Data Analytics, AI, and Automation: Transforming the Future
In today’s interconnected world, data is a powerful asset for growth. Data can help businesses make profitable decisions to improve multiple functions such as sales, marketing, finance, and revenue. Artificial intelligence is empowering businesses, both big and small, to harness more value from their data and get ahead of competitors. However, accelerated use of data is not possible with just manual efforts.
We are generating more data today than ever before. Internet users today produce about 2.5 quintillion bytes of data every day. Given the large volume of data being generated every day, automation is critical. Artificial intelligence is transforming data analytics as we know it and unlocking new growth opportunities. In combination, AI, data analytics, and automation is enabling enterprises across the globe to achieve unmatched speed, efficiency, and results.
What is data analytics automation?
Data analytics automation involves the automation of the entire data analytics life cycle by using AI/ML-based computer techniques. By enabling users to autonomously monitor and analyze large data sets, data analytics automation allows for fast insight discovery and decision making.
Artificial intelligence can help enterprises automate, simplify, and speed up the data preparation, and insight generation process. AI/ML algorithms can automatically analyze large volumes of streaming data, quickly identify patterns, and generate insights for meaningful action.
With data analytics automation, businesses can quickly turn raw data into reliable insights and drive business transformation projects. AI-enabled data analytics automation offers several high-value use cases from customer engagement, predictive analytics, to product optimization. The potential contribution to the global economy from AI is estimated at around $15.7 trillion by 2030.
Why do you need to automate data analytics?
AI-enabled analytics speeds up the process of data preparation, automates insight and report generation, and empowers everyone in the organization to make data-driven decisions. Analytics automation provides enterprises numerous benefits and also makes it easy to share the findings across the organization. Here are some of the key business benefits of automated analytics:
1) Faster insights for profitable decisions
In a competitive market, speed is vital. To successfully launch new services or improve existing products, real-time data insights are essential. Making sense of data metrics and variables from multiple sources is a challenging task. By automating the entire data value chain, users can get real-time insights from raw data to take meaningful, profitable actions. It can help you to successfully upgrade products or manage marketing campaigns.
2) Improve productivity
Automation saves considerable amounts of time and effort in managing the data life cycle process right from data preparation to visualization, allowing data science teams to focus on core business areas and key problems. It removes the complexities of monitoring rapidly changing variables and makes it easy for users to make sense of their data, detect minute anomalies, find hidden patterns, and discover complex insights that are not found through traditional manual approaches.
3) Reduce costs
By saving employee time in data preparation, modeling, and analysis, data analytics automation contributes to overall savings for the enterprise. With SaaS-based AI- platforms, enterprises can quickly scale their AI and data analytics efforts without a large investment in building and maintaining in-house AI capabilities.
By leveraging artificial intelligence, enterprises can automate the entire data life cycle value chain from data ingestion, data preparation, data validation, data analysis, model building to reporting.
No-code AI: The future of AI-enabled analytics
One of the major roadblocks to implementing enterprise AI and data analytics is the limited access to data science and coding skills in the organization. While businesses understand its value, AI implementation becomes a hurdle due to skill shortage.
The emergence of new-age AI-driven analytics solutions is making it easier for enterprises to implement data analytics and automation to get greater business value. New-age flexible, modular, SaaS solutions are allowing businesses to automate the whole data science life cycle including data collection, data preparation, AI modeling, data visualization, and automation of workflows.
In addition to the reduced cost of AI implementation, No-code AI platforms also offer the benefit of speed in turning raw data into insights. Enterprises can run experiments faster and reduce time-to-market.
Subex’s HyperSense is one such no-code AI platform that allows business users without coding skills to easily aggregate data from disparate sources, gain insights by building, interpreting, and tuning AI models. With HyperSense, enterprises can augment the ROI from data analytics, drive automation, and increase efficiency across the entire data value chain.
With a myriad of applications and benefits, AI-enabled data analytics and automation are transforming the future of business as we know it. No-code AI solutions can greatly accelerate the adoption and democratization of Artificial Intelligence (AI) and data analytics in enterprises. As the markets get more competitive, businesses that leverage No-Code AI, stand to benefit in multiple ways and get better results faster.
Want to learn more about No-code AI and HyperSense? Email us at firstname.lastname@example.org or visit our website, www. hypersense.subex.com
Make Better Decisions With Quantifiable Data-driven Evidence
Arundeep is a Director in Subex’s Business & Solutions Consulting Group and works with CSPs across the globe on strategies and solutions to leverage the power of Data, Analytics and AI to generate business value. As part of his current charter, he creates possibilities for enterprises to truly democratise the use of analytics and AI by capitalizing on the cutting-edge capabilities of Subex HyperSense.