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Best Data Discovery Tools for Business in 2020 - Apply For Demo

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The process of extracting data from various databases, archives, and silos to combine it together to form actionable patterns that can be easily and quickly evaluated is called data discovery. The process of extraction is performed by humans or sometimes it can also be done by artificial intelligence systems

A large amount of diversified data is fed into business systems in order to gain business intelligence (BI). Data discovery requires skills to understand complex data structures and relationships to derive insights. 

Data is regarded as an invaluable asset for businesses.

  • It assists companies and organizations to develop reliable insights that can be applied for their competitive advantage.
  • Data improve the overall decision-making process.
  • It boosts growth strategies and customer experience
  • It enables companies/organizations to drive innovation through their business models.

Data discovery matters because it is the first step to unlock the value behind any data. The process takes jumbled and scattered data from various sources, and assembles it in a way that can be understood by both humans and machines. Through the process, users get more familiar with the available data and are able to highlight key insights important to the company’s success.

Data accumulation and arrangement help business leaders to connect with other relevant enterprizes and external data sources. As the data is becoming complex, users need some flexible functions to access and prepare the data for analysis. 

A software that allows you to locate the metadata so you can organize, manage data effectively and make data-driven decisions is referred to as data discovery software. 

These tools are capable of finding metadata, patterns, and trends among huge piles of data.

Data Discovery software enables businesses and organizations to unlock various insights hidden in their data in order to act upon them. Data discovery tools have many benefits such as:

  • Assemble actionable insights: From trends and patterns to KPIs, database discovery software can quickly generate valuable information from heterogeneous data. It breaks down the complex data structures into a presentable form so that the user can read and understand it properly.
  • Time-efficient: Data discovery tools require data to follow a particular format. Although, data isn’t stored to allow that requirement most of the time. These tools accumulate and format data to aid their analysis. Thus, users get the right data in the right format.
  • Allows to clear and reuse data: Data flows constantly. As new data arrives, the older data needs to be cleared and stored for future use. Data discovery solutions leverage both new and old data so it can be reused reliably.
  • Centralization: Data is versatile and often it contains info which can be used in the various analysis. Different teams or departments can use the same data to extract different insights. Data discovery software simply this process to ensure that all users are using the same data.      

As said earlier, data is considered an invaluable asset for businesses. It is predicted to provide high value for innovation and success. More and more businesses and enterprises are adopting a data-driven approach that aims to go beyond just reporting and tracking the company’s performance.

The purpose of data discovery is to utilize the full value of data to improve decision making, optimize business processes, and deliver new business models.

Manufacturing decisions and long term planning require data backing. For that, any particular organization needs relevant and reliable data periodically.     

The feasibility of any data discovery tool depends upon the capabilities of the tool used. There are many solutions available out there that provide a different set of features. So the first thing you need to figure out is what kind of features your organization needs. Not all data discovery software fit all kinds of businesses. Although, there are certain attributes that would benefit most of the users and are sort of a necessary part of a data discovery solution:

  • Minimal need for IT support No doubt that IT experts will always be required when you’re dealing with technology, but an effective database discovery software should be such that it can be handled easily by non-experts as well. Data accumulation, classification, and representation should be automated as far as possible.
  • Support for customization according to users’ needs: Every business has some specific data requirements which according to them is important to carry out the analysis. So, a data discovery tool should allow customization to fulfill the specific data requirements. By selecting certain parameters and filters, business leaders will get exactly what they are looking for.
  • Multi-platform support: Data discovery is a continuous process. And it is not something that a single person can carry out alone, it’s a collective task. An effective data discovery software should be available across multiple platforms to promote usability. This can be achieved through cloud-based data discovery tools. It allows users to access the same data over different devices simultaneously.
  • Advanced data collection and processing abilities: Information can be extracted from ample sources. Therefore, the data discovery tool needs to present info in a way that is easy to read and understand. 

Access to full visualization: an ideal data discovery tool not only presents information in an easily interpretable way but it has to do it without leaving behind appropriate data. It should be able to present full visualization to users so they can comprehend the complete picture rather than just bits of it.  

