What Your Business Should Look For In Data Discovery Tools

What Your Business Should Look For In Data Discovery Tools

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Data intelligence is becoming increasingly important, with a recent Gartner study estimating that 33% of businesses will have data analysts as part of their organization by 2023.

However, these analysts need assistance in processes to speed up data discovery and improve precision. As a result, automation and data discovery tools have taken off as a way of delivering information in an agile way with less need for specialized technical teams.

This guide goes in-depth about modern data discovery tools, what you should look for when selecting a data discovery tool, and provides some recommendations.

What Makes a Good Data Discovery Tool?

Although there are numerous data discovery tools on the market, not all of them boast the same features. As a result, selecting an appropriate tool for your business can be challenging.

In general, there are seven core aspects of these tools that you should look for when choosing one for your company:

  1. Ease of use
  2. Deep discovery capabilities
  3. Ability to process big data
  4. Recognition of data types
  5. An insightful way of displaying data
  6. Collaboration features
  7. Price

Let’s take a closer look at each of these features and how they impact your organization’s success.

Ease of Use

The main thing businesses should look for in data discovery tools is the ease of use. This means that these tools are easily understood by any user, not just tech experts.

As a result, these tools need to be able to facilitate the exploration, analysis, and visualization of data in a way that other business professionals can understand.

Data discovery tools should have visual interfaces with easy-to-understand graphics and interactions that contribute to intuitive use. They should also automate data display wherever possible in order to allow users of all aptitudes to interact with the information.

Doing so reduces interaction cost, which is the amount of physical and mental effort expended to use a platform. That, in turn, helps improve turnaround times and reduce delays, helping cut costs in other areas of business operations.

Deep Discovery Capabilities

Some data discovery tools allow for better exploration of data than others. Deeper exploration means better insights into scattered or disorganized data.

This helps identify data relationships that can then integrate into software and applications and tie information together. Then, hidden patterns useful for analysis and evaluation are extracted, prepared, and visualized in a way that’s easier to understand.

As a result, data professionals can make better decisions, optimize business processes, and promote new market models. The result can mean reduced business expenditures, better-streamlined processes, and increased revenue.

Ability to Process Big Data

Big data is high volume, high velocity, and highly varied data sets that are difficult to process. That means big data can make it tough for companies to pull out necessary insights and intel from available information to further business objectives.

Additionally, working with big data involves interacting with a variety of sources and formats. This presents a challenge for many data discovery applications.

When looking for data discovery tools, look for one that is suitable for managing and controlling large amounts of information. It should also be able to reduce the generation of pre-calculations regarding quantitative data and reliance on predefined metadata.

Doing so enables your business to get more value out of the information it processes, as well as to keep up with high-velocity data.

Recognition of Data Types

Data discovery tools should have the ability to discover and extract meaningful information regardless of the type of data you’re dealing with. Otherwise, your business will be limited to only being able to work with or handle certain data sets.

Before investing in a data tool, identify which data types you’ll be handling in the present as well as which you may need to work with in the future. This also helps your data tool to classify better and analyze data sets that you process.

Display Data in Insightful Dashboards

The way that data is displayed in a data discovery tool goes back to ease of use. When data is displayed in smart panels, it allows clearer and easier visualization of the information, as well as an opportunity to show KPIs and analyze results.

Additionally, insightful dashboards can help provide a quick look at important discoveries, making managing data much faster. It allows professionals to get the insights they need without having to dig for them within the system, speeding up processes and reducing effort.

Collaboration Features

Data sharing and collaboration are two major components of working with data. That’s because these systems allow organizations to develop organic and cohesive approaches to handling information.

A good data discovery tool should make it easy for inter-departmental collaboration, allowing various teams to work together on projects and share insights. This helps departments support one another, and makes business intelligence an integrated part of business operations as opposed to being isolated to a single team or department.

Price

The cost of data discovery tools can be quite high. However, so can the costs of additional time spent trying to uncover insights without these tools’ data automation assistance.

