KILL DATA Silos with Data Discovery of GoTrust

Are data silos hindering your organization's efficiency and data management? It's time to break down those barriers with advanced data discovery tools. In this blog, we'll explore how you can KILL DATA Silos with Data Discovery and ensure seamless data integration across your systems. We'll also highlight the importance of using a reliable Data Privacy Compliance Tool like GoTrust to safeguard your sensitive information. By combining data discovery with robust privacy compliance, you can achieve a more cohesive and secure data environment. Let's dive into how you can unlock the full potential of your data with GoTrust. 

In response to this challenge, data discovery tools pose a very useful solution. Such tools assist in determining relationships between the fragments in our fragmented data space. A strong data discovery capability allows for linking and correlating the various data points, resulting in visibility for our organization. In this article, we are going to look at how data silos affect organizations, how data discovery solves the issue, and how GoTrust can be used to solve it. Join us to become part of a team that is committed to discovering more about the data resource. 

Understanding Data Silos and Their Impact 

Definition of Data Silos:

In our experience, data silos are defined as isolated repositories for data management within an organization that are not easily connected or integrated to other tools or systems. It has been seen that these silos are often created gradually and unexpectedly and hence come as a surprise to the leaders. In simple terms, a data silo is a condition where some people within a company cannot access certain data or information. 

Common Causes of Data Silos: 

We've identified several factors that contribute to the formation of data silos: 

• Lack of collaboration: Most organizational structures operate in silos where departments isolate themselves and embark on creating their methods, structures, and information. Lack of communication between several teams progresses and gradually expands to create data silos. 
  
  

• Varying toolchains: We've noticed that teams often use different tools for similar purposes. For example, in a software firm, one team may prefer Azure DevOps, another team may prefer Jira, while yet another may prefer Notion for requirements gathering.  
  
  

• Acquisitions and mergers: When two companies merge or acquire, there are always likely to be data silos because of diverse ways of working, computing, and technology systems. 
  
  

• Legacy systems: We've found that older systems that aren't compatible with modern technologies can contribute to the formation of data silos.  
  
  

• Organizational culture: Some departments may not be willing to provide their data to other departments because of security, privacy or control issues. 

Negative Effects on Business Operations 

Data silos have significant impacts on business operations and revenue growth. Here are some of the key negative effects we've observed: 

• Incomplete picture and poor decision-making: Data that can be shared across applications is usually distributed in nature. This makes it difficult for data analysts to get a complete picture of the scenario and hence end up making biased decisions and in most cases arrive at the wrong conclusions. A study conducted by Gartner revealed that bad data on average cause organizations a loss of $12 each year. 9 million. 
  
  

• Inefficiencies and duplication of efforts: Data is centralized in different departments; hence we do not communicate with each other; this results in wastage of time and yields low returns on sales. 
  
  

• Lack of collaboration and innovation: Growth of functional silos maintains competition rather than cooperation between functions, reduces cross-functional integration and idea sharing. 
  
  

• Data integrity and security risks: When multiple data stores accumulate over time, synchronicity between these various areas becomes difficult. This is because duplication of work leads to data disparity where the teams spend more time trying to understand the discrepancies. 
  
  

• Increased costs: The storage and maintenance of the backups of the datasets may cost hundreds and thousands of dollars. Second, the silos create different solutions for managing the data, which might be very expensive to maintain and update. 
  
  

• Poor customer experience: Maintaining data in isolated structures can cause miscommunication between different departments as customers may experience disparate services. This can lead to dissatisfaction and losing customers to other competitors in the market. 
  
  

• Employee engagement issues: When working in a siloed organizational environment, employees often lose sight of the role they play and contribution toward the success of an organization. This may lead to disengagement and lack of motivation, and thus high turnover rates. To illustrate the impact of data silos on business operations, let's look at a comparison of key metrics: 

• Metric With Data Silos Without Data Silos Annual cost due to bad data $12.9 million Significantly reduced Average cost per data breach $4.35 million Lower risk Decision-making efficiency Limited by fragmented data Improved with comprehensive data Collaboration between departments Hindered Enhanced Customer experience Inconsistent More unified Data storage costs Higher due to duplication Optimized 

We've seen how data silos can severely impact an organization's ability to use data effectively and make informed decisions. By breaking down these silos, we can improve collaboration, enhance data quality, reduce costs, and ultimately drive better business outcomes. 

