What are the top BI skills professionals need for success?

Hard skills are good, but what makes a good BI professional is soft skills like common sense and communication. Anyone can learn a computer program. Not everyone has common sense and can effectively communicate with customers and coworkers regarding business requirements, functionality limitations, and what the data means in business terms.

Data analysis skills are crucial for deriving meaningful insights. In my experience, when tasked with improving marketing strategies, I applied data analysis to identify customer preferences. By analyzing purchase patterns and demographics, we refined our targeting, resulting in a 20% increase in conversion rates.

To succeed in Business Intelligence (BI), professionals need strong skills in data analysis and interpretation, proficiency in BI tools like Tableau or Power BI, and a solid understanding of SQL and data warehousing concepts. Additionally, programming knowledge in languages such as Python or R, along with the ability to create insightful data visualizations, is crucial. Effective communication skills are essential to convey complex analytical findings to non-technical stakeholders and align BI efforts with business goals.

Data is the new currency. Learn it. Love it. Incorporate it into your processes. If not, then you are handicapping your strategy.

1. Imagination 2. Understanding of business KPIs 3. Willingness to work with source data 4. Nothing else* *coz LinkedIn has mandated min. 125 characters wanted me to

A very important skill of Story Telling is often missed out when a great presentation with complex charts is presented. Story Telling is a required skill which can make a good presentation come alive and have a long lasting impression on the audience. Story telling needs a good command on the language with an ability to explain content with simplicity and a touch of humour.

The visualization UX test: If you have to verbally explain the visualization to audiences to understand what it is trying to achieve, then the visualization needs to be changed.

Effective data visualization is an art. In a recent project, I used tools like Tableau to transform complex sales data into intuitive dashboards. This not only simplified decision-making for stakeholders but also facilitated a quicker understanding of trends, contributing to more informed business choices.

A picture is a worth a thousand words, Visualization and story telling are the key skills, however in addition combined with analytics would be a lethal combination, there are many powerful tools are available, however I would certainly recommend Spotfire, Statistica (Great Visualization with Data Analytics/Science capabilities), Tableau, Qlik etc..

I’ve learned that Crafting visually engaging graphics demands design finesse, understanding color theory, layout principles, and user experience design. Proficiency in tools like Power BI or Qlik is crucial for seamless data translation. The key to effective data visualization is compelling storytelling, aligning data narratives with audience needs. In my personal experience, presenting technical education data in public sector requires a focus on strategic insights—success rates, industry placement, and student career impact. Adapting the narrative and visual elements to education sector needs optimizes the impact of the data presentation, forming the bedrock for successful data visualization in a dynamic educational landscape.

No matter how good you are in data analysis, data visualisation, data engineering or machine learning, if you don't understand your business, you will draw the wrong conclusion. Data canadvise you to take a certain action to optimise your business. Only someone who understands the business can distinguish whether this is feasible or not.

In my experience I have been a techie for the longest but in order to really move the needle for business it is very critical to understand your business domain and understand the desired business outcomes. It is essential to showcase the data that one has to the business so that they also begin to understand what data and how it comes into the ecosystem. Use every interaction opportunity with stakeholders line of business to understand more about the domain it really will help a data practioner be more value add.

Vamos lá, destaco 5 habilidades: 1. Compreens?o do Contexto Empresarial: Capacidade de compreender os objetivos, desafios e stakeholders da organiza??o. 2. Utiliza??o Estratégica de Dados: Habilidade em aplicar insights de dados para orientar decis?es estratégicas. 3. Conhecimento da Indústria e Mercado: Profundo entendimento da indústria, mercado e concorrentes. 4. Habilidades de Comunica??o Eficaz: Capacidade de comunicar insights de dados de forma clara e persuasiva para diversas áreas da organiza??o. 5. Adaptabilidade e Proatividade: Capacidade de se adaptar rapidamente às mudan?as do mercado e ser proativo na identifica??o de oportunidades e amea?as.

In this point the company culture makes the difference. If you want to have a capable professional that will deliver great work, you must have ways for him to participate in the projects across the company. This broad understanding will help him be more confident about which areas we should focus on. Working with High-Performance Teams can be a guide to improving the business operation as well as the communication skills that are essential to delivering the projects.

It is critical for every BI professional to grow as it gives them to understand business perspective, key KPIs and various aspects to define the intelligence required to develop. They can accelerate the visualisation, better understanding of data and define the summarisation which will help leaders to have better details.

In my experience data from trusted sources travel through different systems and gets persisted in different databases either on perm or cloud. It is very essential to build efficient pipelines that also track or perform data observability in this given day and age. While designing etl or elt solutions, or if one decides to do data virtualization it is critical to understand the different components as well as the data sources, choose the right infra/ technology based on the business requirements, outcomes. In case there are near real time requirements then the data architectures, pipelines need to built accordingly Using the latest technology data engineers can track data drifts schema changes so that the downtime can be mimimized.

Vamos para mais um top 5 habilidades: 1. Planejamento e Desenvolvimento de Infraestrutura de Dados: Capacidade de projetar e construir sistemas de dados eficientes. 2. Manuten??o de Fluxos de Dados: Habilidade em garantir a qualidade e disponibilidade contínua dos dados. 3. Conhecimento Técnico Avan?ado: Competência em arquitetura de dados, modelagem, integra??o e computa??o em nuvem. 4. Proficiência em Ferramentas de Engenharia de Dados: Domínio em ferramentas como ETL, Hadoop, Spark e AWS. 5. Atualiza??o Constante: Disposi??o para se manter atualizado com as últimas tecnologias e tendências no campo da engenharia de dados.

