The title “Business Analyst,” is so broad, that it could really describe any business employee whose primary focus is to deliver insights from data. Anyone who analyzes their own business’s operations, creates customer or market intelligence reports, assists in data entry and management, or simply does entry-level spreadsheet work could have the official title of “Business Analyst.” However, regardless of the complexity of analytics one performs, or the skillset they bring to the table, all the distinct types share a common set of factors that differentiate good analysts from bad ones.
A business analyst’s core purpose is to connect the dots hidden in data and provide decision makers and stakeholders with actionable insights. Some roles will solely focus on running queries, creating reports or dashboards, and providing stakeholders with standard metrics, but in many organizations the role has dramatically expanded. Skills in advanced analytics, as well as contextual understanding of high-value business questions are a requirement for most modern business analytics positions. Modern businesses want analysts who can not only provide routine insights but also deliver convincing insights to transformational business questions.
This boom in demand for multi-skilled analysts is primarily a result of organizations going through their digital transformation. Companies are connecting to more data, in more ways, and need to quickly uncover actionable insights from that data to stay competitive. This expansion of data availability and data-driven leadership puts tremendous strain on IT and data science teams to keep up, so if a company does not also transform data accessibility outside of those teams, they lose the true value of collecting the data. This creates a need for workers who know how to analyse the data once accessed, and how to deliver actionable insights quickly. This high-demand hybrid position is one that cannot rely solely on analytic skills alone nor can they rely only on departmental or business knowledge. The most valuable business analyst are ones that combine both analytics and business acumen, and use both to provide:
- Not just statistical reporting but analytical findings that drive high-value action
- Clarity in the current questions being asked, and line-of-sight to answering new questions
- A collaborative analytics evangelist who works across departments to connect data sources, reduce siloing, and provide deeper insights
- An avid learner that seeks to expand their analytics understanding, providing deeper and deeper insights as time passes
Those seeking to improve as an analyst should keep in mind a balance between business knowledge and analytical skills. Business analysts with deep departmental, industry, or general business knowledge can quickly expand their value by expanding analytical skills. At the same time, understanding how to communicate results, how to collaborate across departments, and how to become an analytics evangelist, are all vital for growth as a business analyst.
5 Ways to Stand Out
1. Broaden Your Understanding
As a business analyst, you must be able to clearly communicate insights and collaborate with others on what to do with those insights. It starts with an understanding of the goals of your organization. With this contextual understanding, you’ll be able to present insights from data in a digestible format that provides support for clear action. Don’t be afraid to ask questions. Deep dives into the business, its goals, its approaches, and creating various analytical approaches specific to it can make a significant difference to an analyst’s contribution. As a professional, you’’ll receive requests for solutions to problems at both a micro and macro level. These requests can involve various departments and can vary in complexity and time needed to solve them. Build a network of industry specific knowledge workers that you can tap into when working towards a specific solution.
2. Speak the Language
A business analyst must use the various tools at their disposal to find solutions and answers to questions. However, it’s not enough to simply provide data-driven answers to important questions. In data science and analytics, answers will often be complex and require contextual clarification to understand what the data is saying. It’s up to the analyst to make sure the output is digestible and speaks to the audience. The clarity comes from “speaking the language,” of the audience as much as it comes from providing accurate results.
A few pointers to ensure you are providing clarity in presentations and reports:
- Work with a theme: Choosing a theme to present current trends and put them in the context of past research is a terrific way to set the base for a presentation. This provides a segue into data that can influence approaches for the future.
- The power of visuals: Sometimes, analysis output can be complicated and is best represented in a pictorial form. Picking the right visual to quickly tell the story is imperative. Visuals should tell an accurate, compelling story within a few seconds.
- The structure of stories: Using the structure of storytelling can make data presentation easy and compelling. From a clear beginning (which could be a summary of findings) to a body that outlines the context and process of the findings, to an ending that summarizes it for easy understanding, storytelling makes information more memorable, and helps the audience gain perspective on the data.
- The power of design: An effective way to make a presentation is to combine various themes, visuals, and storytelling. Visual clarity is critical to designing an effective presentation, as people understand graphs and images far more readily than paragraphs of text or tables of data. It should be simple enough to deliver the message succinctly, yet catchy enough to hold attention.
- Easy summaries: Not everyone has the time or the recall ability to absorb and remember all the data presented to them. Providing summaries at the beginning and the end of the presentation is a good idea. Also, providing recommendations for specific problems based on the data collected ensures actionable information is easily communicated.
3. Slay Those Silos
Many organizations have business analysts doing similar work in each department, as well as more advanced analysts who work either on data science teams or IT departments. This often creates numerous silos within each department, where each team may have their own data and may even do the same calculations as other departments, often resulting in multiple answers to the same business questions.
This data and analytics siloing creates a huge need in organizations to combine data sources, streamline analytics, and standardize how analysts interact with data. To remedy this, organizations should invest in the process of analytics transformation, but this change needs to come at the analyst level as well. Immediate value can be garnered from sharing data across departments, working to combine analytical efforts, and personal investment in expanding analytics capabilities.
This will not only increase one’s value to an organization but will also prevent waiting on data science teams to turn around analytics and will also lead to automating tasks that benefits everyone. The more tasks are automated, the more analysts can focus on higher impact projects, increasing personal skills and delivering deeper insights that have cross-departmental benefits.
4. Never Stop Learning
Technology and software are constantly evolving and as a business analyst, learning and upskilling are constant. This can be basic programming languages or even low code platforms. Knowing how Python, R, or SQL works can add a better dimension to your business analytics approach.
In a technologically advanced world, tools like spreadsheets are prone to human error and chaotic presentation. The primary shortfall of being stuck in spreadsheets or relying on data science teams for more advanced analytics, is that it limits understanding and access of where data comes from, and how to go get it. By better understanding database design, programming languages, or analytics platforms such as Alteryx, business analysts can get a better view of data relationships and can work to automate more mundane tasks and increase productivity in multiple ways:
- Repetitive yet crucial tasks can be automated, ensuring error-free input while leaving you free to work on more challenging elements of the job
- Serves as a base to enhance your knowledge and explore advanced analytics. You can use this new knowledge to introduce feature engineering in your approach. Here, domain knowledge is used to extrapolate attributes from raw data
- You can work on building machine learning models with tools like assisted modelling
5. Understand Your Organization’s Data Infrastructure
Understanding how your company obtains, stores, shares, and governs its data is key to understanding how to contribute to analytical transformation. This involves things like understanding of database structure, the different methods for connecting to data, how data moves from one platform to another within your org, and what tools analysts are using across departments. This can uncover gaps and show where you can make the biggest impact on improving analytics at your org. Here are some questions that can help you uncover what can be improved, and will help you assess where your organization is analytically:
- How does your company handle customer data? Does your company rely on a Customer Relationship Platform (CRM)? This is the lifeblood of the modern enterprise and management of this data will be a top initiative for any IT department. Sales, marketing, finance, and c-suite will all work with this data and be interested in insights derived from it.
- Where does your company store most of your data? Does your company store data on-prem and manage with database software or is your data stored with a cloud storage provider? Finding out which service your company uses can give good direction into how you can learn to better access data.
How do analysts access data? Do business analysts in each department send requests to a central data science team for advanced analytics? Is each department doing their own analytics? This is key to understanding what you can do to reduce turnaround for your own analytics and can uncover where the biggest data siloing issues lie.
Business analysts today need to continually grow their analytical capabilities, as well as the skills that enable extraction of valuable insights from that data. By focusing on this balanced growth, analysts can further their career, while at the same time providing transformational analytics for their organization.
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