Using AI for Data Management: Practical Steps for Beginners
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Data management is to streamline or organise information that you have in your files, documents, or databases. Unlike a few years ago, the modern digital era has gifted AI to navigate all challenges involved in manual data management practices. And its adoption has become a necessity because of data complexity and inflow from various sources. This latest technology has equipped a non-technical person with cutting-edge technology, which follows AI prompts to dance to his tunes like a puppet. Simply put, it enables you to manage, organise, process, and interpret data with great efficiency and intelligence. If you are a beginner and want to learn how AI for data handling works, here is an overview.
Why AI in Data Management Matters?
Thinking why AI is required if you have skills? Well, you might be an expert, but it’s also true that errors are inevitable. How is it if you invest exponential time in inputting, cleaning, verifying, and validating data for proper handling, but cannot leverage it because the data becomes obsolete till then?
Certainly, it’s a nightmare for those who deal with large-scale data almost every day. They cannot always be all the time energetic, focused, and fast. It means that the scope for errors remains there. Here, one small change can make a big difference. Introducing AI for data management that reduces the time to process and optimise it. Simply put, automating the process of error-discovery, fixing, and then organizing datasets in a comprehensive manner can reduce workload.
IDC has researched and found that the global datasphere is anticipated to increase to 175 zettabytes by 2025 (source). The enormous size of data makes it necessary to automate data handling and processing, which AI is capable of.
What Is AI in Data Management?
AI in data management typically refers to deploying machine learning (ML) and natural language processing (NLP) to automate and streamline the data pipeline, from collecting it to storing, organising, and analysing. These technologies can themselves learn from data patterns while detecting anomalies and inconsistencies. And Robotic Process Automation (RPA) can also be leveraged to repeatedly carry out the same procedures.
Also, this smart technology can swiftly foresee trends and learn from insights, which is not as convenient and fast manually.
Practical Steps to Get Started with AI for Data Management
Here are some significant steps to remember while working with AI for data handling.
1. Assess Your Current Data Environment
Before integrating AI, meticulously evaluate the current practices of data management. It will reveal some vital details like where your data is scattered, whether it is clean or structured, and whether it is offbeat.
Puzzling, how to observe these things? Here are some clues:
Discover where you keep your data, whether on the cloud, servers, spreadsheets or CRMs.
Thoroughly examine the quality of your data
Determine the hierarchy that has access to your sensitive databases and to what extent they can manipulate them.
These points will outline how your data ecosystem is set up and what data handling practices work effectively.
2. Define Your Data Goals
Every business has its unique goal. Considering it from the perspective of data, it can be related to data entry, accuracy, data security, and analytics.
So first, decide these things:
- What are the key bottlenecks or main problems that AI can resolve?
- How to improve, like efficiency, accuracy, or predictive capabilities.
- Which specific needs can AI fulfil, such as AI-powered data cleansing, categorisation, or real-time reporting, etc?
Once you have decided, you can better align AI tools to your needs and avoid wasting time or resources.
3. Choose Beginner-Friendly AI Tools
Gaining expertise in AI is no longer necessary. Multiple beginner-friendly platforms are available, offering AI-powered data management features without requiring coding skills.
A few beginner-friendly AI tools are here for your use:
Microsoft Power effective data management for BI, which allows analysis in a visual format.
Google Cloud AutoML can be preferred for using its ML models that require minimal expertise.
Zoho DataPrep also helps in cleansing and transforming data.
Talend Data Fabric allows data integration and quality with machine learning.
OpenRefine is a tool that allows cleansing data using simple AI logic.
You may test and try a small sample of wrangling data and automating its flow.
4. Clean and Structure Your Data
AI shows incredible results with high-quality and structured data. Offbeat, inconsistent, and incomplete data will certainly impact the results AI delivers. For preparing data to mine or discover intelligence, you may use tools to
Filter and remove duplicates
Fill in missing values or enrich data
Normalize formats (e.g., date formats, capitalization) by completing short forms
Eliminate noise or irrelevant fields by validating or verifying data.
These steps will make data more effective to analyse and integrate for machine learning. This method can also guide ML in the desirable way to clean, process, and analyse data via tested algorithms.
5. Automate Repetitive Tasks
Robotic Process Automation (RPA) has evolved to simplify repeated tasks like data entry. In other words, it can automate data management.
Data entry: OCR technology has AI algorithms that detect data from documents or forms to extract and digitize data entry.
Data tagging: AI can smartly categorize and label data so it can be retrieved effortlessly.
Validation: You can verify data and red-flag inconsistencies in real-time.
6. Integrate with Existing Systems
The benefits of AI tools can be leveraged if they integrate smoothly with your current databases, CRMs, or data platforms. There are many tools available offering APIs to connect with platforms like Salesforce, HubSpot, or AWS. If the integration is proper, data silos won’t be there. And data can be managed from a central location using AI.
7. Monitor Performance and Retrain Models
AI models have evolved from historical data. And their evolution is an ongoing process, as the data requires. You can set up regular check-ins to track AI performance for modelling that answers
Whether the data is accurately categorised.
Whether it’s free from errors.
Whether it is making valuable predictions.
This modelling allows to retrain and revising models over time.
8. Focus on Data Security and Compliance
Though artificial intelligence has the ability to access sensitive data, it does not mean compromising privacy or compliance. So, you must focus on picking up such tools that are compliant with regulations like GDPR, HIPAA, or PCC DCI.
The Future of AI and Data Management
For sure, the future of AI-driven data management is likely to be more aggressive, insightful, and easygoing. The possibilities will be enormous, driving from predictive analytics to autonomous warehousing of data. Overall, AI is going to be a game-changer in maintaining data governance while working on real-time analytics and business intelligence.
Beginner-level adoption of this smart technology today can set the stage for advanced use cases tomorrow, which can be seen in AI-led forecasting, customer journey mapping, and decision automation.
Conclusion
Data management and AI can completely transform the data-driven world. It can simplify and handle data flow, from assessing and setting goals to choosing the right tools and enhancing the quality of data to harness its full potential. AI tools can make it more effectively done. Reason? These tools have the power to automate and optimize many things that traditionally were lengthy, elongating the data lifecycle. Overall, artificial intelligence finds some impressive solutions to enhance data management services efficiency with accuracy and security while collecting, storing, processing, and assessing data.
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