Serving the Future of AI and Data Analytics

Serving the Future of AI and Data Analytics

Data Analysis
Database Structures
March 15, 2024
Cecily McPherson
Data analytics is advancing at an unprecedented rate, transforming the way most businesses operate and engage with their data on a daily basis. As business needs continue to evolve, data analytics will steer towards greater automation, utilizing AI to assist in quickly identifying insights hidden in extensive datasets; insights that would take humans a much longer time to extract. AI is thus assisting businesses in data analytics through increased automation so that business owners can avert greater focus to their core business needs.

What is Data Analytics

Firstly, it is important to understand what exactly data analytics are and why they are becoming so increasingly important for every business across the globe. Utilizing data analytics is a process of examining, cleansing, transforming, and modeling data to uncover useful information, patterns, and insights that can be used to support decision-making and drive business outcomes.
Every business big and small has data, but not every business is maximizing the use of their data to drive their success. For example, an art museum has data regarding ticket sales, inventory, staff, social media engagement, and much more. This data is likely spread across numerous platforms and may be quite incomprehensible as individual parts. It can be difficult to see how inventory relates to staffing or how social media engagement could help boost ticket sales. Data analytics thus involves gathering all of these different data insights together to examine how they all correlate to better suit the individual needs of the business.
To do this efficiently, a database-specific query must be written. Crafting a query typically takes about 10-30 minutes to write and it must be written in a specific format that matches your database. Since your database is set up as a set of tables connected in a specific way, your query needs to take the structure of your database into account.
However, since data analytics are constantly changing, and data is often spread out amongst multiple different databases, manually analyzing data can be time-consuming and daunting. It also allows for a lot of human error. No matter the expertise, humans make mistakes, and once these mistakes are made, it can be difficult to detect and fix them.
Understanding the emerging data trends can give your business better insight into the future of data analytics and why utilizing these new trends in your business can help your business rise from doing good to great, maximizing the full potential of your datasets.

Emerging Data Trends

Although predicting the exact future of data analytics can be challenging due to the rapid pace of technological advancement, several trends and directions are likely to shape the future of data analytics. For business owners, understanding the latest data trends and continuing to stay up to date with their evolution, can truly make or break long-term business success.

1. Advanced Analytics and AI/ML

As artificial intelligence (AI) and machine learning (ML) continue to advance, they will play a crucial role in data analytics. Organizations will increasingly rely on AI and ML algorithms to derive insights from large and complex datasets, enabling more accurate predictions, pattern recognition, and decision-making.

2. Real-time Analytics

The demand for real-time analytics will continue to grow as organizations seek immediate insights from data sources such as social media, IoT devices, and sensors. Real-time analytics enables businesses to make quicker decisions and respond promptly to changing market conditions to better manage their finances, time, employers, and resources.

3. Edge Analytics

With the proliferation of IoT devices generating vast amounts of data at the edge of networks, edge analytics will become more prevalent. Edge analytics involves processing data closer to the source, reducing latency and bandwidth requirements. This approach is particularly valuable in applications where real-time insights are critical, such as industrial IoT, healthcare, and autonomous vehicles.

4. Data Democratization

There will be a shift towards democratizing data analytics, making it accessible to a broader range of users within organizations. Self-service analytics tools and platforms will empower business users with the ability to explore and analyze data independently, without requiring extensive technical expertise.

5. Augmented Analytics

Augmented analytics combines AI and ML capabilities with human intuition to enhance the analytics process. These tools automate data preparation, insight generation, and interpretation, enabling users to uncover meaningful patterns and trends more efficiently.

6. Privacy and Ethics

With increasing concerns about data privacy and ethical use of data, organizations will need to prioritize ethical data practices and compliance with regulations such as GDPR and CCPA. Technologies like differential privacy and federated learning will become more important for preserving individual privacy while still extracting valuable insights from data.

7. Hybrid and Multi-Cloud Analytics

As organizations adopt hybrid and multi-cloud environments, analytics solutions will need to be flexible and interoperable across different cloud platforms and on-premises infrastructure. This will require seamless integration and data portability between various cloud environments.

8. Explainable AI

As AI and ML algorithms are used more extensively in data analytics, there will be a growing need for explainable AI, which provides transparency into how these algorithms make decisions. Explainable AI is crucial for building trust and understanding the reasoning behind automated insights and recommendations.

How Pearstop Fits Into the Future of Data Analytics

Before turning to AI-assisted data analysis and reaping the benefits it may provide for your business, your data needs to be in one place. That is where Pearstop enters the picture.
You can only use AI if you have a working data structure. It’s not possible to detect patterns without data that can be read by the AI itself. Therefore, conglomerating all your data into one location is necessary before you can use AI.
Pearstop’s unique ETL (Extract, Transform, Load) solution makes sure you have clean, high-quality data in one easily accessible dashboard, crafted to fit your unique business needs. Working with us entails working with an experienced, professional team without a long-term commitment. Pearstop ensures that your data belongs to you and that it is accessible when you need it. After the creation of your dashboard, there is no need to continue working with Pearstop professionals. Your data is simply yours to do with what you wish. Ultimately, we’re enabling businesses to get on the machine learning and AI bandwagon so that they can maximize the use of their data for long-term future use.

Concluding Note

The future of data analytics will be characterized by greater automation, real-time capabilities, democratization, and a focus on privacy and ethics. Organizations that embrace these trends and invest in advanced analytics capabilities will be better positioned to derive actionable insights and gain a competitive edge in their respective industries.

Get to Know the Founders

Seb is our technical expert. After successfully running an AI-startup for almost 2 years he saw the use of data in businesses as limited by the accessibility of said data. He leads the programming and data analytics teams and is the founder of our in-house ETL solution.
Stephanie comes in with knowledge of metrics, programme management and project experience in an extensive range of companies. Through strategy consulting she’s spoken to managers in all lines of business, learning what is relevant for them to steer their business.

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