How can data and analytics help program teams make better decisions?

I believe the “Why?” aspect holds the most significant part of any problem over the “How” and “What”! Being a data analyst myself, I always start with Why do I need to do this analysis and why does my client need this? This defining holds the heart of any given problem. One common mistake people tend to do is start with “What”, then go about “How” to do it, and then end with “Why” they did what they did. But, it should be the opposite way around for any data analysis. If we follow in the common trap, we might end up doing an analysis without purpose. Even when we consider Six Sigma, DEFINE is the first step in bo to DMADV and DMAIC! Defining the problem and understanding why you need to solve it is the correct way to go about it.

Data needs, according to me, are like figuring out exactly what kind of information a business needs to meet its goals. I look at where the data comes from, what type it is, and how it's organized for each specific issue. It's also important to set standards for things like data quality, security, and governance. The specifics of what data is needed can change depending on the project, like how big it is or how complicated the analysis is going to be.

In the context of retail, data and analytics are valuable as they work to enhance customer experience, optimize operations, and drive business success. By leveraging data and analytics in customer segmentation, inventory management, optimizing planograms, customer journey analytics, customer feedback analysis, e-commerce analysis, and basket analysis, retail program teams can make informed decisions that drive sales, improve customer satisfaction, and optimize overall retail operations. However, for all these to be achieved, it's essential for retailers to invest in robust data infrastructure, analytics tools, and talent to derive actionable insights from their data.

Data and analytics help program teams by providing insights into performance and identifying trends, enabling them to make informed decisions and optimize outcomes. This leads to more efficient resource allocation and improved project success.

When collecting data, take note of primary and secondary sources of data. Evaluate them to see if they are fit for purpose, then process them to see if they are as relevant as you initially thought. Sometimes, a data source might be useful and relevant by itself, but when stitching it with other data sources, there might be reconciliation and other issues, so be careful of collecting data. It should be more relevant and coherent in the big picture. For storing data, stick to company-approved storage spaces which have been vetted by the organisation's IT security team, and if any data issues are detected, let the team know immediately. Other than cloud storages and shared drives, note that transfer of files need to be secure as well.

Data Collection & Storage: Key Steps for Success - Diverse Collection Methods: Utilize surveys, interviews, etc., like using the right tool for the right job. - Accuracy & Consistency: Ensure data reliability as if you're double-checking the ingredients in a recipe. - Data Protection & Privacy: Follow best practices like adhering to safety standards in construction. - Centralized Storage: Use databases or cloud services, akin to storing all tools in an easily accessible toolbox. Efficient collection and secure storage are the backbone of trustworthy data analysis.

Um ponto e a coleta e armazenamento de dados que s?o elementos cruciais na era da informa??o, e é essencial abordar essas práticas com uma perspectiva robusta de privacidade. Ao implementar técnicas como pesquisas, entrevistas e observa??es, é crucial assegurar que os dados sejam coletados de maneira ética e transparente, respeitando a privacidade dos indivíduos. A precis?o e consistência dos dados, aliadas a práticas recomendadas de prote??o junto aos comprimentos das leis como GDPR e LGPD, fortalecem a confiabilidade do processo.

Charlie Munger once wisely noted, 'The more you know, the more you realize you don't know.' This resonates deeply when it comes to collecting and storing data. As a consultant, I emphasize the Munger philosophy of acknowledging the limits of our knowledge. In practice, this means creating a data system that not only collects diverse and comprehensive information but also remains flexible and open to new, unexpected insights. By doing so, organizations can build a robust database that serves as a solid foundation for growth and learning, ensuring they are always prepared for the unforeseen, just as Munger advises."

Once you begin collecting and analyzing data, you’re likely to find that it’s easier to reach a confident decision about virtually any business challenge, whether you’re deciding to launch or discontinue a product, adjust your marketing message, branch into a new market, or something else entirely. Just because a decision is based on data doesn’t mean it will always be correct. While the data might show a particular pattern or suggest a certain outcome, if the data collection process or interpretation is flawed, then any decision based on the data would be inaccurate. This is why the impact of every business decision should be regularly measured and monitored.

