Learn More About Data Virtualization Software
What is Data Virtualization Software?
Data virtualization provides agile data storage, retrieval, and integration. It accomplishes this through data layers, which serve as an abstraction of said data, allowing one to access and understand data in a simple and streamlined manner. With this software in place, businesses can access and modify diverse data through a single view.
Data is only valuable if it is accessible. A perennial problem that occurs at organizations, especially larger ones, is that business functions and departments can get siloed. In these cases, company data is not easily accessible across departments, oftentimes leading to different data sources for the same sets of data. In addition, those within a specific department (marketing, for example) might not communicate with another department (finance, for example) and as a result, thereof will maintain the same data in different systems.
What are the Common Features of Data Virtualization Software?
The following are some core features within data virtualization software that can help users in administrating, federating, and transforming their data:
Data administration: This software helps users make sense of and manage their data. As such, administrative features, such as database management, access control, and data security are musts. Data is only valuable if it can be accessed and understood so it is key that users can use data virtualization to manage different types of databases and integration methods.
In addition, it is not only about the “what” (e.g., data types and sources), but also the “who” (i.e., who can access the data). Data virtualization tools must give administrators control over data-related privileges and accessibility. Finally, it must allow users to secure access to data and provide additional support for security practices like IP whitelisting, attack mitigation, and data encryption.
Data federation: Data federation refers to the ability to map data or metadata from multiple autonomous databases into a single (i.e., federated) database or data view. With data federation, businesses can begin to manage and organize storage, networks, and data centers, as well as integrate this data into various systems and applications.
Data transformation: Data need not and should not remain stagnant, stuck in databases and merely glanced at from time to time. Instead, it is important to analyze it, combining different datasets and discovering trends across them. Data virtualization software can help with this through data modeling and data visualization. The former assists in structuring data in a manner that allows extracting insights quickly and accurately and the latter provides the ability to represent data in a variety of graphic formats.
What are the Benefits of Data Virtualization Software?
Profitability: Data virtualization can help to consolidate data and provide a bird’s eye view of a business' data. As a result, these platforms can assist in rooting out and removing duplicate data records and ensuring that data is consistent and clean. Dirty data can be extraordinarily costly, both in terms of the costs involved in cleaning as well as the costs involved in cleaning up messes that come about as a result of inconsistent data.
For example, a company without any form of data consolidation might have a metaphorical brick wall between the finance and marketing departments. If the marketing team, based on their datasets and sources, believes that the business is succeeding, they may pour exorbitant amounts of money into their campaigns. However, if they had a proper view of company data, they might realize that things were not as great as they thought. The overspend from the marketing team, in this case, could have been forestalled with data virtualization tools, helping them better understand their data across teams and functions.
Productivity: As is the case with other data tools, such as self-service analytics, efficiency can be significantly boosted with data virtualization software. Historically, data access and analysis was the purview of specialized individuals and teams, such as IT. As a result, others who had an interest in analyzing or even accessing this data were forced to wait in line and get it handed to them by the data gatekeepers. This was not a quick and efficient solution and was costly as well, both in terms of the need for specialized workers, as well as the fact that by the time the data was presented it might be stale and outdated.
Scalability: As an agile solution, data virtualization can easily scale as a business. In addition, it can be deployed across on-premises, cloud, or hybrid infrastructure.
Who Uses Data Virtualization Software?
Database administrators: Those who are in charge of storing and organizing data will typically be using or evaluating a host of different software offerings and categories. First, they will typically focus on data storage solutions such as database software. Concurrently or thereafter, they should consider data virtualization technology that can help them develop a robust data storage solution, helping their colleagues gain access to the company’s data.
Data analyst: Data analysts work with a variety of data sources and resources, often needing to access various systems to extract data. With data virtualization software, they get a logical data extraction layer, which makes their work easier. Now, they need not move around data and can instead use pointers to data blocks in order to conduct their analysis.
Data engineer: Similar to database administrators, data engineers focus on the consolidation and integration of data. They aid other team members, such as analysts. With their focus on the data within databases, as opposed to data itself, data engineers can benefit greatly from virtualization tools which can help them reduce issues with company data.
What are the Alternatives to Data Virtualization Software?
Alternatives to data virtualization software can replace this type of software, either partially or completely:
Data replication software: As opposed to data virtualization software which serves as a layer that connects disparate data sources, data replication software helps companies store data in more than one location to improve both availability and accessibility. Both software types can reduce the workload on databases (for example, transactional databases) where performance is key.
Data fabric software: Businesses focused on the integration of data can look to data fabric software, which is a unified data platform that enables organizations to integrate their data and data management processes. This software offers benefits such as the ability to explore and extract value from any form of data regardless of location by connecting stores of structured and unstructured data. It provides centralized access via a single, unified view of an organization's data that inherits access and governance restrictions.
Software Related to Data Virtualization Software
Related solutions that can be used together with data virtualization software include:
Database software: In order to use data virtualization tools, there must be data in the first place, which is frequently stored in repositories, such as databases, including relational, noSQL, and nonnative database management systems. Before looking to adopt a layer on top of one’s data it is important to have a firm understanding of and strategy for managing the data.
Analytics platforms: Data virtualization provides the ability to analyze data without needing direct access to the original source data. As such, data is ready to be analyzed and examined with tools such as analytics platforms, which provide a toolset for businesses to absorb, organize, discover, and analyze data. This helps reveal actionable insights that can help improve decision making and inform business strategy.
