How can you ensure data used in visualization is relevant and up-to-date?

Doing Visuals without basic knowledge of the correlation between your dataset and the reason for the visuals is actually a waste of time because insights generated would be irrelevant anyways, that's if there would be any at all. Common steps i take regarding this: I try to understand the stakeholders ask as this helps me trim out irrelevant data. This is key! Secondly, i ensure that my i jot down points to hit/Milestones with respect to the Task. This hleps me avoid the temptation to veer off the task i have at hand. Writing is old school yeah, but then might save one the stress of "what on earth was i meant to achieve here again?" (...from experience though).

For relevancy, don’t forget to think about the long term. A start point should be “how is the answer going to be used repeatedly and in various changed situations.”

Creating compelling and accurate visualizations requires relevant and up-to-date data. The process begins with clearly defining data needs, using a framework like SMART to ensure that the data is specific, measurable, achievable, relevant, and timely. Regularly updating and monitoring data changes is crucial, and feedback can provide insights into refinement or clarification. Ongoing learning and improvement, leveraging resources, and engaging with the community are vital for ensuring the visualizations remain relevant and practical.

One of the most beautiful thing I appreciate that I know earlier is that there is actually no way you can have a better visualization if you dont pen down the design already. Yes! You can actually get more insight into the data when randomly exploring through the visualization tool but when you are ready to make the most meaning out of it, either base on the request of the stakeholder or base on your insight that you want to bring to their attention to make data driven decisions, you must have it all planned out first else you will just be telling the wrong story to the right person.

To properly define the needs in creating a data visualization involves understanding the audience, the objectives of the visualization, the available data and the context. Typically, you would start by identifying what should be communicated, what the objective of the visualization is, and what insights should be highlighted. Knowing the audience well helps to choose the type of visualization and make it more accessible and understandable for the people who will use it.

"Every year, poor data quality costs organisations an average $12.9 million" - Gartner Several harmonization challenges exist which ultimately hinders an organisation's ability to leverage analytics and data visualisation to its full potential. For example, no common identifiers across the different syndicated and internal data sources, inconsistent data formats, product taxonomy mismatch, etc. This results in unharmonized and duplicative product and customer data without a single source of truth. Industry leaders are overcoming limitations of MDMs (rule based, not scalable, low accuracy, no feedback mechanism) using AI/ML based data harmonization tools which can 2x accuracy and provide master data managers with a feedback mechanism.

Data quality while it's one of the earliest consideration it's only considered the very first step in getting insights from data visualisation. There are others to consider like data collection, metadata and others too.

Ensuring data relevance and accuracy in visualizations is a critical task. Personally, I conduct a comprehensive data quality check across the entire database. This involves not only standardization but also a meticulous examination of the ETL (extract, transform, load) process. Verifying the accuracy and timeliness during each stage is crucial. I make it a point to ensure a continuous flow of current data, preventing the inadvertent use of outdated information. This thorough approach guarantees that I have access to a live and up-to-date database, essential for meaningful and accurate visualizations.

Evaluating data quality is a non-negotiable part of the data visualization process. Accurate, complete, and timely data is key to ensuring that your visualizations are both credible and insightful. Techniques such as data profiling, cleansing, validation, and auditing are vital tools in this endeavour. They help to identify and correct errors, ensuring that the data you use paints a true and meaningful picture. Remember, in the world of data analytics, quality trumps quantity every time. Your reputation and the effectiveness of your visualizations depend on the integrity of your data.

A lot of a manual idea can cause data to be inaccurate, for instance having a lot of randomly generated figures in your real data set. Another thing is that if the null contained in a feature is far more than average then its advisable to get another reliable data or drop the column if not really relevant to the report to be presented. Automatic generated data from database is more reliable and of good quality than manually generated figures

Much of our time as a data team is spent developing the infrastructure to enable us to update our visualizations as close to live as possible. While data visualization is often thought of as a primarily front-end endeavor, when designing a data visualization that is meant to layer on top of dynamic data, the back-end infrastructure (e.g. how fresh data gets added to a database, and how your front-end tool accesses that data on the fly) is just as important. Not all data products require these back-end considerations, but if data refresh is a necessary component for the success of the product, designing a robust data-flow architecture is a critical early step.

