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Smart Data Discovery

The phrase ‘data is the new oil’ is fast becoming a cliché. But it actually points to a reality no business, organization, or individual can deny: the supremacy of data in the 21st century. It validates the idea that organizational success today is based on a firm’s ability to harness the power of data. However, it is now a widely accepted fact that humans lack the capacity to harness the power of vast amounts of data available to companies today. It is here that modern technologies like artificial intelligence (AI) play a vital role by enabling smart data discovery for businesses.    

Research suggests that by 2025, the total amount of data created, curated, copied, captured, and consumed will amount to 180 zettabytes. To put that into perspective, consider that one zettabyte is equal to a trillion gigabytes!  

Despite the evident benefits of utilizing the power of data, nearly half of all companies struggle to turn unstructured and even structured data into actionable information.

In this article, we will look at smart data discovery and its benefits for enterprises.   

What is Data Discovery?  

Data discovery is the process of collecting data from multiple data sources (including third-party sources) and analyzing it to uncover trends, insights, and metrics. It generates actionable information that would otherwise not have been discovered.   

Data discovery involves both structured and unstructured data. It provides you with a comprehensive outlook of the business and its processes, letting you make better decisions. When making data discovery, you’re only looking to find patterns of information that can help you make better business decisions. You don’t necessarily have to have something particular in mind while performing data discovery.   

At its core, data discovery is an iterative process. It involves using text, diagrams, and visual storytelling to extract valuable business insights that can be shared across enterprise teams. The best part about it is that it enables your non-IT teams to comprehend and analyze large volumes of information.   

Thus, it enables both collaboration and democratization of data insights.  

Let’s take a dive into how data discovery is performed.  

How Do You Discover Data?  

The process of data discovery constitutes five steps. And since it is an iterative process, you can continue refining your data discovery methods by incorporating the results of previous findings and collecting and analyzing more data.  

Here’s how it works.  

Step 1 – Identify Your Goal  

You should have a clear goal in mind before you embark on discovering data insights. It ensures that your data discovery is efficient and effective. This does not mean that you should presuppose what you will learn. Instead, it just means you know what kinds of data would be worth exploring.  

For example, an e-commerce website might want to perform smart data discovery to uncover the reasons why customers are falling out of the sales funnel. Or you could be a retail bank trying to discover system vulnerabilities through data discovery. In other words, try to formulate your goal in terms of resolving a pain point.  

Step 2 – Combine Data From Applicable Sources  

You need to perform data crunching to ensure effective data discovery. This entails combining and integrating data from all the relevant data streams to get a comprehensive picture of your business processes.  

Step 3 – Data Cleansing and Preparation  

Data cleansing is a vital part of the data discovery process. It removes what many call dirty data from your data streams so that you can get a clearer picture from your data analyses.   

Step 4 – Analyze the Data  

Once your data is cleansed, combined, and integrated, you can use it to get a complete picture of your business operations, thus enabling you to uncover the obstacles that prevent operational excellence.  

Step 5 – Record the Findings and Iterate  

Effective data discovery is never a one-time process. Instead, it is a commitment you make to improve your business process efficiency continuously. As with all other worthwhile things in business and life, practice and iteration will strengthen your data insights discovery.  

Let’s explore briefly how the concept and process of data discovery evolved over time.  

Evolution of Data Discovery  

Data discovery is not a new phenomenon. It has its roots in the 1960s when economic gurus referred to it as data fishing, a crude term for data mining. It gained more attention in the 1990s when data scientists started relying on data mining to fuel business growth. Even the academic community focused on it to find how it could benefit organizations and businesses.  

However, the real boom in data discovery came with the advent of big data, artificial intelligence and machine learning algorithms. It gave birth to data-driven decision-making proper. Today, be it a pet store around the corner of your block or the leading financial institution of your country, all organizations from all sectors employ data discovery to enable intelligent decision-making.  

Categories of Data Discovery  

Data discovery emerges in various forms, models, and visual outputs. To make it effective, you must understand the relationship between different enterprise data streams. Use business intelligence (BI) tools like Power BI and Tableau to create the following three categories of data discovery.   

1.  Data Preparation  

As stated before, data preparation is the prerequisite for performing smart data discovery and analysis. It involves the processes of data cleansing, merging, and reformatting. It also includes removing data duplication, removing null values, and identifying outliers to ensure that only the correct data type goes into the analysis stage.  

2. Data Visualization  

Data visualization is a powerful way to analyze business data insights. To visualize data, you can use diagrams, flow charts, graphs, dashboards, and other pictorial representations. The most significant advantage of data visualization is that it makes your data understandable to other organizational teams that don’t possess IT or data analysis skills.   

3. Advanced Analytics  

Advanced analytics is the part where you bring together visualizations and descriptions to render a holistic picture of your firm’s data. Unlike your classical data analytics results, advanced analytics emphasizes the interplay between multiple data streams to make you aware of its implications.  

Moreover, advanced analytics is particularly useful for companies undergoing digital transformation. Suppose a retail store was moving to eCommerce. Such a transition would require you to integrate existing data streams with web data to enable appropriate strategizing.    

Business Benefits of Data Discovery  

Data discovery is actually a part of business intelligence. It involves collecting and consolidating information from various data sources to unearth otherwise hidden patterns.   

Here are some of its benefits:  

  • A comprehensive picture of company data Data discovery gives you access to a broad and overall view of the multiple data streams in your company. You can use it to develop better, more efficient business processes and give your customers an improved customer experience.   
  • Democratization, collaboration, and improved decision-making – Business insights should not remain confined to IT teams; every team in your organization should have access to valuable business metrics and insights.  

This enables improved collaboration and efficient decision-making. For example, your sales team can use data discovery to reveal why potential customers don’t go through the entire sales funnel, or your finance team can highlight unnecessary operating costs in other departments.  

  • Better risk management and regulatory compliance – Today’s tech-enabled and data-driven world have compelled governments to be more mindful of how companies use data. Thus, compliance and risk management have emerged as priority areas for companies. Data discovery helps you comply with regulations by revealing potential data threats and outliers.  
  • Contextual data classification – Companies receive tons of new data every week from various sources, including third parties. This data comes in many different formats. Data discovery enables you to automatically classify all this new data according to the context in which you collect or receive it.   
  • Real-time data controls – With smart data discovery, you can ensure that your data is appropriately stored and analyzed by applying specific actions to it. Such measures may include using contextual factors or predefined controls. It also ensures that your data is compliant with government regulations.  

Wrap-Up  

New technologies like AI, machine learning, and deep learning disrupt traditional business practices. As the world continues to transform digitally, the power of big data will grow exponentially. Business intelligence tools and data discovery processes are at the core of this shift.  

These technologies come together to offer immense business opportunities. They allow us to make our business processes more efficient, fuel growth, and help develop innovative products. None of this is possible without utilizing the incredible power of big data.  

We at Xavor offer cutting-edge AI/ML solutions to our clients, including startups and Fortune 500 companies.  

If you’re looking to build a data discovery process specific to your organization, reach out to us at info@xavor.com. We would love to make a complete end-to-end data discovery solution aligned with your business requirements! 

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Muhammad Abbas

Muhammad Abbas is an anthropologist at heart and a Marketing Specialist by profession. He has worked with leading marketing agencies over the years. While he considers himself a history buff, his interests also extend to topics like politics, economics, social justice, climate change, and tech.