In an increasingly connected world, your business will have to deal with all sorts of data. Surveys show that managing unstructured data has emerged as a big challenge for 95% of businesses. Unstructured data is 80% to 90% of overall data generated, while structured and semi-structed data accounts for 20% and 10% of the overall data volume.

This includes not just data on customers and interactions, but also sensor readings, financial reports, and more. Handling all that information can seem overwhelming. Conventionally, businesses would analyze such data with a siloed approach, using different sets of applications and methods for different data types.

That is changing and you need to change such a traditional approach to data analytics. Modern data offers a massive competitive edge, and you cannot afford to delay the analytics transformation.

The Changes Taking Place in Data

The following elements are the driving forces behind shifts in data. Learning about the reasons behind the changes can help you gain a better idea of where the industry is headed:

1. Impact of AI & GenAI on Data

AI is deeply changing the way businesses work with data. It is no longer a buzzword in data analytics. Implementing AI systems requires access to high-quality and structured data.

The AI revolution has initiated a transformative effect across the data world. Machine learning models require a persistent supply of quality data and business decision-makers require AI-enabled insights at their fingertips. All this is no longer possible with batch processing pipelines that used to update once every 24 hours.

2. Data Volume Growth at Exponential Levels

The volume of data generated and collected by businesses is growing at an exponential rate. It is also growing in terms of variety. The type of data your business is dealing with includes:

• Structured data in databases
• Unstructured social media data
• Semi-structured data
• IoT sensor data
• Video streams

And more. It is no longer sustainable or efficient to manage such complex and vast data sets with conventional, centralized approaches.

3. From Batch Processing to Real-Time Processing

It is no longer acceptable to wait overnight for reports. Your business must react to the following factors in real-time:

• Market changes
• Operational challenges or bottlenecks
• Customer behavior

Consider situations such as personalized suggestions in e-commerce or the detection of fraud in banking operations. These scenarios cannot wait for end-of-day batch processing. So, there is a need for real-time data flow, analytics, and decision-making.

4. Data Democratization

The point that everyone in your organization wants access to data to empower their decision-making process is excellent yet challenging. Consider the following scenarios:

• Your marketing department seeks to create its own dashboards
• HR finds tremendous value in having talent management predictive models
• Product managers need their own separate data analysis

The conventional approach of having a small team of data specialists helping all the departments no longer works. Data democratization has created massive challenges for data teams. There is a need to create systems that are powerful yet user-friendly.

All these changes clearly indicate a need for analytics transformation.

How AI is Redefining Data Analytics & Decision Making?

Artificial intelligence (AI) is transforming the way businesses are approaching data analysis. AI-based analytics enables your teams to understand complex data and helps drive better decision-making at different management levels.

AI has evolved at an exponential rate over the last few years. So, the current AI-based data analytics models have come a long way in empowering decision-making. Conventional AI-based data analytics is usually focused on a single data modality at any given time. For example, a natural language processing (NLP) model will only process text. An image recognition system will only analyze visual data. However, in real-world scenarios different formats of information need to be processed and analyzed.

For example, a customer service interaction involving a complaint can involve different data modalities, including:

• Audio as their voice data
• Text as the words they used
• Image of a screenshot they shared

Using the latest AI-based analytics systems means you will no longer have to deal with incomplete insights due to disparate pieces of data. They can process different types or modalities of data at the same time. This includes data types such as:

• Audio
• Text
• Images
• Video
• Time-series data
• Sensor data

And more. Additionally, advanced AI models know the relationships between the different data types or data modalities. This creates a more layered understanding of the situation compared to what is offered by conventional data analysis.

Some of the most important benefits of implementing modern AI systems for data analytics and decision-making are as follows:

1. Better Insights from Unstructured & Unstructured Data

A large volume of information is available in only unstructured format. This includes videos, audio recordings, and images. Conventional techniques have a difficult time processing such data properly.

However, the latest AI analytics models can extract useful information from such data sources. For example, in the field of healthcare, such systems can analyze images from MRI scans and X-rays, text from clinical notes, and audio from patient-doctor interactions to provide a more effective diagnosis. Business leaders cannot simply afford to ignore these data analytics capabilities of AI.

2. Highly Accurate Predictions

Since AI can process different data types, the accuracy of predictive analytics increases significantly. For example, in the manufacturing sector, sensor data from machinery, maintenance logs, and visual inspection can help predict machinery failure with a high level of accuracy.

3. Deeper Contextual Insights

This ability to bring together information from different formats also provides a deeper context. For example, when used for social media monitoring, an AI system can do more than analyze sentiments from textual posts. It can also analyze images, videos, and facial expressions to gain a better understanding of public opinion. Such a level of deep insight can allow your business to make more informed promotional or PR decisions.

4. Guiding Intelligent Automation

The use of AI can help drive advanced intelligent automation. For example, AI-based chatbots are capable of interpreting typed queries, screenshots, and voice messages. Interestingly, they can also evaluate the customer’s voice tone to determine their emotional state. This can help provide more empathetic solutions. The result is improved customer satisfaction and reduced manual interference.