Less is more in visualisation. Ask yourself who you audience is, what experience do they have and what is there skillset relating to interpreting data analysis and visualisation. Visualisation should be used to tell the story, lead the reader to the answer and make interpretations easier.

There is no reason to collect the data if you don't intend to analyze it. Make sure to review your data, visualize and analyze it in order to have metrics and make decisions about your program. Data analysis will show you whether the program is on the right track, whether you are going to achieve your goals and whether you should continue the program.

Analyzing and visualizing data is a crucial step guiding feature selection. It's vital not to overlook any impactful feature in your decision-making model. Two approaches exist: the orthodox method explores essential parts based on your perspective that you learn during your initial stages, while the second employs interactive libraries like ydata profiling, autoviz, sweetviz, etc. Despite potentially exploring more, these tools prevent omitting vital factors. Their detailed statistics and visualizations contribute significantly to a comprehensive understanding.

And during visualization avoiding so many contrasting colors, but instead go for complimenting colors. As this will not only help give a better analysis, but also captivating and tells a better story, with color patterns.

Charlie Munger astutely remarked, 'All I want to know is where I’m going to die, so I’ll never go there.' This humorous yet profound insight speaks to the power of foresight and understanding, which is exactly what data analysis and visualization provide. In my consultancy, I emphasize Munger's approach to seeking knowledge and clarity. By analyzing and visualizing data, organizations can uncover patterns and predictions that guide them away from potential pitfalls and towards opportunities. Effective visualization turns complex data into clear, actionable insights, enabling teams to make decisions that are both informed and intuitive, a balance that Munger himself might appreciate.

Don't share all the data analyzed. People have short attention spans, and if you lose them, you don't get your message across and your actionable recommendations won't get actioned. Only present data that forms part of the main findings, and helps to tell the story in an effective way. Keep communication simple, clear and concise.

One of the most eye-opening seminars I've attended was around data visualization. Effective communication in colors, shapes, and locations on a plane. A solid visual representing the story goes a long way towards engaging the audience and building credibility. And if you're excited to tell them about that, good energy always helps.

Knowing your audience is key! Tailor your presentation to their level of expertise and interest. Avoid technical jargon for non-expert audiences and use your visuals wisely. Ensure they are clear, accurately labeled, and free from clutter. Tell a story. Weave a narrative around your data to make it relatable and memorable. Highlight key findings and their implications through a compelling storytelling approach. Keep it simple. Overloading your audience with too much information can be counterproductive. Focus on the most pertinent data and communicate it concisely. Finally, engage with your audience. Keep them engaged through discussions and feedback, and by encouraging questions.

The data you send needs to be helpful to stakeholders to determine what is going on. Have profits increased or decreased? Why has there been a change? Could there be an issue with data quality? How can we find the root cause?

Its extremely important to understand the parity between the data and the audience. Our data and its views have to meet the requirements of stakeholders. As an example, the data needed by a Scrum Master will revolve around sprint metrics like velocity, defects per sprint or release, and production stoppers, whereas the one for product manager can be the projection of roadmap delivery and milestone dates. Thus as a PM, we can create a matrix of our stakeholders and their data requirements alongside the frequency, mode, and format of data delivery. Having said that, I am not a big fan of push based notifications, I am more towards pull based data delivery via real time dashboard.

Know your data: In addition to the technical aspects of processing, transforming and analyzing data, understand the caveats, strengths and nuances of your data. Many datasets will be incomplete or lacking in standardization, sometimes both - such datasets can still enable decision-making but may need to be supplemented with other data or leveraged only for a specific set of use-cases.

Using data for decision-making involves breaking it down into two sections: training and test data. After building and Hyperparameter-tuning your model, it's crucial to evaluate its accuracy and generate a classification report. These metrics provide evidence of your model's performance, guiding its implementation in the real world. Beyond model development, understanding how well it performs in practical scenarios is essential for informed decision-making.

