The convergence of powerful forces like the cloud, big data, machine learning, data virtualization, business intelligence, etc., is reshaping the world while unlocking many incredible business opportunities. These technologies allow us to connect everything in a way that dramatically transforms how we work and live.
With technological advancements come extensive data from various sources. As enterprises store years of data in numerous databases, finding missing data, combining disparate data, and determining which databases and data sets should interact becomes more challenging.
Data Discovery is the first step to overcoming modern-day data transparency and analysis challenges.
While some of you may be familiar with Data Discovery, others might need to learn more about it.
So, to sort it out, let me start from the beginning.
What is Data Discovery?
Data Discovery is the process of finding, arranging, and identifying unused, hidden, sensitive, regulated, and disparate data sources. With data discovery, one can get a coherent picture of a company's data that helps discover new trends, insights, and patterns to improve decision-making.
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Data discovery involves various data types, including structured, unstructured, and semi-structured data. Furthermore, it consists of multiple techniques and tools to search, classify, and analyze data, uncovering insights that can inform business decisions.
Also, data discovery acts as a predecessor to data visualization, allowing you to create compelling data visual representations that have been collected and analyzed.
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There are two ways to convey data discovery:
Manual Data Discovery:
It is the traditional method for conducting data discovery. The manual data discovery process involves creating manual data maps, classifying, documenting, and analyzing data by data scientists.
Smart Data Discovery:
With the introduction of technologies such as Business Intelligence (BI), Machine Learning (ML), and Artificial Intelligence (AI), the process of data discovery has been automated to make it seamless and quick.
Instead of depending on data scientists to conduct manual data discovery, smart data discovery uses these advanced technologies.
Process Behind Data Discovery
Data Discovery is an iterative process where companies continuously collect, interpret, and refine data. The steps include:
Data Exploration
Before conducting effective data discovery, you must combine data from disparate sources into a centralized database. As part of this process, data is cleaned, checked, and transformed to ensure it is formatted consistently.
Data Preparation
In this step, raw data is organized and handled by the software system with ETL (extract, transform, load), data visualization, and data warehousing for discovery and analysis.
Smart Data Discovery
In the last step, AI and ML are used to discover and prepare data patterns for visualization and reporting.
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Why is Data Discovery Important?
Data discovery forms a foundation for business agility, a characteristic of a successful business. With data discovery, anyone can gain an overview of their operations to understand and address the challenges, whether they're a financial controller seeking business reporting process efficiency or a Chief Information Officer (CIO) wishing to migrate to cloud-based solutions.
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Benefits of Data Discovery
Some of the significant benefits that data discovery offers to a business include the following:
- With data discovery, analysts can create meaningful, easy-to-understand visualizations.
- It provides a complete view of the primary data streams in the enterprise and combines them for analysis to develop solutions that solve their business problems or meet customers’ needs.
- It becomes easier to find reliable and high-quality data using data discovery.
- Data analysts can dive into multiple business intelligence data pools with explicit goals about the information they want to discover.
- Data discovery tools allow businesses to maximize their BI data to set improvement goals and gain competitive advantage.
- It can also analyze customer behavioral data collected from various sources, including service inquiries, purchase history, basic demographics, and online behavior.
- With data discovery tools, businesses can get valuable insights about their current performance and key improvement metrics for the future.
- With ever-growing data and demand for data protection by the government, compliance and risk management have become the top agenda for companies. Data discovery can help spot potential threats in the data so it can be managed proactively.
- Data discovery helps companies classify all the data accurately based on the sources, context, and conditions in which they are collected.
- Using data discovery tools, companies can collect, manage, and analyze data in real-time based on predefined controls or contextual factors, ensuring compliance and security.
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Real-World Applications of Data Discovery
Thanks to AI and ML algorithms, data discovery has become more refined, allowing enterprises to discover new opportunities.
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Here are some real-world examples of how data discovery is making a difference.
- Fraud prevention is a significant challenge for businesses operating online. Data discovery allows businesses to spot unusual patterns by analyzing internal and external data sources, enabling proactive measures to prevent fraudulent activities before they cause harm.
- Business planning is an area where data discovery can provide valuable insights. Data discovery allows you to make more informed decisions, eliminate inefficiencies, and optimize resource allocation by analyzing data.
- Processing insurance claims can be a costly and time-consuming process. While manual processing can lead to the risk of delays and errors, data discovery can speed up the process using AI algorithms to identify fraud by comparing suspicious claims and reducing the cost and time that comes with manual processing.
- Prospecting for new clients is a critical aspect of business development. With data discovery, businesses can collect and consolidate relevant data to create accurate customer profiles, enabling personalized and relevant marketing campaigns.
- Companies must analyze their Social Media accounts to maintain a positive online presence. Although managing customer relationships on multiple platforms manually can be challenging. A company can prevent PR crises and address complaints proactively by identifying behavioral trends through data discovery.
Read: Why Is Data Synchronization Important and What Is It
Conclusion
So that was all about data discovery, and I hope you enjoyed reading this as much as I did while writing. In conclusion, data discovery can help businesses with data visualization, customer behavioral analysis, social media analysis, risk and compliance management, business planning, predictive analytics, and more.
While I tried to cover everything I could in the blog, we have just scratched the surface. Shortly, I will share more informational content regarding data science.