Before committing to any data discovery tool, it’s important to look carefully at the costs and benefits of the tool. This will help you get a strong ROI on data discovery without incurring sunk costs into overpriced systems.

Advantages of Data Discovery Tools

Advantages of Data Discovery Tools

Data discovery tools can provide businesses with a number of advantages, such as furthering business objectives, saving time and resources, improving manual data discovery, and keeping companies compliant.

Additionally, they can also help businesses extract more insights from the information that they currently have available. To break it down, let’s look in-depth at some of the advantages of data discovery tools to businesses.

Saves Time and Resources versus Manual Data Discovery

Data-related costs increased significantly between 2019 and 2021 as opposed to the previous three years, in some cases, by as much as 50%. Data is costing businesses a significant amount of time and resources, making it even more important to find ways to curb these costs and reduce expenditures.

Manual data discovery increases the amount of time and resources used. Here’s a quick look at a few of the differences between manual and automated data discovery.

Manual Automated
A team of analysts with advanced technical knowledge is necessary to configure all the files and the data sources to be explored and to guide the process in general. It combines the best of automated processes, artificial intelligence, and natural language processing to explore data and can be used by anyone who isn’t an expert.
People are in charge of drawing the final results on a map to understand the data. The data discovery tools are the same ones in charge of presenting results in a dynamic, visual, and didactic way.
The amount of possible data to handle varies according to the number of people in charge and the long time for results. Handle high volumes of data without any problem.

As explained in the table, manual discovery requires a group of expert analysts with the ability to configure files and the data sources to be explored and prepare queries. All of this takes a significant amount of time, as well as investment into the team managing and overseeing those tasks.

Additionally, human error can play into this, causing the need for these processes to be repeated or corrected later down the line.

In contrast, data discovery tools automate these processes. They allow non-experts to access information and speed up processing times by eliminating the need for intervention from these highly skilled teams to understand information.

They can also reduce human error, helping speed up any interim processes of correcting data before it can be used.

Helps Maintain Regulatory Compliance

As mentioned, data discovery tools can help reduce human error. One way they do this is by cleaning the data to identify any anomalies such as spelling errors, dates, or incorrect account numbers.

Then, those systems ensure that data is correct before it’s entered into business systems.

This error reduction, in turn, helps maintain regulatory compliance. It ensures that businesses aren’t processing erroneous data or compromising data security.

One example of regulatory compliance that many businesses need to follow is a European regulation called GDPR. GDPR principles help guide how data is stored, processed, and managed, and with strong data discovery tools, businesses can ensure that they’re adhering to those guidelines. Doing so can help them avoid fines and legal trouble later down the line.

The table below outlines some of these principles and how data discovery tools can help businesses adhere to them.

GDPR Principle Impact of Data Discovery Tools
Monitoring Data discovery tools support continuous data management by allowing businesses to update, sort, process easily, and organize information.
Accuracy Data discovery tools help identify and correct data sets discrepancies, ensuring that all information stored within a database is accurate.
Lawfulness These tools can help businesses properly store client content and organize them in a cohesive manner. They also ensure updated consent records to continue working with client data.
Storage Limitation Data discovery allows businesses to identify key data pieces and limit storage to only those pertinent to the lawful base they’ve established for using personal information.
Integrity and Confidentiality Businesses can conduct data protection impact assessments to determine whether a particular data processing activity could compromise a user’s information.

When businesses use their data discovery tools in this manner, they can avoid running into legal issues due to incompliance.

Helps Businesses Extract Valuable Insights from Raw Data

Raw data refers to data that a source has gathered before analyzing or procuring insights from that data.

Data discovery tools help process raw data, pulling out important analytics that can give businesses actionable information to implement. This helps convert raw data into useful processed data.

From there, businesses can monetize data for internal use or package it for data monetization, sharing, exchange, or a host of other options. This processing of raw data can help further business initiatives and increase organizations’ bottom line.

