Dashboards are a powerful and unique way to present data-driven insights using data visualization techniques that display relevant and actionable data along with tracking statistics and KPIs (key performance indicators). Dashboards must ideally present this data in a quick, easy-to-navigate format with only the most relevant information understandable at a glance.
Leveraging Data Analytics For Business Insights
Today, businesses can leverage all the data they need, including internal and third-party resources. From an organizational and management perspective, this would require developing a data strategy, assigning data owners and an analytics team, taking targeted security precautions, and developing a data-driven initiative – a business case that would explain why certain data insights are needed and how to use them.
From a technology perspective, to leverage the capabilities of all available data, companies would need to overcome certain barriers and discover the tools that would solve the challenges associated with data – volume, source fragmentation, expensive operations, and quality control. In order to leverage the entire potential of data analytics to gain actionable business insights, companies must –
- be capable of consistently collecting, processing, analyzing and deriving insights from large volumes of raw data;
- use existing visualization tools or require the development of a data visualization platform to display information;
- ensure high quality of output data and ensure reliable quality control;
- optimize costs and effort so that the benefits of the data-driven initiative fully cover and exceed the investment;
- be flexible and elastic to modify, scale up or down on the go and meet changing business needs.
Most companies, when embarking on a formal decision-making process, turn to tools and techniques to help leaders organize their thoughts and make the best decision for their company. Some ways in which businesses can take advantage of business intelligence tools include –
- To Derive Actionable Insights From Data
When analyzing data associated with a decision, it can be difficult to weigh all the varied factors and their impact on the outcome of a decision. A decision support tool will help put things into perspective and guide decision makers to act on the organization’s most important factors.
- To Facilitate Easier Brainstorming & Creative Thinking
When tasked with using a decision support tool, team members engaged in the process tend to push their creative limits to develop different possible outcomes to think about. Decision support tools inspire more creativity, guiding users to think outside the box rather than weighing only the options that come to mind immediately.
- To Organize & Prioritize Objectives
Decisions tend to involve multiple objectives. For instance, a company may need a project to be profitable while complying with laws and regulations. Decision support tools can assign importance to competing objectives in a decision, helping organizations choose a solution that aligns with their business priorities.
- To Thwart Preconceived Notions From Adversely Impact Decision-Making
Everyone has biases that can lead to errors in the decision-making process. Decision tools remove much of the individual bias and emotion from the process. For instance, a product manager might want to launch a new product created by his/her department without clearly thinking about production costs or customer demand. A decision support tool would introduce these factors into its framework.
- To Prevent Misconceptions From Taking Hold
A formal decision-making process can prevent a business from being driven by mistakes, often resulting from “gut feelings” or an abject lack of planning. In the discipline of behavioral decision theory, which explores the separation of objectively rational decision-making and intuitive (often irrational) decision-making, these blunders fall into the second category.
AI is useful for businesses because it has the unique ability to constantly self-learn – the more data-driven decisions that it makes, the more it learns. AI trains and uses collections of data to create models that get really, really good at making predictions and categorizations on that data. These same models can then be made use of on real-time live data to make real-time predictions, categorizations, and recommendations, enabling businesses to make good business decisions.
For example, using customer transaction data drawn from thousands and thousands of purchases, a business can learn which products certain customer segments buy together. This model is then made use of to recommend complementary products on a website. If that sounds familiar, it’s because other companies like Amazon are adopting the same strategy to offer better product recommendations to their customers to increase purchases. It’s remarkably different from how businesses operated until even a decade or two ago. Because thus far, there was a central point where every significant decision was made – a human being.
Instead of depending on machines, humans analyzed data to determine everything from –
- which customers to target,
- which overly risky marketing campaigns to attempt,
- decide whether the anticipated cost of a new product launch was worth it.
The problem with leaving every decision to a human is that making mistakes is part of being human. Our emotions seep in, we get stressed out, and our cognitive biases tend to guide our decisions.
Finding out the latest industry trends using data visualization helps the organization deliver quality services and products. Additionally, trends allow business leaders to spot problems before they happen. And leaders make way for higher business profits by staying on top of these trends.
Visual analysis tools and interactive visualizations help to better understand the data. Until a decade ago, there were only a few data sources available, but now there are many data sources available. Only people from the IT department could evaluate the data before. And non-IT professionals had to wait to get their visual reports. Now things have changed a lot with visual analysis on the scene.
Some of the key benefits that visual reporting affords businesses are –
- Facilitating better data exploration improves data analysis and minimizes overall business costs.
- Simplification of complex information, leading to better business decisions.
- The capabilities needed to solve large and complex problems and produce accurate results are extended.
- Different visualization trends and different ways of presenting data are on offer.
- Visual reporting includes data visualization methods such as interactive bar charts, network charts, and 3D scatter plots, among others.
Businesses and large corporations have tons of data and information to process. It is difficult to collect, evaluate, process and visualize data manually. Thus, data visualization and visual analysis tools are used to enable data-driven decision-making and achieve organizational goals and objectives.
Predictive analytics isn’t a crystal ball, but its benefits are transforming nearly every industry. Companies with a more accurate picture of the future are exponentially better off than those that plan based on historical performance. These companies can now make decisions informed by lots of granular details instead of vague guesses based on outdated information.
At best, predictive analytics infuses intelligence directly into workflows, automatically guiding users to take the right action at the right time to build the future they want. Leaders can also use predictive analytics to guide the broader course of a company’s decisions and direction. Organizations have full-fledged begun taking advantage of predictive analytics. While some companies will continue to collect and synthesize data they will nevertheless remain focused on the past bu other forward-looking companies will use data to predict patterns days, months, or even years ahead.