The Role of Data Discovery in Breaking Silos 

What is Data Discovery? 

Data discovering involves identifying data, obtaining it from various sources within the organization and analyzing it to reveal underlying patterns and relationships. It encompasses searching both organized and dispersed data throughout the various systems, databases, and applications to develop an overall map of information within the organization. It's a powerful tool that democratizes data insight, allowing business users across all departments to understand their customers and operations without requiring IT or data expertise. 

In our experience, data discovery is a basic iterative process that occurs in five steps. This approach allows us to perform studies, assimilate lessons, and modify our methods over time based on feedback and results from business partners. As we have noted earlier, this process is beneficial to business leaders in enhancing their understanding of their business and the issues affecting them. 
One feature which we have identified for ourselves is the use of diagrams, text, and graphic potential as the means of telling the story and presenting the trends. This type of approach addresses the needs of individuals who are not directly related to IT but require significant amounts of complex data and rapid insight generation. 

How GoTrust Facilitates Data Discovery 

GoTrust is an important tool in facilitating data discovery because it is one of the prime data discovery tools within organizations. However, it assists us in breaking down data silos by providing an integrated environment where data consolidation occurs. This approach complies with the industry's best practice of consolidating vendors and creating a single source of truth. 

With GoTrust, all the intended teams get connected to siloed data, thereby promoting collaboration, efficiency and timely decision making and reduction of technology clutter. This efficient transfer of data collaboration improved co-ordination across departments and functions in the organizations and is beneficial to all the parties concerned. 

Moreover, GoTrust offers the creation of a semantic data layer that we consider critical for making intelligence available at the time of need, on any storage solution. This approach rises above simply presenting information to interpret what should be known, enabling better decision-making and offering purposes. 

Key Features of GoTrust for Silo Breakdown 

Data Preparation:

GoTrust excels in data preparation, an important step that precedes any meaningful data discovery and analysis. It aids in cleaning, formatting, and combining data from other sources to enable analysis in one uniform format. 

Advanced Visualization:

From our experience, the best solution has been the GoTrust visualization that has assisted us in understanding the content of data effectively. Charts, data flow diagrams, and other types of visualization make the analysis comprehensible for people with no background in data science because they present the connections between different data streams in an easily digestible manner. 

Guided Advanced Analytics:

GoTrust uses both descriptions and visuals to provide an overall impression of our company’s information. While traditional analytics format tends to provide a single, specific and specific way of presenting the given topic, guided analytics enable us to visualize the context of our data analysis, such as connections between streams of information from various teams and processes. 

Smart Data Discovery:

GoTrust utilizes AI and ML to explore and analyze Big Data that cannot be analyzed individually due to the time-consuming process. This feature is valuable for identifying potential suspicious activities like any unusual data access patterns to safeguard our valued customers. 

Automated Data Monitoring:

GoTrust’s AI algorithms offer data monitoring features and specific data protection policies that can be implemented automatically. This allows us to monitor, manage, and mitigate risks, including cybercriminals holding the organization’s data hostage or employees mishandling sensitive information. 

Through these features, we have found that GoTrust enhances our ability to transform our organizational data and resolves numerous challenges such as data silos, poor data stewardship, and organizational decision-making. 

Implementing GoTrust for Effective Data Integration 

We’ve discovered that integrating GoTrust into our existing systems is vital for maintaining data integrity and confidentiality while boosting operational efficiency. By using Data Privacy Management Software by GoTrust, we can effectively address the management of our data protection obligations as well as compliance with different privacy regulations. 

Steps to Deploy GoTrust 

• Assess Compatibility: First of all, we ensure the compatibility of GoTrust’s software with our existing infrastructure and technological environment. 
  
