What is a Dashboard?
In data analytics, a dashboard is a tool designed for information management. It provides a way to track, analyze, and visually display points of data, metrics, and KPIs (key performance indicators). Dashboards are commonly used to keep track of how well a department or business is doing.
Dashboards can connect to files such as Google Sheets or Excel spreadsheets, as well as APIs, services, and other attachments. Once the connection is established, raw data is translated into a readable form. Dashboards provide users with a means to evaluate data in charts or tables rather than having to search through spreadsheet rows and columns. Because they bring data together from a variety of data sources, dashboards provide a time-saving method for reviewing data, since it is all in one place.
Within a company or organization, dashboards are typically used by various decision-makers to review productivity. Stakeholders like directors, department and project managers, CEOs, and other workers each work with dashboards to gain insights into their own work or how a project is coming together. Within the marketing sector, in particular, employees across departments typically work with dashboards to keep track of campaigns and review how their initiatives are performing.
Despite how common the use of the dashboard has been in the past years to the field of data analytics, there is a current movement away from the traditional dashboard. This article will explore some of the reasons fueling this trend of leaving dashboards behind, as well as the technologies businesses are implementing in place of dashboards.
Is the Decline of the Dashboard Inevitable in Data Analytics?
Although the dashboard has helped Data Analysts handle large sets of data for decades, this business intelligence and modern analytics platform is no longer serving the needs of its users. In 2020, Gartner predicted the decline of the dashboard in data analytics, a trend that has continued into 2021 and is expected to gain momentum.
More and more often, dashboard use is being phased out, and in its place, the data-driven approach is being used instead. But why is this happening?
Why is the Decline in Dashboard Use Happening?
Some experts believe the decline of the dashboard is inevitable in the coming years, as data management, analysis, and visualization needs at companies evolve and expand. The following are several reasons for this decline:
- Dashboards alone are not able to monitor every crucial metric or aspect of the dynamic modern-day business world. While manual analysis can be used, this is tedious and can compromise efficiency and the reliability of the results.
- KPI dashboards are not always able to offer real-time, actionable insights. In addition, these standard business KPI dashboards have drawbacks with usability, since Data Analysts must manually process data and don’t have the ability to quickly hone in on data at the metric level.
- Although dashboards are helpful to most businesses at an operational level in areas where data moves quickly and must be evaluated regularly, if the user needs to understand details behind these numbers, dashboards are less effective.
- The insights dashboards provide to some users are not contextualized or actionable. The majority of business KPI dashboards can’t handle large volumes of data or complex data.
What Tools are Data Analysts Using Instead of Dashboards?
Some businesses and companies are exploring other options beyond traditional dashboards to better meet their professional needs. Here are just a few of the tools and technologies that are beginning to enhance or replace dashboards:
- Some users opt to add extensions to their current dashboard-centered user experience.
- Conversational interfaces are growing in popularity. More people than ever are conversing with their technology, such as phones and cars. Computers are no exception. It’s now possible to converse with data just like we would with friends or family.
- In place of the more traditional point-and-click authoring and exploration of past visual displays, many businesses are instead relying on dynamic data stories that incorporate automated components.
- The prevalence of in-context data stories rather than predefined dashboards is increasing. These stories provide each user with useful insights that are streamed to them depending on their use or context. Tools like natural language processing, streaming anomaly detection, and augmented analytics help provide these tailored insights.
- Search-driven data exploration options are growing increasingly popular, like the search approach Google offers.
- Auto-generated data stories have emerged, such as those that follow business data and identify important changes for every user.
- The question-and-answer interaction pattern provides users with another alternative to the predefined dashboard experience.
- Machine learning and artificial intelligence (AI) provide insights into predictive analytics, the branch of data analytics that deals with what may occur next for a business or a company. These tools also benefit the field of prescriptive analytics, which is concerned with the decision a user should make pertaining to the information at hand. Basic dashboards that present information in charts and graphs do not offer users the same advantages that machine learning and AI can.
It’s important for those working with data analytics to regularly devote time to evaluate the business intelligence and analytics tools in order to decide if they are still serving their organization’s needs. If the traditional predefined dashboard setup is no longer providing the best analytics and visualization options, then it may be time to consider augmented or natural language processing-driven options.
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