Empowering with insights: Democratizing data with Generative AI

We keep reading everywhere online that ‘data is the new gold’. It resonates with most businesses, as numbers do not lie. Businesses across the globe are transitioning from gut-feeling decisions to data-driven decision-making. Recent studies show that enterprises that harness data to drive their decisions are 58% more likely to surpass their revenue targets than those that don’t. 

However, even with the vast amount of data available, 41% of business leaders find it challenging to harness data as they find it too complex or difficult to access. Data can only be valuable when we can derive meaningful insights from it and use that to make business decisions. The advent of generative AI is set to change this scenario.  

By integrating generative AI in data analytics, users can accelerate the time to gain insights and explore their data within seconds. Generative AI can be an impressive data democratization enabler for enterprises. Let us deep dive into the blog and find out how.

What is data democratization? 

Data democratization makes data accessible to everyone in an organization, regardless of technical expertise. Authorized users get universal access and can leverage the data for business purposes. Instead of waiting for the specific data team to access, drive insights, and deliver the information, individual business teams can directly access the data, get insights, and make timely data-driven decisions. In fact, 42% of CDOs in a survey convey that they prioritize investment in capabilities that allow efficient data sharing, democratization, and usage.

Data democratization eliminates bottlenecks and siloed processes. It allows shared data ownership and responsibility across the organization. Being is a revolutionary approach; it encourages innovation, enhances competitiveness, and drives a data-driven agile culture.

How can you use Generative AI to manage and democratize data?

Automation to handle growing data challenges

As the volume and types of data continue to grow, managing data is becoming increasingly difficult. Enterprises need large amounts of data to train LLMs and secure the data pipelines. Manually managing the data at scale will not suffice. Organizations can address this growing data challenge with AI-powered automation. It involves using AI copilots to automate tasks such as data classification, generating and applying data quality rules, developing data pipelines, and overseeing master data through data matching.

Natural language and user-friendly interface

Generative AI can accelerate data democratization and supercharge productivity as it has a natural language interface for data management. Moreover, with a user-friendly interface for all data users to interact with the data, generative AI can massively improve data management and democratization.

Synthetic data

Enterprises can harness Generative AI to create synthetic data for machine learning models. Real data can be inaccessible due to privacy, sensitivity, or compliance requirements. In sectors such as healthcare and finance, data insights are highly valuable, however data privacy is stringent. In such cases, enterprises can leverage the power of synthetic data to make data accessible and harness insights. Gen AI can create synthetic data that mimics the authentic data statistically, mirroring the actual data set’s patterns, distributions, and interconnections.

Build applications for non-technical users 

With generative AI, enterprises can quickly build apps that enable non-technical users to easily interact with data meaningfully. For instance, generative AI-powered chatbots can answer questions about data and clarify doubts meaningfully, even to users unfamiliar with technical terms. Again, enterprises can build an application using generative AI that allows users to query data in simple language and receive visualizations like charts, graphs, and more. The possibilities are endless with leveraging Gen AI to bridge the technical gap of users.

Translating data

Generative AI can also translate data into various languages, formats, and designs as required by the teams. This makes the data reachable and accessible to users of different cultures and backgrounds. For global organizations, such a feature can enhance the data sharing capabilities with the data available to the employees and customers from all over the world in a language and format they can easily understand.

Integrating Data visualization tools with Gen AI

Integrating generative AI with data visualization tools can significantly democratize data. Data visualization speeds up data-driven decision-making by doing away with data complexity. Applications such as Power BI rapidly process data and simplify that in visual insights that are shareable, accessible, and give a clear understanding instantaneously. Leveraging the insights, business leaders can make better, swifter, and more accurate data-driven decisions that benefit the business. 

Incorporating generative AI in data visualization tools such as Copilot in Microsoft Power BI can simplify the process. You can describe in natural language and get insights. Similarly, you can leverage copilot in Microsoft Fabric to get real-time analytics.

Identifying and mitigating bias

Generative AI can also identify and address biases within datasets. You can train the gen AI tool to detect biased language in text data or find patterns in image data. 

The year 2022 ended with the big bang of generative AI. It has started a chain of reactions and disruptive possibilities. Among the myriad applications and use cases, one major feat of generative AI is the democratization of data, AI, and information. Not requiring hard technical skills and becoming easily accessible, copilots are one of the most disruptive innovations of this decade.

Challenges of democratizing data

Among the many benefits of data democratization with generative AI, specific challenges need addressing.

• Ensuring the usage of fair and ethical data: Since miscreants can use generative AI to craft disinformation and create deepfakes, it is essential to have proper safeguards to prevent generative AI misuse.

• Promoting data literacy skills: Since we are talking about data democratization with generative AI, having the requisite data literacy skills is vital. However, in an enterprise, many may need to gain the necessary data skills to use data effectively. Many organizations are investing in upskilling their employees with self-service data analytics tools and empowering them to become citizen data scientists.

• Privacy and security issues: Since Gen AI models can craft hyper-realistic synthetic data, miscreants can also use it to compromise sensitive and critical information. Thus, it is vital to have robust security and privacy guardrails in place to protect against these risks.

Summing up

One key emerging data trend is data democratization, and generative AI is a key enabler. Organizations are also keen on democratizing data to leverage the numerous benefits that it can offer. From understanding their customers better, enhanced decision-making, driving a data-driven culture, and embracing innovation, the democratization of data will only open the gateways to success. We also discussed the challenges that organizations should address before implementing data democratization.  Saxon AI is a pioneer in data, analytics, and AI. Our latest offering, Generative AI COE, provides a comprehensive package for all generative AI implementations depending on your business use case. So, if you want to implement generative AI in your enterprise and seek a technology partner, we are here to help you. Book a consultation, and our experts will get in touch with you. 

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