Simplify Data Fulfillment with Revelate

Revelate provides a suite of capabilities for data sharing and data commercialization for our customers to fully realize the value of their data. Harness the power of your data today!

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Traits of an Effective Data Discovery Platform

Data discovery tools serve as an automated platform to find new information, refine it, and make it available to the company in the most visual and clear way possible.

Automatic data discovery has very particular ways of operating that is different from manual data discovery. The four main traits of an effective data discovery platform include:

  1. Speed
  2. Accessibility
  3. Specificity
  4. Collaboration

Let’s go further in-depth on each of these traits.

Speed

One of the most important aspects of effective data discovery tools is that they allow businesses to process large amounts of data quickly. These tools have configurations that allow them to find the required information almost immediately.

In contrast, when teams conduct manual data discovery, they have to sift through large volumes of information on their own without the support of algorithms to make the process easier. This can make finding data insights tedious and time-consuming.

Accessibility

Data discovery tools simplify understanding, discovering, searching, and visualizing data. They’re created for users who aren’t necessarily specialized in data management with simple and user-friendly interfaces, making data discovery more accessible to multiple team members.

Specificity

Data discovery tools can focus on very specific data sets, pulling out highly refined insights. In addition, they can create insights and analytics that offer very specific pieces of information to businesses.

Collaboration

Data discovery tools also allow multiple users within an organization to collaborate. Many of these tools have features that make sharing insights easy across the platform. As a result, multiple departments can access the information through the tool in order to share analytics that would help their own objectives.

Why is There a Need for a Data Discovery Tool?

Why is There a Need for a Data Discovery Tool

Data discovery tools are important for helping businesses make decisions and reduce human error. They also help streamline processes and search for very specific information within large data sets.

However, that’s not the only reason data discovery tools are so important. Today, the data discovery market is projected to reach over $16 billion, and the market is only set to continue growing.

In a landscape that’s growing so quickly, that makes it more important than ever to have effective data discovery tools in your wheelhouse.

A few other reasons why there’s a growing need for data discovery tools include:

  1. They increase productivity
  2. They allow for higher data precision
  3. They support evidence-based management
  4. They make forecasting simpler.

Here’s a bit more detail on each of these reasons why data discovery tools are so necessary.

Productivity Increase

Manual data processing requires a lot of time and labor as well as a significant amount of resources. Data discovery, on the other hand, improves productivity by analyzing data and allowing employees to allocate their time better.

In addition, data discovery tools can help users work in centralized systems. This provides any member of the organization access to up-to-date data in real-time.

Higher Data Precision

Automation also allows for higher data precision. Data discovery tools apply standards to data processing, helping create efficiencies and catch any anomalies or errors in the data that it finds.

From there, these tools can list those errors before saving information, making it easier to avoid them in final reports.

Evidence-Based Management

Data discovery tools track and work on custom KPIs so that companies are constantly monitoring their results, allowing them to stay on target and not lose performance.

In this way, the decisions to be made to improve are made quickly based on the data provided. Users can track highly relevant data in real-time such as:

  • Levels of efficiency and innovation
  • Normative compliance
  • Risk management
  • Customer service
  • People development

All of this makes data management much easier and ensures that organizations meet their goals.

Work on Forecasts

Data discovery tools also allows users to test models and scenarios before starting to use them in real scenarios. This helps them to make forecasts that can give them a competitive edge and use their data insights to their advantage.

Essential Elements of an Effective Data Discovery Solution

Essential Elements of an Effective Data Discovery Solution

Once your team has selected an appropriate data discovery tool, it’s time to implement a solution that helps your team achieve its objectives.

Three essential elements of an effective data discovery solution make it possible for businesses to gather appropriate insights from their data. These include:

  1. Looking at more than just the metadata
  2. Scaling information
  3. Correctly training AI

Here’s what else to know about these three factors.

Going Deeper than the Metadata

Data discovery allows data to be stored and modeled from structured or unstructured sources. This minimizes reliance on predefined metadata and ensures specific data discovery within a vast ocean of information.

