How can you make data analysis more actionable and relevant?

To make data useful, you'll need to know why you're looking at it. What's the story you want to tell? Make sure the data you have is correct and current. Pick ways to show the data that make key points stand out. Before diving in, know what you're trying to achieve so you stay aware of the details. Use tools that help you reach these goals. Feel free to try different ways to look at your data. You can test your ideas and change your approach based on your learning. This way, your data analysis will be apparent, to the point, and helpful.

You can spend many resources and hours gathering data and developing an analysis, and after all this effort, it can all be wasted because people didn't use it to make decisions. It all starts with understanding the actual value of any data analysis, enabling action, and informing decision-making; to do so, you will need to understand the key factors that influence a problem, what trends matter, and what situations might cause a need to change course; once you have those in mind you can start and develop your analysis to search for signs that an action is needed.

Set your goals in advanced . Have a detailed list, or at least good bullet points of your targets. You should create a list of guided questions which will help you getting better answers later. Based on these questions, you can define the KPI's to check them. You should also define some follow-ups questions , which can be modified based on the results from previous steps. Have a reliable source of data , so your results are accurate or can be checked and compared. Always take into consideration that the info is dynamic and may need to change on a high frequency until you get the final results.

Making Data Actionable: A Quick Guide Data is a treasure, but sorting it is the real quest. Giants like Samsung and Garmin agree. According to Forrester, data-driven firms are 58% more likely to hit revenue targets. So, how to get actionable gold from data dust? Context: Tie metrics to KPIs. Don't let data be the "so what" at the party. Prioritize: Not all data deserves the VIP treatment. Pick what impacts your bottom line. Visuals: Our brain loves pictures. Use visuals for 60,000x faster understanding. Track: HubSpot says consistent tracking makes you 20% more likely to hit goals. In short, don't just collect data; make it actionable. The goal is not more data but smarter insights. Cheers!

Analysis if done with the exact objective in mind, can certainly help in informed decision-making. Here's a glimpse of the 'Hows', ; *By briefly Summarising the objective concisely rather than presenting more different ways *By highlighting the critical aspects that can drive the decisions ##Less is More!

Knowing your purpose in data analysis means having a clear idea of what you want to achieve and why you're looking at the data. It's like having a plan before you start something, so you stay focused and don't get lost in the details.

Data analysis is the process of gathering, modelling and transforming data so as to get useful information, suggestions and conclusions in decision-making. Appropriate analytical tools are used to convert raw data into information that can be used in marketing research. Percentile and ratios are some elementary methods of data analysis. Some statistical methods such as mean, median, mode, percentage, and standard deviation should be used as per requirement. Tests of significance, factor analysis, multiple discriminate analysis, and regression analysis can also be used as advanced statistical tools.

Albert Einstein was once quoted as saying, “If I had an hour to solve a problem and my life depended on the solution, I would spend the first 55 minutes determining the proper question to ask … “. Until you have properly framed your question, you cannot begin your research as you have no direction. Once you understand the challenge or opportunity you are investigating, you can then begin collecting your data. This not only makes your analysis more relevant but it helps you use the data to develop and implement an actionable solution.

To ensure that your data model is accurate and effective in solving the problem at hand, it is important to follow a few key steps. These steps include: Clarify the questions first to avoid getting trapped in a sea of assumptions. Clarifying questions are simple questions of fact that clarify the dilemma and provide the nuts and bolts so that the participants can ask good probing questions and provide useful feedback. Define the problem statement clearly and mark the goal as the finishing line. Form step-by-step processes to ensure that the right output eventually is an input to the next process. By following these steps, you can ensure that your data model is effective in solving the problem at hand and output is accurate.

Choosing the right format for data analysis is like dressing for an occasion; you wouldn't wear flip-flops to a business meeting, right? Different formats serve various needs. Dashboards offer a quick pulse-check, ideal for busy execs needing real-time updates. Reports dig deep, perfect for analysts seeking to understand the why behind the what. Presentations? They're your persuasive tool, designed to spur action. It's not just about showcasing data; it's about delivering it in a way that resonates with your audience's needs and expectations. In essence, the medium is part of the message. Choose wisely!

Numerous readily accessible user-friendly templates are at your disposal to enhance the presentation of data to your target audience. Take your time finding the right tool or else you will easily loose your audience engagement and hinder the achievement of your primary objective.

Para 2024, o nosso time de análise de dados está trabalhando em uma melhor visualização de dados para a nossa gerente comercial. Percebemos que o formato com que as análises estavam sendo demonstradas não estavam atingindo o seu objetivo principal: Apresentar os principais indicadores comerciais em um só lugar. Sendo assim, o trabalho que já foi feito não será em vão, mas precisará ser revisado e colocado em um formato que permita com que o acesso à todas informações para acompanhamento de resultados e tomada de decisão seja prático. Em análise de dados, o planejamento é tão importante quanto a execução.