Narrow down a goal or issue to prioritize for your data. List the various problems your business is facing. List the various curiosities your business has in regards to its campaigns or target market. Find one or more issues that require the most attention. Once you’ve narrowed down an issue, decide on what you need to improve, or which aspects are in want of more information.

Enhanced monitoring and evaluation are enabled through real-time tracking of key performance indicators, fostering data-driven decision-making and allowing comprehensive assessments of program impact. Optimized performance and efficiency are achievable by identifying bottlenecks, enabling data-driven optimizations, and predicting future outcomes based on historical data and trends. Increased collaboration and stakeholder engagement result from sharing data, promoting a shared understanding, facilitating collaboration, and improving transparency and accountability. Enhanced innovation and adaptability are driven by data-informed development of new strategies, rapid adaptation to changing circumstances through real-time monitoring

Use the result of your analysis and visualisation to make your decision making objective and quantifiable. After all, you started off the data process because you identified a question that needs an answer. If followed correctly, the result should be an answer to your question and help you objectively make decisions. Now, that does not mean it is always the answer or outcome you like or expect, however, it is a data based, objective answer.

Project management analytics enables project managers to identify potential risks, maximize resource allocation, get helpful information on the project's progress, make well-informed decisions, and seamlessly measure key performance indicators (KPIs)—all to enhance the project's results. The reports and dashboards enable project managers to identify trends, patterns, and potential issues at a glance. With the improved visibility of project data, project managers can make strategic decisions, allocate resources efficiently, and take proactive measures to ensure project success. It also helps project managers create more accurate, informed budget plans through the analysis of data from previous projects.

Según mi experiencia los datos en una empresa proporcionan información objetiva que puede ser interpretada de manera favorable o desfavorable por quienes analizan la información, especialmente los directivos. La percepción de si la información es positiva o negativa depende del juicio subjetivo del analista y se contextualiza de acuerdo con la realidad y el entorno de la empresa o entidad en estudio.

A jornada de aprendizado com seus dados é como cultivar uma sabedoria em constante evolu??o. Você molda planos de a??o com sensibilidade. Avaliar o impacto das decis?es n?o é apenas uma análise fria, mas uma reflex?o profunda sobre lacunas e oportunidades de melhoria. O objetivo é criar um ciclo de aprendizado contínuo, flexível e inovador, transformando seus dados em narrativas humanas que impulsionam decis?es mais conscientes e resultados mais significativos.

Arguably one of the most important aspects of data analytics is stakeholder management. Because analysts usually will not have a lot of expertise in the subject they are handling, they will need to study the topic in-depth and with relation to what stakeholders know and expect. This means poring over the data, understanding the nuance and context of the task and apply business knowledge to the task. In order to do that, analysts will often have to check with colleagues who are more proficient in those areas, preferably Subject Matter Experts who can answer queries reliably and readily. Unfortunately, in real-life scenarios, it is very difficult to do this well, but it is still crucial to manage stakeholders the best possible.

?? Prote??o dos dados: ?? Antes de mergulhar na coleta e análise de dados, é como planejar uma jornada. Você precisa parar e pensar: que informa??es s?o realmente necessárias? Por que precisamos delas? Como vamos usá-las? Essa reflex?o ajuda a garantir que cada peda?o de informa??o coletada seja valioso, e, ao mesmo tempo, protege a privacidade das pessoas envolvidas. é como construir uma rela??o de confian?a, garantindo que cada passo seja transparente, ético e legal.

An additional point would be to understand why we are collecting the data and for what duration. Data is growing exponentially and sometimes it's hard to make sense of the data collected if the purpose is not clearly defined. Deriving insights from randomly collected data is difficult

One usual example for data storage is to plot the cumulative size of the files or tables created in folder or database by time. It’s very easy to predicted when the storage will be full and trigger some actions for clean up routine.