Despite this, some data discovery solutions only work on metadata, providing information about the data without actually looking into a particular data set. However, this can cause limitations to what companies can learn from their information. Sensitive data, for example, can’t be reached with this level of search.

An effective data discovery tool should go beyond just the metadata and be able to pull out insights from the data itself. This provides real value to organizations from the information within a data set.

Scaling Across Large Volumes of Data

Many times, the amounts of data that an organization is dealing with are so large that they can’t be stored in one location. These quantities are known as big data.

However, the distributed storage system of data discovery tools allows scalability, with the ability to add more units to the network. Additionally, these systems break down data sets when appropriate to continue growing systems organically without compromising the order or structure of organized data.

AI Trained on the Correct Data

Artificial intelligence, automation, and other technologies used together for data classification and enrichment must be trained properly. Otherwise, these systems don’t know what data to ignore and what to look into.

A good data discovery tool is able to facilitate programming these systems to correctly pull out data that can provide appropriate analytics to businesses.

Approaches to Data Discovery Solutions

Depending on your organization and how different stakeholders need to gather and use data, different approaches to data discovery are necessary to consider. For instance, if you’re just looking to push data to programs for reporting purposes, using software such as Tableau may be all that you need. On the other end of the scale, if you’re a data scientist that deals with massive amounts of incoming data via a real-time data stream, then robust data discovery and cataloging software like Databricks or Snowflake would be needed.

Gathering Data for Reporting

Various departments in an organziation may produce reports for the executive team to review. Gathering the data from across the organization to be used in these reports, however, can be easier said than done without an effective data discovery solution. The default for most organizations is that data lives in silos, and therefore can only really be accessed by individuals within that department. In other words, if marketing needs financial data to determine the exact value of website conversions for a specific product, that data may not be easily accessible to them.

Data accessibility starts by building an organizational culture that embraces data. One of the ways this can be accomplished is by using a tool like Revelate. With Revelate, datasets can be made available for different departments to access immediately. For instance, if the finance department knows that marketing usually asks for a specific dataset for their reports, that dataset can be created and displayed on Revelate’s customizable data marketplace. Then, then marketing needs it, they simply request it (with the necessary security checks and balances being performed, of course) and download it directly into a program like Tableau for immediate use.

Data Discovery via Data Marketplaces

Data marketplaces provide another avenue for data discovery. Marketplaces can be used to discover internal or external data, depending on the use-case. For instance, a data analyst may use a data marketplace to find new data sources to augment existing internal data for business intelligence purposes. On the other hand, like we mentioned in the previous point, data marketplaces can be used by employees to request datasets from other internal departments across the organization.

Data Discovery for Data Scientists and Other High-Level Professionals

Increasingly, organizations are discovering the potential of using big data. By having a ton of real-time data flowing into an organziation at one time, there’s always current data available to be used for a plethora of scenarios, from improving operations, developing new products, or gaining a better understanding of a target market.

For data scientists to effectively manage this data, they require robust data catalog tools that can help them make sense of data as it enters the organziation’s data ecoystem, as well as keep it organized. Data catalog tools alongside data management platforms like Databricks and Snowflake are essential for making sense of high volumes of data, and ensuring that it’s accessible to both internal and external stakeholders in usable states.

Final Thoughts

Data discovery tools offer companies a way to create better efficiency in the data discovery process, leading to better information analysis and insights. Additionally, they support regulatory compliance to help businesses avoid legal troubles.

In order to find an effective data discovery tool, businesses need to look for systems that are easy to use, support deep discovery, allow for collaboration, and recognize a variety of data types.

Another major part of reducing the burden of IT and analytics teams involves having a solid data platform that lets you visualize your data and properly distribute it within and outside your organization. Book a demo of Revelate today to discover how to improve these objectives within your organization.

Simplify Data Fulfillment with Revelate

Revelate provides a suite of capabilities for data sharing and data commercialization for our customers to fully realize the value of their data. Harness the power of your data today!

Get Started