Data Discovery: What It Is, Uses and Tools

Data Discovery What It Is, Uses and Tools

Data Discovery: What It Is, Uses and Tools

According to studies, 79% of enterprise executives believe that companies who do not use big data correctly will lose their competitive edge and face extinction. To gain a competitive advantage, 83% have taken up big data projects. Data discovery is the best way to extract maximum value from metrics, insights, and information.

This is fundamentally a term that describes how businesses gather information from many sources and use it to create real value. Improved data-driven decision-making can be a boon for businesses. It also allows them to share information across departments and create intelligent strategies.

Understanding your data is the first step to becoming a data-driven company. No matter if you are talking about an IT specialist or a CIO, all business users must be able to access and understand data in today’s digital age.

This is an analytic approach that supports success. It ensures that every employee in an organization can make the most of information by understanding it in an interactive, seamless way.

What is data discovery? What tools are available to help with this task? What is a data discovery platform? How can you use it in real-world business situations? We explore these topics and more in this article, beginning with the most fundamental question.

What is Data Discovery?

Data discovery is the process by which data is collected from different sources to identify patterns and outliers. Businesses can use data discovery tools and visual navigation to gain insight and answer crucial questions that will help them drive their success.

Let’s suppose you are an analyst, business owner, CIO, program manager, or CIO. Every employee in an organization must be able to understand, read and extract value from the data that comes in.

Modern business environment: Data can be used to derive value and It is essential for any company to succeed. Businesses can use data to gain competitive advantage, achieve goals, stay relevant, and maintain relevance in the digital age by being able to identify and analyze patterns and trends.

Also read: 5 Best Data Quality Issues and How to Fix Them

The concept isn’t a tool, but it can be used to create business value. To enhance your efforts, you have data analysis tools that can be used.

This level of discovery can also be described and categorized as:

  • Data preparation: This is the process of managing unstructured data from various sources and turning it into usable formats.
  • Visual analysis: The visual tools that enable users to interact with data and extract actionable insights.
  • Advanced analytics with guided guidance: Businesses can get a complete view of their data using a combination of visual and reporting techniques. This category allows users to identify patterns and relationships that can be used to improve decision-making.

The process can be broken down into two types: data discovery and data mining. The main difference is that one requires more technical know-how, while the other is more user-friendly and accessible to average users. Let’s take a closer look at them.

Manual Data Discovery

This type allows experts and scientists to do all the hard work of cleaning and preparing the data. Specialists had to be able to think critically before advanced technologies like machine learning could help them manage their data effectively.

Smart Data Discovery

This is a user-friendly and automated way to discover data. It uses software that allows you to prepare, integrate, and analyze data through interactive visualizations. This allows the average business user to use data quickly and intuitively. This article will mainly focus on smart types of discovery.

With the help of BI-Survey’s guide, the following sections provide in-depth analysis and explanation of the trend’s inner workings and why so many enterprises are making use of it.

Big Data Discovery: Why is it so popular?

We’ve now covered the data discovery definition. Let’s dive into this trend to see its main benefits.

The big data industry has seen an exponential rise in its popularity over the last decade, as we discussed at the beginning. Studies show that more data was generated in the past two years than at any time in history. This is in addition to the fact that the industry has helped create around 13 million jobs worldwide since 2012. It is one of the most important trends in the world and has the ability to predict patterns and connect. Let’s find out why.

  • Better decision-making: Data is a valuable commodity and “currency”, for businesses. Data is a trusted resource that companies can use to increase their competitive edge. It enhances decision-making, drives growth strategies, dramatically improves customer experience, and allows organizations to drive innovation in their business models.
  • Accessibility: It is not necessary to have IT or data expertise in order to gain business insight. Intelligent data discovery is essential in today’s business environment. This trend is accessible at its core. It provides the tools necessary to make data points easily understandable for all users. This empowers them to use data to support any decision.
  • Better risk management: Data handling is not without its risks. As data volumes increase, so do the requirements to ensure that it remains compliant with all applicable laws. Companies can identify potential threats, address them immediately and ensure compliance with data discovery tools.
  • Analytics saves time and money: Time is everything when it comes down to analytics. In the past, users would have to wait for days to receive a report that contained outdated information. Data discovery platforms offer both automated and real-time data access. Combining these two functions allows organizations to save time and money so they can optimize their processes based on the insights that were gleaned from the analysis.

How can you get started with this mindset? How can you implement this strategy in your organization? What visual analytics tools can be used to assist with this process?

First, your business can be tracked using specific metrics – Key performance indicators – and you get all the insights that your data offers. Depending on your industry or department, there are many KPI examples that you can choose from. You can then use the information from all departments and teams to help you make collective decisions and solve problems and create sustainable solutions in many key areas.

Also read: 5 Best Practices Used for Data Collection

Top Attributes to Look for in Data Discovery Tools

Business Intelligence Software is a tool that exists currently. These platforms are specifically designed to enhance traditional BI capabilities.

We have already mentioned the three steps of data discovery: data preparation and visual analysis. Advanced analytics should be used to provide the foundations for the next three critical stages. We have listed the key attributes of these types of tools.

1. Be user-friendly

Data discovery and analysis were traditionally left to professionals or IT specialists. This has all changed with the advent of modern technology and the decentralization of data. One of the principles of any modern tool must have an easy-to-use interface. This will allow everyone within your company to benefit from the information and tools that are available.

