Data is the true differentiator for market-leading companies. Almost 95% of businesses rely on data to identify business opportunities. As per MicroStrategy, 57% of organizations use data and analytics to drive strategy.
Even during the uncertain times of the pandemic BI and analytics tools helped businesses to stay operational and address unforeseen challenges. The adoption and growth of BI have been steady over the past few years. The Global Business Intelligence market expected to grow at a CAGR of 8.6% during 2021-28, a report from Zion Market Research says.
Challenges with growing data volumes
Data is exploding in every industry. More businesses are trying to deep dive into every data point to bring efficiencies to the internal processes, increase revenue streams, and deep dive into customer behaviour. Successful businesses use data to understand more about their product and services to drive the new transformation wave.
BI tools like Tableau and Power BI have evolved rapidly to address the growing analytics, and AI needs in data visualization in recent years. Modern data visualization tools are here to address the needs of growing data volumes, preparing them, and deriving some meaningful insights. But businesses encounter a few challenges in the process of deriving insights.
Data cleansing is the first challenge, with lots of big data coming from many streams stored in various places. As per Experian, bad data affects almost 88% of the US businesses and results in a 12% revenue loss. On the other hand, the C-suite executives lack trust in their data. 77% of business users feel the same. Also, this results in irrelevant IT spending.
The reasons for this bad data are manifold. Businesses should prioritize data cleansing and preparation as their first step to data visualization. Only then can decision-makers rely on the data assets for insights and back them with growing data volumes.
Extracted insights are not meaningful unless there is a context to them. Understanding the dependencies between the variables and identifying the patterns in the data seems to be another challenge. Businesses can drive their strategy only if they recognize the correlations to solve business challenges, drive revenues and optimize operations.
The charts and graphs from the data visualization tool may not have value and drown in the reports pool without identifying the context and dependencies.
Data management strategy
Business insights are compelling only if you have a data strategy. Data strategy is about analyzing current data trends, prioritizing the data assets within the business, identifying the data sources, and selecting the right tools and technologies for data processing, machine learning, and insights.
Without the right data management strategy, all the efforts to generate insights may go in vain and unnoticed. The key is to process the data, understand the context, and have a data strategy according to business goals to generate ROI from data visualization investments.
The Impact of Data Visualization
Quality data assets for valuable insights
The quick outcome of any visualization is to understand the meaning of the underlying data. The widely used charts, bar graphs, and line charts help people understand the information faster and easier than paper-based reports.
Visualization offers this decisive advantage to anyone in the organization. Irrespective of their skillset and technical understanding, all the users can quickly grasp the patterns and meaning of the data points.
Data visualization, in other words, is the initial quality filter for the data streams. Before presenting it in reports, the pre-processing and creation of a unified data pool offers quality data assets for valuable insights.
Every one of us knows the history of bar graphs and pie charts. They are more than 100 years old. The BI tools like Tableau, Power BI, MicroStrategy, and Cognos have evolved recently.
Are the users satisfied with the old charts and graphs? The answer may be no for everyone. The BI tools, too, evolved rapidly in the recent past.
The new options in BI
The utilization of AR and VR everywhere in businesses introduced immersive visualization for many industries. 3D visualizations are now standard for many reports. The experience of a dashboard has altered over the recent past. Users now prefer a more intuitive experience in data visualization than ever before.
Apart from these changes, AI is now creating new avenues in BI. AI now powers data storytelling. You may no longer need a data scientist or a visualization expert to pull out the reports you need. It also provides the hidden messages behind the change in data patterns.
The self-service option in BI tools has brought more ease for users to fast-track the entire data visualization process.