  

• API Integration: To establish integration, we use GoTrust APIs which allow easy interconnectivity between our current systems and GoTrust. Such an approach enables the constant synchronization of data and identical enforcement of privacy compliances. 
  
  

• Middleware Solutions: Sometimes we use middleware solutions that help us become intermediaries between GoTrust and our current systems. This aids in managing data fluxes and guarantees compliance with privacy standards despite not changing existing architecture and processes. 
  
  

• Custom Integration: For specific needs or unique challenges, we work with GoTrust's integration team or our IT department to develop tailored solutions.  
  
  

• Training and Education: Training is implemented to ensure that the staff know about the new changes to the processes and tools to be adopted. This helps our employees handle data integration issues, any complications arising and put data into action for business advantages. 

Best Practices for Data Integration 

• Unified Data Platform: We provide a single platform to gather data from multiple sources such as POS systems, online sales portals and inventory management systems. This gives us a clear unified view of our operations. 
  
  

• Data Standardization: We recommend that data providers align with industry standardization best practices to minimize integration challenges. 
  
  

• Automation: We use data integration solutions which would enable us to implement the required data transformation and synchronization tasks at a higher efficiency. This helps in avoiding integration complexities, saves time besides minimizing the amount of work largely done manually. 
  
  

• Master Data Management (MDM): We apply the MDM strategies to specify and govern a standard and correct referent of business data, like products and customers. This makes the data consistent and timely for decision making and improving the operation's performance. 
  
  

• Data Governance: To maintain data integrity and standard, we outline specific policies for data management and define roles and reporting procedures to ensure compliance with said policies. 
  
  

• Regular Audits: Our data is periodically audited and often checked for maintainability to ensure that the quality of data is not compromised or contains any mismatches or mistakes. 

  
  

Overcoming Common Challenges 

Breaking Down Silos:

We foster collaboration across departments to break down organizational silos and create a culture of data sharing. Since data integration involves several functions and systems, cross-functional teams help tackle fragmentation issues by applying thought patterns unique to each given function. 

Addressing Data Quality Issues:

The lack of complete or accurate data is also prevented and corrected through well-developed data quality management. This involves data validation checks, cleansing steps, and data enhancement steps to make the data as accurate and complete as possible. 

Scalability:

We need to select a data integration solution to address factors such as higher volumes of data as we scale without a negative impact on data processing time. 

Security Concerns:

To prevent unauthorized access to the information and maintain its confidentiality, we use strong security measures. This includes: 

• Data Access Management: Requiring high levels of authorization and permissions and revoking & changing them frequently. 
  
  

• Authorization Levels: Identifying and setting up varying degrees of permission of both read and write privileges for the data. 
  
  

• Data Management and Handling: Creating policies covering data management, storage, and decommissioning, this includes data encryption when in transit and especially when stored. 
  
  

• Regular Security Audits: Ongoing risk assessment of the integration procedures and technologies to assess where threats could arise. 
  
  

• Compliance with Standards: Guaranteeing that all our integration processes adhere to the proper industry standards and rules. 

By following these steps, best practices, and addressing common challenges, we can effectively implement GoTrust for data integration, enhancing our data management capabilities and ensuring robust privacy compliance across our organization. 

Conclusion 

Breaking down data silos with GoTrust's data discovery capabilities has a significant impact on enhancing business operations and decision-making. When organizations use GoTrust, the various data sources are connected and therefore organizations can be able to have a better understanding of how the information the organization has is structured and can be shared among the various departments. This process not only leads to improved productivity but also assists in avoiding violations of the law and protection of information. 

To wrap up, the journey to break down data silos requires a strategic approach and the right tools. As a leading data discovery product, GoTrust offers a powerful solution to tackle this challenge, providing features like advanced visualization, smart data discovery, and automated monitoring. Through an adherence to sound data integration principles and the identification of issues that can prevent these and other strategies from being effective, organizations are able to get the most from their data assets for the benefit of the business.