2. Be quick

The market has seen a rise in online data analysis tools in recent years. These solutions are being used by many businesses, so it is important to ensure that you choose technologies that will give your company a competitive edge. Software that is easy to use and has the necessary functionalities will help you focus your efforts. automated reports make it easy to create dashboards and reports that are updated with real-time data within seconds.

3. Use to easily work with large amounts of data

Visual discovery helps expand traditional business intelligence and improve efficiency. You should search for a tool that can easily integrate multiple databases from different sources. The discovery tool should not only connect the data but also allow you to work with large amounts of data. You can use advanced filters or recognize patterns.

4. Have interactive visualizations

Visuals are processed by the human brain much more quickly than numbers. A data discovery tool that is of high quality should have powerful, interactive visualizations to make it easy to analyze and work with the data. Datapine is a tool that allows you to create a dashboard. It includes many predefined templates that include different chart types and colors. This will allow anyone in your company to tell a story using data.

5. Advanced chart options

A well-developed software program should not only provide interactive visualizations but also include advanced chat options that allow you to do quality analysis and extract maximum value from your data. These advanced features include:

  • The ability to combine multiple types of charts into one.
  • To enable advanced analysis and comparison, add a second axis.
  • Different trend indicators, such as colors, can be used to indicate positive and negative outcomes.
  • Conditional formatting allows you to find hidden patterns or trends by allowing you to highlighting unexpected values.

You can use comparison period features to help you compare data from different periods. Then, draw conclusions based upon performance and targets.

6. Use custom fields

Another important feature to be aware of is custom fields. With the help of various join types, users can create custom fields and combine multiple categories dimensions into one field. Datapine, a professional dashboard tool, allows users to create custom fields with just a drag and drop.

7. Add predictions

When it comes to making investments in these solutions, predictive analytics functions are essential. Users can identify patterns and trends by using machine learning and artificial intelligence to analyze historical and current data. This can help them answer critical questions. Companies can make accurate predictions about future performance, which allows them to plan ahead for potential changes. The interface must be easy to use so that even non-technical users can visually examine data and make predictions.

8. It’s easy to share

Collaboration is key to data management success. Sharing capabilities are one of the key attributes that you should look for when purchasing BI software. Datapine lets you give different access levels to stakeholders depending on their roles and the data they need. Sharing insights and reports with ease will improve communication among teams, resulting in a more productive environment. You can also plan a schedule for sharing and automate the process so clients get the information they need on time.

9. Allow embedded analytics

To add to the above, embedding capabilities are another powerful functionality. It is becoming increasingly important to integrate functionalities that bring people together in a data-driven environment. Organizations are more aware of the value of collaborative data-driven environments. Embedded analytics is a bridge that allows for the integration of interactive dashboards and all their functions into both internal and external applications that can be accessed from any location. embedded dashboards are a great way to speed up data discovery.

You don’t need to be an analyst or be able to read programming code to get value out of the data in your business. Data discovery tools should allow people to share and read patterns between departments. The right tool should be flexible enough to adapt to your organization’s needs, whether you are extracting and evaluating KPIs, or preparing specific Retail Performance Metrics.

You must create a modern business environment in your company by implementing the discovery of data. This will allow you to remain relevant and profitable and foster a data-driven culture. You won’t be able to meet the demands of the digital age if you don’t do this. Your business will fall behind if you don’t know how to extract the most value from your data.

Also read: Top 10 Business Analytics Tools for Business

Data Discovery Use Cases

We’ve given you a description of the process and a few best practices for data discovery. We will now look at a few use cases within a business context to keep the value of these processes in perspective.

  • Strategic Planning: Data discovery systems are a great tool for strategic planning. They provide both historical and current information that can be used to enhance planning. This comprehensive view allows users to make informed decisions taking into account all factors. A CFO can use this information to determine the budget allocation for next year.
  • Identifying customer problems: Data discovery allows businesses to combine data from different sources and make innovative use of it. It allows them to see everything that is happening with their customers, including their behavior and interactions. This allows them to spot customer dissatisfaction, such as unanticipated customer churn, high-return product issues, or promotional failures. You can identify these issues using technologies like text sentiment analysis, which can be used to analyze what customers have to say about your brand.
  • Security prevention: Data breaches are becoming a growing concern. Companies have been forced to pay more attention to privacy and security since the introduction of new security regulations. Automated data classification can be used to detect security gaps. This involves the tagging of data and labeling it to identify any non-compliant information. It allows users to identify suspicious activity and protect themselves from external and internal security threats.
  • Social media analysis: The number of social media channels keeps growing, which leaves companies with the challenge of adapting and learning how to best use them. Through interactive social media reporting, data discovery allows companies to have a central view of all relevant indicators that relate to customer behavior. This allows for any issue to be addressed immediately and can allow efforts to be focused on the most effective channels.
  • Machine maintenance: In a traditional manufacturing firm, the only way to fix machinery problems is to send a technician. This can cause production delays and cost factories money. Manufacturers can use predictive analytics and self-service data discovery to analyze historical equipment efficiency and find failure points that could be avoided from occurring again.

This branch of BI continues to evolve as technology advances. We need to dig deeper into the business to understand what is happening as we implement it. It is where are we going? How is it impacting our ability to make good decisions and the future of our companies?

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