1 February 2023
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In the age of digital transformation, Artificial Intelligence (AI) and Machine Learning (ML) solutions are becoming the de facto means for businesses to obtain a competitive edge. A 2020 report by MicroStrategy found that 65% of global enterprises had plans to increase their AI-driven data analytics spending in the next few years. So where does data visualization come in?
To get the most out of their IT investments, companies need strategies to disseminate analytical findings effectively. The data story has to be understood by not just stakeholders and analysts but every human touchpoint in your value chain.
Researchers from MIT found that 90% of the information transmitted to the brain is visual. Therefore, data visualization is crucial in understanding analytics that can be utilized to drive your business forward.
AI and ML are highly effective tools for aiding companies in gathering and processing data. They also help transform simple information visualization modalities (such as charts and graphs) and make them interactive by incorporating real-time information (such as geographical heat maps).
This article will explore how AI and ML improve organisational visual representation capabilities to help companies make savvier business decisions and run operations more efficiently.
How helpful are AI and ML for data visualization?
By 2025, the total amount of data in the world is predicted to be 175 zettabytes (or 175 trillion gigabytes). Therefore, projects involving Big Data and data science have grown in popularity, raising the importance of visualization.
Pie charts, histograms, and graphs are helpful, but more complex visualizations like heat maps and fever charts provide more context on business data. AI and ML tools are vital technologies that make granular data visualization possible.
Here is a quick rundown of some of the ways AI and ML can be deployed to aid or enhance data visualization:
- Transformation: AI can use algorithms to convert data from one modality to another (e.g., transform area sales figures into a geospatial visualization)
- Assessment: Rules-based systems can measure the relative quality of data sets (i.e., create scores or rankings)
- Comparison: AI can identify patterns and trends, noting similarities and differences in data
- Querying: Analysts can set up automated enquiries to find information based on a number of specific needs
- Reasoning: ML and AI tools can develop autonomous reasoning capabilities and come up with hypotheses (i.e., modelling and forecasting)
- Recommendations: AI in Business Intelligence (BI) tools can suggest data and/or visual encoding methods that may be insightful to users and decision-makers
- Mining: AI and ML can reveal insights from data visualizations without impacting the overall presentation of the data set
When used in conjunction with Qlik + Vizlib, AI tools that perform these functions can really elevate the way that your teams approach data visualization and data storytelling.
Benefits of enhanced data visualization technologies
On a fundamental level, data visualization forms part of a business’ output, so presentation matters. In fact, managers in organisations with optimised data visualization tools are 28% more likely to find timely information. Furthermore, the same study found that 74% of companies can use data visualization tools to customise workflows to suit their team’s needs. Companies are also five times more likely to make important business decisions faster than their direct competitors when they use the right data visualization tools.
Consider the following benefits of investing in a powerful data visualization tool, like Vizlib, to elevate your organisation’s approach to analytics and decision-making:
- Boosts company collaboration: Visually displaying data can make information easier to grasp for everyone. And collaborative tools help to foster data literacy and boost adoption across your organisation.
Vizlib Writeback Table and Teamwork tools, for example, allow teams to quickly modify, change, and comment directly in your data visualization dashboards while remaining in the analysis flow.
- Elevate your data storytelling and accelerate the path to decision-making: Effective solutions for data visualization enable users to meaningfully interact with data and use what they learn in order to make or support data-driven decisions. With approachable, intuitive tools that make self-service analytics easy and interesting, tools like Vizlib can take your organisation beyond visualization, into action.
- Future-proof your approach to analytics and decision-making: Devising what-if situations with data visualizations can improve your company’s strategic decision-making abilities. Vizlib Input Form allows you to change numerous Qlik Sense variables to formulate business simulations quickly.
From there, teams can use the findings to identify bottlenecks or inconsistencies to improve their operations and reduce errors.
Limitations of AI and ML within data visualization
- Requires investment and data scientist expertise: In the world of Big Data, it’s crucial to consider that analysis will demand robust computer technology, effective storage systems, and even a shift to the cloud. IT engagement is also necessary for effective data management. In this world, a strictly self-service approach isn’t yet possible.
Practical ways to incorporate AI and ML into your data visualization approach
- Real-time monitoring: ML algorithms are indispensable tools in the fight against fraud. Financial service teams using ML can train their system to detect irregular account activity and alert staff to potential fraud. In turn, this means that finance teams can confidently use tools such as Vizlib Finance to share sensitive financial data safely, knowing that the information they see is legitimate.
Vizlib Writeback Table for Qlik Sense can analyse your account information and take appropriate action to keep track of your business transactions. The Vizlib Writeback Table allows you to monitor activities such as interest rate changes, late payments, and estimated balances. To find out more about its use cases for real-time financial monitoring, take a look at our previous article.
- Generate granular business insights: You can tailor your BI solution to look for deeper relationships in massive data sets by incorporating an ML component into the data discovery process.
Analysts can then use the data to create specialised data visualization graphs and tables to help shareholders and employees better understand your business operations. In e-commerce, these granular data insights may help companies build upselling and cross-selling campaigns based on onsite user behaviours and purchase histories. Take a look at this previous article to see five more things to consider when uncovering deep insights from your data.
- Refine search parameters within data visualization dashboards: modern data visualization tools enable you to set up detailed search parameters and queries easily.
ML tools can also apply algorithms to sort and prioritise search queries to help teams find the answers they need faster and with a high degree of accuracy. As a result, business analytics teams can query and test more scenarios to create better business outcomes.
Take your data visualization techniques to the next level with AI, and ML
Data visualization tells the story of your business, so you need to make sure that it is compelling, accurate, and easy to understand.
Optimise your Qlik Sense with Vizlib and scale your brand to new heights. Equip your business with all the visual storytelling components you need to get your stakeholders on the same page.
If you want to learn more about our dynamic data visualization tools, start a free trial now.
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