There are many reasons why people search for better business intelligence solutions. We see analysis paralysis problems, information overload problems, operational inefficiencies. People are still managing a lot of data processes with Excel and manual data manipulation, and there is a still big problem of alignment.
So there's a lot of spending going on to try to solve those problems, and I would ask myself, are you experiencing the same type of problems? Your clients or you also spend that amount of, of course, not by yourself, but you also spend a lot of money every year on your BI, and you ask has BI lived up to the promise so far for your organization at least.
So here's a great quote by Steven Few, who is a well-known author and respected thought leader in the United States, specifically in the visualization space, and what he says is the following. He says that the methods and technologies that are supposed to support the analysis and reporting, what we know as BI, this intelligence, they often fail to deliver on the promise of intelligence, and that is because often the data is not well understood.
Three Ways We Typically Consume Data
The fact is that the intelligence does reside with humans, not necessarily the technologies, and as humans, there are basically three ways we typically consume data. If you look at most of the organizations that we know today, there are basically three different categories that they have. It is either reports, ad hoc analysis, or dashboards.
They are other forms of consuming data such as infographics, but I want to focus on the ones that are commonly used in businesses, are repeatedly used to consume information. So you could take data and present it choosing Excel, or PowerPoint, or some kind of a specialized reporting software. You are creating a report.
Reports are very useful because they are very simple. They can span multiple pages, they can show you data that is very high level or very-very detailed, and they allow to randomly scan through columns and rows to find what you are looking for. Now, if you look at reports in comparison to dashboards, reports are very multipurpose, dashboards are usually not.
Reports can span across many pages, and dashboards typically fit on a single screen. Ad hoc analysis, that's a great way to further explore data and answer new questions that have come up that were not previously addressed with your dashboards and reports.
These days, tools like InetSoft's offer these kinds of analysis using visualization. So visual data discovery can be indeed very useful, but it does require some level of analytical skills to truly leverage the power of this way of consuming data. So not everyone in the business can really use it effectively, and then of course, there are dashboards, which are what we want to focus on today.
Analytical Skills Required for Visual Data Discovery Tools
Visual data discovery tools have transformed how businesses analyze and interpret data. These tools allow users to explore, visualize, and uncover insights without needing advanced technical skills. However, a certain level of analytical proficiency is still required to effectively use them. Analytical skills encompass the ability to interpret data, identify patterns, and draw meaningful conclusions—all critical in maximizing the potential of visual data discovery tools.
Basic Analytical Skills for Entry-Level Users
For casual or entry-level users, foundational analytical skills are sufficient to engage with visual data discovery tools. These users should understand basic data concepts such as averages, percentages, and trends. Familiarity with business key performance indicators (KPIs) is also valuable, as it helps in aligning data insights with business objectives. For example, understanding how to interpret a bar chart showing monthly sales performance is a fundamental requirement.
Intermediate Skills for Power Users
More advanced users, often referred to as "power users," need intermediate analytical skills to unlock deeper insights. These skills include the ability to apply filters, segment data, and recognize correlations or anomalies. Power users often work with multiple datasets and need to understand how to join, blend, or compare them within the tool. They also require critical thinking to evaluate the reliability of the data and the validity of the visualizations they create.
Advanced Analytical Skills for Data Analysts
Professional data analysts and other advanced users of visual data discovery tools require a higher level of analytical expertise. These users often design complex dashboards, derive predictive insights, and perform advanced calculations or statistical analyses. They need a strong grasp of data modeling, transformation, and visualization best practices to ensure their findings are accurate and actionable. They may also use these tools in conjunction with programming languages like Python or R for custom analyses.
Soft Skills and Domain Knowledge
Beyond technical analytical skills, soft skills and domain knowledge play a crucial role in using visual data discovery tools effectively. Understanding the context of the data is essential for interpreting results accurately. For instance, a user in the healthcare industry must be familiar with clinical metrics and regulatory requirements, while a retail analyst should understand customer behavior and supply chain dynamics. Communication skills are equally important, as users must present their findings in a way that stakeholders can easily understand and act upon.
A Scalable Learning Curve
The level of analytical skills required to use visual data discovery tools varies depending on the complexity of the task and the role of the user. These tools are designed to democratize data access, making it possible for users with varying skill levels to derive value. While basic skills suffice for simple visualizations, more sophisticated tasks demand deeper analytical expertise. Organizations can maximize the impact of these tools by providing tailored training to match users' skill levels, ensuring everyone can contribute to data-driven decision-making.