In my experience, data engineering is a vital skill that any wanna-be BI professional should strive to hone. We all know that - "Data is the new oil". So, unless we know how to extract the data, transform it and load it to target system (ETL) or, load first, transform later (ELT), deriving actionable insights from that data would stay a dream. Data cleansing, transformation and curation takes up huge bulk of what a data engineer does as his/her/their day job.

Visualisation is the product of a good backend. Understand your system architecture, relationships, refreshing, and limitations. With those key elements, you will be able to effectively communicate capabilities and expectations to your audience.

I actually disagree with the fact that a strong mathematical background is required. This could have been the case few year back but it is not the case anymore. Yes, you need to understand the basic concepts to understand what is happening under the hood. But you do not need to be able to derive proofs or explain algorithms. Todays library’s like the ones mentioned above does most of the heavy lifting for you. I believe that having experience and experimenting with the tools available with get you covered in the beginning. You will need the math if you are in academia or working on a novel algorithm, or perhaps when optimizing an existing one.

Machine learning adds a predictive dimension. Applying ML models to customer churn prediction, I saw tangible results. The system's accuracy reached 90%, enabling proactive retention strategies. This not only saved revenue but also showcased the practical impact of integrating machine learning into business intelligence.

In my personal experience I’ve realized that a robust background in computer science, mathematics, and statistics is vital for applying machine learning in data science. Proficiency in tools like Scikit-learn is essential, which I’ve used it often when forecasting. I've also noticed that machine learning still requires a nuanced understanding of business knowledge, domain field is important. For instance, in my case, when applying machine learning to analyze technical education data, like students growth prediction or learning indicators, involves tailoring goals and metrics to highlight program success rates, industry placement, and student and families impact on their lives, while data helps define good education policies.

Agreed, in respect to Mousa's post, one doesn't need a strong mathematical background to use power BI. The whole point of a program like this is to make it easier to tell a business story (performance, metrics, KPI'S, etc ) instead of having to look under the hood each time. I started using MS excel many years ago before AI, before there were so many helpful YouTube videos, and I recall it was challenging at times to go check formulas in the cells when running complex calculations. Fortunately, now with newer technology including AI tools, we can spend more time on creativity and telling the business story. What I do see as essential is having a foundation in DAX formulas and the willingness to explore the creative tools in Power BI.

1.Conhecimento em Ciência da Computa??o, Matemática e Estatística: Base sólida nessas áreas é fundamental para compreender os fundamentos do machine learning. 2. Domínio em Técnicas de Inteligência Artificial: Capacidade de aplicar técnicas avan?adas, como classifica??o, clustering, regress?o e processamento de linguagem natural. 3. Proficiência Algoritmos de Machine Learning: Compreens?o dos algoritmos utilizados e suas aplica??es práticas. 4. Familiaridade com Ferramentas e Bibliotecas: Habilidade em utilizar ferramentas e bibliotecas populares, como TensorFlow, Scikit-learn e PyTorch. 5. Capacidade de Aplica??o Prática: Ser capaz de aplicar conhecimentos de machine learning em projetos reais, gerando insights valiosos a partir dos dados.

Beyond crunching numbers and crafting visually appealing dashboards, a BI professional with solid business acumen understands the narrative behind the data. This skill empowers them to translate complex findings into actionable recommendations that resonate with stakeholders at various levels of the organization. It's about more than just delivering reports; it's about providing valuable insights that contribute directly to strategic decision-making. In my own journey, I've learned that business acumen is not a static skill but a dynamic mindset. It involves staying abreast of industry trends, understanding market dynamics, and anticipating the evolving needs of both internal and external stakeholders

In my opinion, having critical thinking skills is crucial for working with data, regardless of the field. Critical thinking is indispensable for professionals as it helps them steer clear of biases, errors, and fallacies in their data analysis and visualization. This, in turn, ensures the accuracy, reliability, and validity of their data insights.

Critical thinking along with business acumen make BI professionals to full stack BI professional who will be excellent asset to the team. Critical thinking provides professionals to understand the value of data, visualisation and define well structured ROI. This will also help them to define predictive and prescriptive analytics to be added to their work.

The critical thinking step of processing needs to be surrounded by a solid process. This process need to evolve through DFMEA, FMEA and your control plan. Having absolute quantitative data is a first in prioritizing your “CT”. Always be aware of subjective influences and that they do NOT out weigh your true data for CT.

Critical thinking in my role involves rigorously analysing data to challenge assumptions, identify patterns, and solve complex problems. It's about asking the right questions and scrutinising the data from multiple perspectives to ensure that the insights we derive are robust and actionable. This skill enables me to navigate through vast amounts of data, discern relevant information, and propose solutions that are both innovative and grounded in data. By applying critical thinking, I ensure that our data-driven decisions are not just reactive but strategically anticipate future trends and challenges, thereby empowering the organisation to stay ahead in a dynamic business environment.

- Data preparation and transformation (SQL is essential, ETL tools) - Data visualization (+ any BI tool) - Soft skills, like communicating issues, resolving. Requirements gathering to get business users what is needed. Storytelling. I don’t personally believe that hard skills with data like statistics or modeling is needed, but still nice to have.

1. Data engineering (Clean, Transform, Feature Engineering) 2. Data modeling 3. Data analysis (SQL, ML) 4. Data visualization 5. Storytelling

Data modelling and conceptual modelling It is necessary to take the time understanding the business concepts and interrelations in order for you to design the data model that supports the organization needs.

Also, non IT professionals should have statistical analysis skills. IT professionals and developers should have also skills in database management, data analytics, and in programming languages such as R and Python, besides the most important database language, SQL.

Its important to colloborate with stakeholders, carry out hackathons, willingness to try out new tools ,be a student ,experiment ,try out quick wins , talk about success stories ,build credibility amoung peers will help establish.