The more dynamic the data update in the data visualization panel, the greater the engagement of the data consumer audience. However, often some data visualizations address some processes that do not allow data automation at the beginning of creation (and sometimes effort/time also influences...). However, as data visualization is disseminated to the target audience, this drives the evolution of activities involved in improving the data collection and automation process.

In order to revise for a near real-time data updates, it is advisable to schedule or set up an automatic mechanism for data refreshes on your visualization. This would help to keep the data visualization more relevant for the users which is latest and upto date. This schedule can be either done on a regular basis or on a trigger based strategy. And hence your manual errors are avoided and alot of efforts in upkeeping the data, visualization and dashboards is reduced which will aid to easier maintenance.

Not all data needs to be automated. It could be true for operational data (eg statistics in a smart factory), however for other more complex and ambiguous data, it does take an human eye to check and inspect (and possibly, clean) the data before visualising them.

Automating data updates in your visualizations is not just a luxury; it's a necessity in the fast-paced world of data. With data constantly evolving, manual updates are no longer viable. They're time-consuming and prone to errors. By automating this process, you ensure that your visualizations always reflect the latest data, maintaining accuracy and relevance. This not only streamlines your workflow but also reinforces the integrity and reliability of your analyses. In a field driven by precision and up-to-date information, automation is the key to staying ahead.

Beyond just automating data updates, it's crucial to actively monitor these changes and regularly review your data visualizations. Data isn't static; it evolves, impacting the scales, trends, and patterns in your visualizations. Keeping a close eye on these changes and adjusting your visualizations accordingly, like changing the colour schemes or labels, is essential. This isn't just about maintaining accuracy; it's about telling a true and relevant data story. Also, the key is to clearly communicate these updates to your audience. It's not just about showing data; it's about weaving a narrative that your audience can follow and trust, even as the data evolves.

Make sure to maintain your data resources updated by setting up a refresh schedule. The frequency of those updates will depend upon how frequently the data updates. For example, if it updates each day, you would possibly need to update it every day. If it is much less, then a weekly or monthly updates would possibly suffice. To automate this system, we can use scheduled scripts or connecting to live datasource

In my view, this is the most complex task, the most, as well, misunderstood task, and the one that is not taken seriously. Even though you extract information or a query, that doesn't mean that the data is from that specific day. That means that those data points are the ones in that particular moment, but it could be because those data points were loaded in the database, weeks or months ago. So really understanding when the data was captured (not just stored or queried) makes a big difference in interpreting data.

Automated updates can ensure data is up-to-date, but significant changes might be noticed with proper monitoring, leading to misleading visualizations. Monitoring data changes includes understanding the implications of changes on the overall data narrative. It's essential to communicate changes in data and subsequent modifications in the visualization to maintain trust and credibility. Monitoring data changes should also consider the broader context to guide more relevant adjustments. In summary, monitoring data changes is an ongoing process that requires technical vigilance and contextual awareness to ensure data visualizations remain contextually relevant and understandable.

From my viewpoint on keeping tabs on data changes, it's all about creating a dynamic and responsive setup. Take Power BI, for instance—it's like a live wire where real-time insights are key. Features like continuous data refresh and automated accuracy checks make sure our visualizations are not just accurate but also lively. Users actively dive into the data, providing feedback that adds to a collaborative and refined experience.

Engage your subject matter expert or business area expert here! They can tell you if the data is current enough to meet their needs and inform their decisions.

In my experience, collecting feedback on data visualization can be done in several ways: satisfaction survey, which are the details of the panel that users interact with the most and cause the most engagement, establish a channel for answering questions and suggestions, and if possible use metrics tools to check the number of user accesses, length of stay, etc.