Real-World Applications Across Industries

AI systems have become highly versatile and are being adopted in different industries. A few examples of its real-world applications are as follows:

• Retail: AI-based data analytics can make personalized recommendations to customers and prospects after analyzing their past orders, browsing history, and the product images they checked.
• Manufacturing: Quality control can be maintained by using visual data from cameras and sensor data to find even the smallest product defects. AI analysis can also assist with predictive maintenance by integrating and analyzing sensor data, operator reports, and maintenance logs.
• Customer Service: AI chatbots can also integrate NLP, speech recognition, and image recognition to respond more effectively to customer queries while providing highly relevant answers or solutions.

How Organizations Can Implement AI for Data Analytics?

Over the last few years, businesses and industrial organizations have mostly moved their operations and data to online and cloud environments. This shift has created the setting for scaled-up computing environments where large volumes of complex data are handled.

This has resulted in the implementation of AI for data analytics. Here are the different ways organizations can implement AI:

1. Implementation by Senior Tech Management

The application of AI-based data analytics should be a topic of open discussion across your organization. The AI objectives must align effectively with your organization’s goals, processes, and budgets. When top-level executives are educated in the capabilities of AI-based data analytics, the chances of new initiatives increase significantly.

2. Adopting Deeper Algorithms

The early applications of data analytics have been based on shallow algorithms and supervised learning. As AI develops, a wide range of models, such as semi-supervised learning, are being used. Organizations can identify and implement algorithms, strategies, and approaches beyond the scope of data pattern identification.

3. Implementation of Broader Intelligent & Automation Systems

AI-based analytics can help you go beyond the scope of basic process automation. Your business operations and decision-making processes can be enhanced with AI capabilities for more sophisticated and unbiased organizational decisions.

Such systems can help overcome the challenges that come with human intervention, such as:

• Cognitive bias
• Non-compliance
• Manual errors
• Inaccuracies

5 Steps to AI-Based Data Analytics Implementation

When CXOs speak of AI-based data analytics implementation, it is easy to focus on buzzwords. Interestingly, what actually works is often less trendy but highly impactful. It is recommended to take the following steps to bring analytics transformation in your organization:

1. Upskill in AI-Based Data Analytics

It is important to have your teams gain expertise in the AI subfield, including NLP, audio processing, and computer vision. Promote training programs around multimodal AI tools and get your employees familiar with cutting-edge frameworks for training and developing models.

2. Invest in AI-Ready Infrastructure

You should set up AI-ready infrastructure to accommodate the unique demands. This requires:

• Ensuring access to a large volume of diverse datasets across different formats
• Implement the right platforms to source data for training and testing models
• Leverage cloud platforms such as Google Cloud, Azure, and AWS, as your organization will need scalable computing resources
• Equip your in-house teams with AI tools capable of analyzing and processing multimodal data. Microsoft Azure AI is one such example.

3. Create Multidisciplinary Teams

Effective implementation of multimodal AI requires creating multidisciplinary teams that bring together experts with expertise in areas such as data science, computer vision, audio engineering, and NLP. Collaboration between the teams can lead to the design and deployment of systems that utilize different data formats for more comprehensive and useful outcomes.

Cross-training can further improve the understanding of the interaction between different data types. This can lead to better integration of different technologies.

4. Create Ethical AI Policies

It is essential to create and implement ethical AI policies and practices to detect and mitigate bias. Such algorithms can help in the identification and reduction of biases within data.

The growing application of AI-based systems also raises data privacy concerns. So, it is important to adopt advanced security measures to protect user data. This can include:

• Creating secure storage policies and ensuring data anonymization
• Complying with CCPA, GDPR, and other data privacy regulations
• Creating transparent AI models for accountability and building trust

5. Implementing Industry-Based Applications

AI-based analytics will have different types of implementations across different sectors. You should assess how the technology can be customized based on your industry. For example, in healthcare, AI systems can integrate diagnostic data and patient records into a service, ensuring improved patient treatment.

It is important to balance governance and experimentation because a rigid system can destroy innovation. Teams should be allowed to test AI models and new data solutions within governed innovation environments.

Deeper Adoption of AI Analytics Means More Efficient Enterprises

Your organization can rely on AI, not just for automating processes, but also for effective decision-making and in areas that make a big difference. Today, every business needs:

• Interpreting complex data
• Self-learning AI systems
• Support in business decision-making

AI-based data analytics has ushered in a new era in business decision-making. It is capable of leveraging the strengths of different data types and combining them to create valuable intelligent systems. The technology can transform different industries, improve human and computing interactions, and take business decision-making close to the artificial generative intelligence (AGI) dream.

Every major business decision taken today, from customer experience to new product development, relies on data insights. As the application of AI increases in measure and simplifies in terms of implementation, there is growing pressure on data teams to deliver real-time and reliable analytics insights.

By focusing on challenges and promoting integration, your business can unlock modern data analytics’ potential and ensure a more connected and smarter future.

If you want to know what Infojini Consulting can do to help your organization gain the competitive edge of modern data, it is recommended to book a free consultation today. We can help you make the right decisions and steps to bring about optimized analytics transformation.