Data discovery tools are also referred to as business intelligence software. These tools are basically designed to enhance traditional BI methods.

Data discovery solutions are primarily divided into three categories - data preparation, visual analysis, and guided advanced analytics. Here are some common tools in each category:

  1. Data Preparation: Alteryx, Cirro, ClearStory Data, Dataiku, Datameer, Datawatch, etc.
  2. Visual Analysis: Advizor, Comma Soft, Dimensional Insight, IBM Watson Analytics, Microsoft Power BI, etc.
  3. Guided Advanced Analytics: Coheris, Dell Statistica, HP Haven, IBM SPSS, RapidMiner, etc.

There are many data discovery solutions available out there and many of them have the following features in common: 

  • It provides a code-free setting for data analysis. 
  • It allows smooth integration of data preparation and analysis.
  • It allows sharing insights with different team members and departments.
  • It supports visual and easy to read and understand data representations.
  • It even offers unorthodox advanced analytics features to support statistical comparisons. 
  • It provides data preparation and representation proficiencies like integrating data from various sources.
  • It allows interactive visualization for the users.
  • It allows users to access data from a variety of sources.

Successful data discovery relies on various factors like accuracy, completeness, manageability, and consistency of the data. Thus, these factors pose some challenges to data discovery.

  1. Size: It refers to the extensive amount of data produced and stored. This can become a stumbling block in the analysis. Therefore data discovery needs to overcome this by adequate regulation and technology.
  2. Diversity: As technology evolves, data keeps coming from different sources. Not only this but data formats are also increasing rapidly. So it becomes difficult to present data consistently. A proper data discovery requires advanced technical skills to combine and clean data to get it ready for analysis.
  3. Speed: This refers to the rate at which data is being created. With technological advancements, the speed at which information is produced is increasing with each passing day. This is another kind of challenge data discovery faces. To overcome this, new data needs to be added to the archive continuously and accurately to derive timely insights.
  4. Consistency: As said earlier, data comes fast is a variety of formats and it needs to be stored. Accomplishing all these maintain the consistency of the data. It ensures that everyone in an organization has access to the same information across different teams or departments. Inconsistencies generate poor decision making due to out of sync and non-updated data.
  5. Data Management: If data is collected and stored inaccurately or inappropriately, then it will lead to errors and undesirable results in the analysis. Therefore, data management is a crucial aspect of data discovery. Properly managed data not only speeds up the analysis process but it also becomes easier to search and locate specific information.
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Logikcull

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board Business Intelligence

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Frequently Asked Questions (FAQs)

  • A. Business Intelligence refers to the collection, analysis, and demonstration of business data by using different technologies, applications, and tools.
  • A. Basically there are three kinds of data discovery solutions available out there:

    - Search engine like tools for textual search queries
    - Visual interaction tools for images and graphics processing
    - AI-based tools that are capable of recognizing patterns and trends in a sea of data
  • A. Big Data Discovery: With a combination of big data, data discovery, and data science, this approach helps in creating business insights.
    Smart Data Discovery: It depends on the latest technologies like machine learning and artificial intelligence for analysis. This approach is more human-controlled.
  • A. Business analysts, manufacturing firms, and companies that need to unlock the true value of data use data discovery tools. These tools help businesses reduce risks, customize insights, and identify the flaws in business processes.
  • A. Visual and search-based data discovery are the two common data discovery processes. However, they differ as follows

    - Search-based data discovery provides a broad scope to unveil data from structured as well as the unstructured data model, whereas visual data discovery uncovers relevant data from dashboards, reports, tables, and charts.
    - Visual data discovery empowers non-technical users to produce more descriptive and advanced analytics as opposed to search-based data discovery.
  • A. To get an effective data discovery tool for your business, it is crucial to check if the tool poses – limited IT support, extensive customization, compatibility across different platforms, full visualization, and advanced data gathering and refining abilities.

Nikunj DudhatBy Nikunj Dudhat | Last Updated: October 27, 2020

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