How can you manage deadlines with large data sets?

After defining the outcome you desire and defining the KPIs or definite metrics, use tools like KNIME for a proper analysis of your data. Take a sample first and try to do simple univariate, bivariate and Multi variate analysis to define if the data can effectively support your effort. Understand the deficiencies in your data and this will help you define a clear timeline as well as set the right expectations with stakeholders. This data discovery phase can go a long way in helping you accelerate your efforts.

Begin by creating a detailed project plan that outlines all the tasks, milestones, and deliverables. Break down the smaller manageable phases to make it more achievable Assign responsibilities to team members based on their expertise and availability

Gérer les délais avec de grands ensembles de données nécessite une combinaison de planification efficace, d'organisation et de collaboration. Voici quelques astuces : - Comprendre les données : Compréhension des données afin d'identifier les variables clés et leurs relations. -Compréhension du métier (Business understanding) : Une collaboration pour aligner votre analyse sur les objectifs. - Segmentation des données : Cela peut aider à répartir la charge de travail et à faciliter la concentration sur des aspects spécifiques de données. - Génération d'hypothèses : Formuler des hypothèses en fonction de votre compréhension des données. -Communication régulière : Maintenir une communication ouverte et régulière .

Gestionar los plazos con grandes conjuntos de datos puede ser un desafío, ya que estos conjuntos de datos pueden ser muy grandes y complejos. Sin embargo, hay algunas estrategias que se pueden utilizar para mejorar la gestión de los plazos en este contexto. Una de las estrategias más importantes es establecer objetivos claros y alcanzables. ?Qué resultados se espera lograr con el análisis de datos? ?Cuánto tiempo se tiene para alcanzar estos resultados? Una vez que se tengan claros los objetivos, se puede desarrollar un plan para alcanzarlos.

One thing I find very helpful when planning a data project is to identify who are the key stakeholders and what are their concerns or needs related to the project. This will be the very first step of the project and the basis of the planning so we know what are the "stress-points" of the project Talking from experience, with today's advanced technology and good team members, we can complete most data projects quickly ahead of schedule. But getting buy-ins from Stakeholders and revisions takes a lot of time to the point the project barely meets the deadline

Two other points to add to this list are: (1) to consider the need to future-proof and (2) customisability of the tools Having a future-proof tools with high customisability to specifically suit your need will be very expensive, either in terms of effort, financial or both. To balance the two, I would suggest to future-proof your infrastructure with a generic structured dataset, then do the customisation-heavy tasks on the analytics side

There is a definite gap between marketing and data science teams. While the latter are more proficient in the tools and tech to support the decision making, they lack the understanding of the data at hand. It helps for a marketer to scale up their understanding of the capabilities of the tech stack being used. In most companies, these tools are legacy purchases and there’s little flexibility or budget to incorporate new ones. So scale up your understanding of the tech and define your dashboards and work top-down by defining your storytelling flow in the first place. When you have outlined what data can help you populate what metrics, let the data science team lead the effort of translating that requirement.

I definitely agree with all 1-5 points in this article. Plan ahead to avoid double-work and optimize project structure before starting. 6. What I think is probably overlooked most of the time when working with big data, is to use coding principles such as "Single responsibility principle" and objective code structure: Basically structure your code in a reusable way by dividing the code into multiple files and each function you write in the files should in principle only have a single responsibility (subjective of course). On top of that keep a template (text file) of functions and short code samples you use often. Using these principles, you can easily re-use your previous code for a faster workflow when planning new big data projects.

Leverage automation tools and scripts to handle repetitive and time-consuming tasks. Automation not only saves time but also reduces the likelihood of errors in data processing.

When it comes to working with big data, storage should be one of the first things to consider. Cloud computing platforms have made it easier for big data to be easily scalable and accessible. When starting out a big data project, deciding to use the cloud as your data storage tool is wise and making the choice of the cloud platform to use in the early stages of your project is also very important and beneficial in the long run.

In the topic of automation and optimisation, I personally would prioritise having low (or even zero) error rate before optimising the process. Getting the right message across is much better than getting the message quickly but with arguable credibility

If applicable, explore options for parallel processing. Distributing tasks across multiple resources or processors can significantly accelerate the processing of large datasets.

Data integrity right from the very start is very important. The necessary procedures needed to have a well cleaned data is needed . And I think the use of AI can really help when it comes to cases like this.

During my professional career I have learned that automation and optimisation helps to save a lot of time with traditional reporting and you can spend that time on finding trends and actionable insights from the data.

Since most data project involves writing codes / formulas and each team member usually have their own way to write them, it is essential to follow a common standard and write clean codes / formulas. This is especially crucial in the overlaps of team members' scopes, i.e. when the codes / formulas from 2 different team member meets In my experience developing spreadsheets collaboratively, this can be achieved by having template sheets that team members will fill-in at the scope overlap

Collaborating with other data experts makes work easier, delegating different aspects of the work makes it faster especially when working with a large dataset. Working with tools that allows editing in real time also helps. That way all the collaborators will be involved in the updates.

Allocate resources wisely. Ensure that team members with the appropriate skills are assigned to specific tasks and that they have the necessary tools and support to meet deadlines.

Working as a team makes your life extremely easy. This not only help you meeting up with the deadline but also helps take away the pressure which then helps you give more attention to details needed.

This step must be related to the stated goals, objectives and KPIs stated in the first step. I believe in the saying: "If it's not broken, don't fix it". So unless there's a very good reason to revisit the code / formulas (e.g., improving quality of the insight), focusing the effort to maximise and expand the impact of the data project will be much more valuable

It's a good practice to always go back to a project to check how it's going. But if there's no reason to make some adjustments, then let it be. If it's not broken then there's no reason to fix anything.

Review and refine can make your results more accurate. Sometimes we don’t get expected results for the visualisations but they help you to understand the problem and helps you in refining your data and make your results more accurate.

Review and optimize code, queries, or algorithms for data processing. Efficiency gains in data manipulation and analysis can significantly impact the overall project timeline.

Changes are always there in data and the project provided by the clients,may be we need to just quickly understand the KPI..and answer the questions but as we go make necessary changes that affect us less.

What defines a valuable data project is how we define the objectives and it fits to the organisation needs. In terms of tools used to execute the project, most of the time, a simple solution is good enough for your organisation needs and will save a lot of your resources (money, time and manpower). At one point, additional complexities require high effort to implement but only add incremental value to the final objectives

To manage deadlines with large data sets, you need to store your data securely, visualize it, record your workflow, and break down large tasks. Storing your data securely means creating automatic backups and sharing it with your team. Visualizing it means making graphs and checking for outliers. Recording your workflow means documenting your data version, clean-up steps, and processing code. Breaking down large tasks means dividing large project into smaller steps, such as researching, outlining, drafting, and editing.

Allocate resources wisely. Ensure that team members with the appropriate skills are assigned to specific tasks and that they have the necessary tools and support to meet deadlines.

To beat a deadline when working with a large datasets you have to know that, working as a team with other experts in the field is never a bad thing, data integrity is key as this makes the work flow easy and also the important tools should be used. Don't try to show off the numerous tools you know how to use because sometimes it only ends up you wasting your time.