In my experience, having consistent and transparent communication with stakeholders along with collecting consistent feedback, allows for a more positive and tailored experience when communicating important metrics. Leveraging aspects from design theory alleviates eye strain and headaches stemming from dense information, I will say sometimes less is more, and being able to gauge your audience will guide you in the right direction.

One way to ensure data used in visualizations is relevant and up to date is to do a "check in" with the end consumers at least once or twice a year. Why? Because data that was needed 6 months ago may not be of value anymore. Checking in with the end consumers can help identify the data that is no longer needed and identify new data that will provide more up to date insights. This feedback loop with end users will help ensure that your data visualizations are always up to date and the data is relevant to the current needs of your consumers.

A melhoria contínua é a pedra angular de qualquer trabalho de análise e constru??o de relatórios/dashboards. O feedback dos usuários é uma fonte valiosa para garantir que a entrega atenda às necessidades. Além disso, é através desse diálogo constante que podemos identificar oportunidades de melhoria, seja por desvios de comunica??o na constru??o ou por insights que surgem com o uso das informa??es geradas. A evolu??o contínua é fundamental para manter nossas análises relevantes e impactantes.

The use of data plays a key role in continuous process improvement, allowing organizations to identify inefficiencies, optimize operations and monitor progress over time. Collecting and analyzing data helps you make informed decisions, evaluate the impact of implemented changes, and identify best practices to drive efficiency, reduce costs, and improve quality, thereby contributing to the sustainable success of business operations.

In this AI era, one has to focus on continuous improving and learning. As the technology evolving day by day one has to be on top of the game to catch up the speed. I can't stress to learn new technologies and certifications (If it's helps).

Yes it’s a continuous process. The requirements from business can change overtime. Accordingly the data visualization gets impacted. The data structure source may undergo some change as well. This requires observation and update for data visualization regularly. If the visualization is not in sync with the source structure, the data fetched would be either wrong or incomplete.

Data visualization will never be perfect, whether it is from data or for the purpose of a use case, because the business environment and user needs are constantly evolving. Increasingly, AI solutions are emerging and they will be useful to compare with manual data visualization (DV), showing trends and patterns human analysts might not be able to detect, due to complexity of big data. Instead of relying only on one side, AI analysis can complement human analysis to form a well-rounded way of understanding and analyzing data. As analysts usually will not be doing DVs of their everyday tasks, there is a need to continually understand and seek feedback from subject matter experts and stakeholders in order to make DVs for for purpose.

It is important to keep updated with the latest trends and new visualization tools launched in the market. This will give us ideas to serve our needs to develop the enticing and intuitive visualization. We strive our best to think through about the KPIs and insights in order to understand the impact in the business decisions, we should present them with informative and rich user-friendly manner. We should also go through the latest sample dashboards developed and published by the community developers to get the creativity and UI/UX ideas which can eventually be leveraged to our dashboard development.

I trick I often use is a bit similar to backtesting with historical data. So I design my viz on a restrained, smaller subperiod of the original one, and then see how it reacts when extra years of data are added. A nice way to see if my visualization would fall apart or hold still at the next update!

Any visualization is meaningful as long as it solves for business problems and depicts actionable KPIs. No matter how many fancy charts are used, key is to keep visualizations simple and focused. Too much information can overwhelm and dilute the impact of the relevant data.

I always ensure this by incorporating a concise and compact card visualization at the top or bottom of each display, showcasing the last updated date, corresponding to the maximum date in the dataset.

When I started my career, I often forgot to include the last update timestamp in my reports. It's such a small element, but it has a lot of meaning. Indicating updated data in a report is crucial for accuracy and relevance. Current data reflects the latest trends. Clear indications of data freshness allow users to make informed decisions, enhancing the report's reliability. Not indicating whether data is updated can lead to incorrect conclusions, highlighting the importance of timely data updates in maintaining the validity and usefulness of reports.

Continuously evaluate and refine our data quality assessment and visualization processes to adapt to changing data sources, evolving data needs and emerging technologies.