discussions and foster a more data-centric, collaborative culture as a result.


Compliance and Competitiveness Drive Governance

In industries such as healthcare and BFSI, strong data governance has become an imperative in order for these organizations to adapt to changing regulatory environment. Regulatory volatility has made it much harder for these organizations to improve data integrity in a timely manner across business functions. Pioneering organizations in these sectors have led the way with strategies that prioritize an active data governance approach, leveraging cross-functional decision-making and the latest automation technologies to adapt to changing environments fast.

Compliance has been a key driver for data governance until recently but now companies are beginning to understand the importance of data stewardship for value generation as much as risk mitigation. Any business that is committed to its digital transformation goals around improving data-driven decision-making  – indeed any business that has invested or is planning to invest in data and analytics – should make data governance a major focus for 2022.