The secret to success is not to choose the right format or channel to communicate data and analysis--it's to develop a communication plan that encompasses all of them. There is too much reliance on dashboards, but while dashboards are essential, they are terrible at sustaining attention and encouraging action. Alerts and automated reports are part of the solution. But so is analysis that doesn't just tell people WHAT is happening but WHY and WHAT THEY SHOULD DO ABOUT IT. Consider the DIKW pyramid and ask yourself if you are merely communicating data (NPS is 28), information (NPS is down), knowledge (NPS is declining due to product quality), or wisdom (This feature is frustrating customers and must be improved.) Share wisdom, not just data.

This is so important! If you choose the wrong format, you may loose the connection to the audience before you even start. Important to use corporate templates, colors and fonts, as well as the prefer charts of the audience to guarantee more time selling your idea and connecting to the audience, that explaining the charts.

You should ensure that your report and provided data are presented in a simple /clear way . Based on your audience ,In most cases it should be short and simple. You need to present the facts , and allow the viewers to present their response and feedback.

Choosing the right format is crucial, it's equally important to consider the evolving digital landscape, accessibility, and user feedback to ensure your data presentation is both practical and user-friendly.

The right format can make data easier to understand for your audience. Whether it's through charts, graphs, tables, or infographics, a visually appealing format can convey complex information more clearly. But more importantly, it can impact engagement. An engaging format can capture and maintain your audience's attention. Interactive visuals or storytelling through data can keep people interested and invested in the information being presented.

You have to grab eyes before minds! Choosing the suitable formate lets others dive deeply in you analysis easily and you have to choose the format which represents you analysis well.

Don't just tell "a story." Tell people what they need to know in a way that says why they should care. If you are using data to influence decisions, you must tell people: - What in the data is important to know - Why it's important - What they should do about it Too often, people think that merely sharing data about a trend will be enough to affect decisions. It isn't. That leaves it to decision-makers to evaluate the data and know what to do, and decision-makers are busy and overwhelmed with data. Instead, tell them what they need to know, why it's important, and offer one or more options to address the problem or exploit the opportunity.

Storytelling is often likened to the "executive summary" of a detailed report. No matter how comprehensive the research, without a concise and compelling narrative, the core message is lost. Every good data story has three elements: relevant business context, strong visual insights, and an actionable narrative that drives actionability.

Data storytelling is more than just graphs; it's making numbers speak. Start with a hook. Identify a key metric that shatters expectations, inviting curiosity. In the "rising action," delve deeper. For example, if sales are up, illuminate the specifics—was it a holiday, a new feature, or a marketing push? Unearth the "why" to add layers to your narrative. The climax is your pivotal insight, underpinned by rigorous analysis. It's the point where the data coalesces into a revelation that could steer future strategies. Conclude by connecting the insight to larger business goals. In this way, you forge a compelling story that doesn't just inform but compels action. Your data turns from being a side note to the protagonist in your business saga.

Data analysis transcends mere numbers and charts; it's a narrative weaving context and goals. Elevate your analysis by sculpting a story—guide your audience through the data, spotlight key insights, and reveal connections to the broader context. Employ a clear structure, logical flow, and a compelling hook to captivate and embed your insights in your audience's memory. Because in the world of data, a compelling story is the bridge between information and impact.

Data analysis is a bit like telling a story. Data is like the story waiting to be found. Data analysts, like storytellers, look for important details and patterns, just as a storyteller looks for characters and the plot. So, in data analysis, you're not just dealing with numbers. You're like a storyteller, and what you do can help people make decisions, solve problems, and improve things.

To make data analysis more actionable and relevant, start with a clear understanding of your objectives and questions. Focus on collecting and analyzing data that directly aligns with your goals, ensuring it's of high quality and relevance. Employ visualization techniques to present insights in a comprehensible and compelling manner, making it easier for stakeholders to grasp key findings. Collaborate with domain experts to contextualize the data and extract meaningful interpretations. Prioritize actionable recommendations and insights that can drive decision-making, and regularly communicate these findings to the relevant teams or individuals.

Visualization in data is the boardroom's equivalent of a master orator. Just as an eloquent speaker can captivate an audience with well-chosen words, effective data visualization captures and directs the attention of stakeholders. As Edward Tufte, a pioneer in the field of data visualization, aptly said, "There is no such thing as information overload, just bad design."

Data visualization serves as the interpretive lens for complex data sets, amplifying clarity and expediting comprehension. Utilizing tools like scatter plots or heat maps can unveil multidimensional relationships, while bar charts can crystallize comparative metrics. However, there's a caveat: the risk of visual overload or misrepresentation. To mitigate this, adhere to principles like data-ink ratio to optimize clarity and Tufte's "lie factor" to ensure honest scaling. Choose the visual medium that is both congruent with your data type and resonates with your target audience, be it a C-suite executive or data scientist. Remember, the best visual is the one that makes the complex simple, but not simpler.

