What do you do if you want to maximize insights with data analytics and visualization tools?

I recommend defining the objectives of the entire process, this is key to guiding the analysis to avoid being distracted on the journey. 1. Define clear objectives 2. Collect relevant data 3. Choose the right tools 4. Explore the data 5. Apply appropriate analytics techniques 6. Visualize the insights

Une chose fondamentale et cruciale, est la définition de l objectif. Quoique on dise, ce sont les hommes qui font l organisation. Si vous n êtes alignés avec les dirigeants (demandeur du service) sur les objectifs, vous risquez de faire un travail remarquable mais inutile qui va ensuite aller à la poubelle. La définition des objectifs vous oriente tout au long du projet, depuis l identification des données nécessaires, la collecte de celles ci, jusqu'à la présentation des résultats en passant par le choix des méthodes ou techniques d analyse.

Data analytics involves extracting valuable insights from raw data to drive informed decision-making. Visualization tools aid in presenting complex data in a comprehensible format, facilitating better understanding and interpretation. Effective data visualization techniques, including charts, graphs, and maps, enhance comprehension and facilitate decision-making. Develop interactive dashboards to enable stakeholders to explore data dynamically and gain actionable insights. By following these steps and embracing a data-driven mindset, organizations can unlock the full potential of their data to drive innovation, improve decision-making, and gain a competitive advantage.

It's imperative that you ask the why, atleast five times , to the stakeholders. By asking "why" five times, we've delved deeper into the stakeholders' motivations and uncovered broader objectives beyond the initial objective.

By far the most important - Understand the main goal of the organization/dept. Deliver insights that help them meet those goals.

The caliber of your insights is linked directly to the quality of your data. It is important to invest in time and resources to establish a credible data collection process that provides a strong data set base for analysis.

There is a soup known as "Oha" (made in the Eastern Part of Nigeria) it is made from a specific kinda vegetable that using any other types of vegetable won't give you that "Oha" vibe. The above analogy is true for the kinda insights/problem you wanna solve. If you don't have the right data, you will end up given wrong solutions. Getting the right data for your analysis cannot be over emphasised.

The first thing that has to be setup is good structure for data consumption and processing. Factors such as data massaging and cleaning shpuld eb considered before choosing the tools to be used for visualization and analysis. The complex computations should be done at the backend and less load should be there on the visualization tools. Usage of views instead of direct accessing the tables enhances the processing time. Less cluttered vizzes should be preferred and an information icon with necessary information should be provided at the end for terms that many might not be familiar with

I would say here that your analysis will be as dirty as your unclean data. So, once your data is gotten, it is important you prepare such that to fit into your defined objectives by cleaning it and performing other data preparation tasks.

Investing 80% time on ETL and 20 % on visualisation Will maximize insight. During ETL we can divide T transform into two parts on one part we perform cleaning and on the other part we transform as per client's needs.

This is where careers are made. There are many data experts, but this is the area where a true data person can shine, get excited, motivated and unlock so much value from an organization's data about their business, customers, products, vendors, marketplace, competitors, sales, HR - you name it! There will be a lot of false positives, ambiguities, confusion, clarifications, iterations. Do not be disappointed! Keep peeling off the insights, 1 layer at a time. One may need to completely overhaul and design new data models, or tweak existing ones. There will be many 'Aha' moments. Data analysis and discovery will lead to both descriptive and prescriptive solutions that benefit the business.

One thing to consider is the expected pattern of the data you are analyzing. For example, sales data tends to be cyclical with and upward or downward trend, while train wait times would have a Poisson distribution. This would be a good sanity check for whether your analysis is in the right direction, and may help you to spot anomalous data distribution/movement

Needless to say, this needs data/technical skills, and also someone creative. A decent combination of art, science, technical acumen. A heat map may be appropriate for a specific situation, not in the other. A bar chart may totally have a different look/interpretation when you change certain time filters. A graph may not appeal to a finance professional as much as it does to a sales person. During visualization activities, one must be agile and flexible enough to change and experiment with various options available, and also be cognizant not to go overboard with colors, fonts, size (depending on the audience). So, adjust and customize the visuals to your stakeholder you want to impress.

Data visualization is very important, again, it is important to have quality data to be able to give the insights for your analysis

This is one of the most challenges in our business. I saw many times, good teams extracting data, creating beautiful dashboards but sometimes they don't consider how these data will be consumed. In that step, the team needs to emphasize with the "on premise clients" and give them the answer they want to. I used to say "Something well done in a simple way generally brings more information that something complex and done shallowly.

Once you have a strong understanding of your analytics pipeline, it may be valuable to begin to consider why the data is distributed the way that it is. Modeling assumptions such as interest rate changes, population growth, exchange rate changes, market competition etc may help a data analyst to explain the short term and long term data movements which would be useful for senior leaders to take necessary steps to adjust their shorten and long term business plans accordingly.