Like a skilled orator who tailors their message to resonate with a specific audience, effective visualizations are tailored to the needs and preferences of board members. They consider the audience's level of expertise, priorities, and preferences, ensuring the message resonates effectively.Data visualization is not just about presenting numbers and charts; it's about crafting a compelling narrative, guiding the audience through insights, and inspiring action based on data-driven insights. Just as a master orator captivates with words, visualization captivates with images, both serving as powerful tools for effective communication and decision-making in the boardroom.

Translating your insights into an easily understandable graph or visual is extremely important. This helps your audience connect the data to your insights.

If “Raw Data” are considered as an ingredient then “Data Visualization” is no less than as “Prep Meal”. Without decent and easily understandable data visualization and presentation format, data is of no use since it plays a major role in translating the beforehand complex data to all considered parties.

Based on our experience with leading brands like Samsung and Garmin, providing actionable recommendations is the linchpin for effective data analysis. It's not enough to merely present data; you must also guide decision-making. Your recommendations should be S.M.A.R.T: Specific, Measurable, Achievable, Relevant, and Time-bound. For instance, if your analysis reveals a decline in customer satisfaction scores, propose a targeted customer feedback initiative within a specific timeframe. Back this up with data, such as correlating lower satisfaction to reduced repeat purchases. This bridges the gap between analysis and action, underscoring your value as a data interpreter.

Quando penso em como irei fornecer recomendações, me faço às seguintes perguntas: Quem é o meu interlocutor? Qual é a melhor forma de apresentá-las? Como iremos mensurar os resultados das tomadas de decisão a partir dessas recomendações? Reuniões com diversos setores necessitam de um aproximação mais ampla em sua comunicação, sendo necessário traçar metas específicas, vinculando as contribuições de cada setor. Reuniões com setores ou decisores específicos, precisam ser focadas em promover uma discussão compreensível e vinculativa às responsabilidades do seu interlocutor, sendo possível se aprofundar mais no assunto sem perder o foco. Fornecer recomendações é uma ótima oportunidade de promover a tomada de decisão baseada em análise.

My go-to method to provide valuable information is to continually ask myself; 'What does this mean?'. It reminds me that the evaluations need to be concise and easily explainable. I gather conclusions based on the 'story' the data tells, and ensure it is in context and alignment with the overarching objective for the analysis. It's important to stay open minded so as not to misdiagnose or misdirect the investigation. I turn actions into 'scenarios' and break them down as granular as possible; making it simpler to allocate resources and put a timeline to the proposal. If my report is going to several stakeholders with differing objectives, I create tailored recommendations to demonstrate the value and consequence of each action.

I'll back up the point that feedback is a task for the entire lifecycle. Develop your initial goals together with feedback from the customer base. Check in periodically with those or different customers as you see hypotheses forming. Vet out final results. (FWIW: I call this Community Driven Product Development but I think it applies to data gathering too)

When you want advice about your data work, it's important to listen to what others say. Their helpful suggestions can help you find mistakes, learn new things, and make your data work better. Also, when you get your coworkers or experts involved, it can make your data work even stronger and more trustworthy, which is really useful when making decisions.

Focusing on the bigger picture and applying system thinking in data analysis allows annalysts to see interconnectedness and dependencies among variables. This approach helps in identifying root causes, not just symptoms, thereby leading to more effective solutions. It also aids in anticipating unintended consequences and feedback loops, resulting in more sustainable decisions. Overall, the approach facilitates actionable insights that are contextually relevant and strategically sound.

Data integration and triangulation is such a powerful tool that very few organisations do well. I was surprised to learn that a retailer I buy some clothes from does not integrate their in-store versus on-line databases . If they could, what an interesting view they would have of me, their customer. They would learn that whilst I am happy to buy tops online, I only buy pants and skirts in-store. They would be able customise what to send me as EDMs and find the right incentives to bring me into the store.

Dive Deeper Diving deeper into data enables you to be more confident when it comes to decision making or telling a particular story. Take for example you are asked to do a WoW or MoM revenue analysis. Just aggregating the data and performing the analysis won't provide better insight as opposed to doing deep dive on each of the verticals that drive revenue growth, and also doing trend analysis. In addition to just looking at the data alone, it is a best practice to consider seasonality, in that way you will have an actionable data.

1. Understand Goals: Clarify the objective behind data analysis to ensure relevance and actionability. 2. Clean Data: Ensure data is accurate and clean to make analysis reliable. 3. Relevant Metrics: Focus on key metrics that directly impact decisions and actions. 4. Visual Representation: Use graphs and charts for clearer insights, making data easily interpretable. 5. Real-Time Analysis: Utilize tools enabling real-time analysis for timely decision-making. 6. Collaboration: Work closely with stakeholders to translate data insights into actionable steps. 7. Continuous Feedback: Obtain feedback on data reports to refine analysis, making it more actionable and relevant over time.

The most important thing in data analysis is the content.... the "cornerstone" is in the creation of data needed. Hence, all inputs should be considered by taking out "silos" in the organization & encourage collaboration between different departments. This is one thing that most of the time overlooked especially in a BIG organization.

Remember, the goal of data analysis is not just to provide insights but to drive action. Always keep the end goal in mind and tailor your analysis accordingly.