DataOps platforms: Although data virtualization can assist in a host of data-related tasks, it often does not provide a holistic end-to-end solution for data operations. For this task, DataOps platforms can help control the entire workflow and related processes and ensure data-driven decisions are being made; cycle times are reduced significantly and users are empowered with a single point of access to manage the data. Companies can leverage DataOps platforms to derive on-demand insights for successful business decisions.
Challenges with Data Virtualization Software
Software solutions can come with their own set of challenges. For data virtualization, it is critical that those who interact with, share, and analyze company data adopt the solution. Without adoption, business users risk accessing old and outdated data, or not being able to access data at all.
User adoption: It is not always easy to transform a business into a data-driven company. Particularly at more established companies that have done things the same way for years, it is not simple to force analytics tools upon employees, especially if there are ways for them to avoid it. If there are other options, such as spreadsheets or existing tools that employees can use instead of analytics software, they will most likely go that route. However, if managers and leaders ensure that analytics tools are a necessity in an employee’s day to day, then adoption rates will increase.
Data organization: Big data solutions are only as good as the data that they consume. To get the most of the tool, that data needs to be organized. This means that databases should be set up correctly and integrated properly. This may require building a data warehouse, which stores data from a variety of applications and databases in a central location. Businesses may need to purchase a dedicated data preparation software as well to ensure that data is joined and clean for the virtualization solution to consume in the right way. This often requires a skilled data analyst, IT employee, or an external consultant to help ensure data quality is at its finest for easy analysis.
Data security: Companies must consider security options to ensure the right users see the correct data, to guarantee strict data security. Effective analytics solutions should offer security options that enable administrators to assign verified users different levels of access to the platform, based on their security clearance or level of seniority.
Which Companies Should Buy Data Virtualization Software?
Businesses from across industries can benefit from this technology.
Health care: Within health care, a large amount of data is produced, such as patient records, clinical trial data, and more. In addition, as the process of drug discovery is particularly costly and takes a significant amount of time, health care organizations are using data virtualization software to speed up the process, using data from past trials, research papers, and more. It should be noted that data privacy concerns that arise in a health care context will still be relevant when deploying these solutions.
With data virtualization they are able to better access their data, thus helping health care organizations innovate effectively and efficiently. Sometimes, this technology is coupled with synthetic data software, which allows organizations to share and use the synthetic data without compliance concerns or exposing personal data.
Retail: In retail, especially e-commerce, personalization is important. Top retailers are recognizing the importance of data virtualization in order to access customer-related data from vast and disparate systems. With the proper software in place, these businesses can begin to get their data in order, and port this data into data science and machine learning platforms, as well as analytics platforms.
Finance: The use of data in financial services can yield significant gains, such as for banks, which can use it for everything from processing credit score related data to distributing identification data. With this software, data teams can access and process company data and deploy it to both internal and external applications.
How to Buy Data Virtualization Software
Requirements Gathering (RFI/RFP) for Data Virtualization Software
When assessing tools, buyers must keep in mind that as the company and its data scale, it may be necessary to reevaluate software options down the line. Therefore, when possible it is best to consider solutions that are scalable and offer different options or tiers depending on the amount of data and usage. Also, one should be sure to consider the heterogeneous data sources at their organization, to ensure that the product integrates with them.
In addition, it is important to consider the use case. If a business is considering an operational use case, a less full-featured traditional platform might suffice. However, if one is looking to use the software for analytics workloads, which can be more varied data wise, it might be wise to consider more robust solutions that can support autonomous performance management.
Compare Data Virtualization Software Products
Create a long list
To evaluate the software, buyers can start by jotting down all relevant data sources, systems, and data uses. With this at hand, it will be easier to assess whether or not they are supported by a given product. Businesses must take note of whether or not a seller supports various data types, such as file-based, relational, API-based, etc. Also, considering the development environment is crucial—whether or not the product allows for one to design virtual data views or semantic models and if they support web-based design environments, for example.
Create a short list
With a matrix of the business’ data ecosystem and requirements in comparison to the capabilities of the products, which can be facilitated by G2’s verified features, a business can determine where the greatest amount of overlap is. The ideal is for there to be complete overlap (i.e., the software can support all that the business is looking to accomplish). If there is no complete overlap, it is recommended to try to find a solution that is the closest fit and is within budget.
Conduct demos
Trying before buying is critical. Buyers must test out data virtualization products and see how it looks and feels. One should note down how fast it works, whether data queries are working as expected, and more. It is also important to ask questions and request features if they do not already have them.
Selection of Data Virtualization Software
Choose a selection team
Multiple stakeholders should be involved in the purchasing process, including those who interact with the business data, as well as data analysts and database administrators who are tasked with data organization and deriving insight from data. These individuals will all have different perspectives and will provide useful insight into the buying process.
Negotiation
As with any software category, the price is often flexible and should be questioned. Buyers may mention other prices and offerings in order to get a fair price. Negotiations can happen around factors such as contract length, the number of users, and more. It is recommended to dig into the impact of these factors in order to get a fair price.
Final decision
In larger organizations, the final decision would likely be made by the chief information officer (CIO). In smaller organizations, it may be the chief technology officer (CTO), or even the data analytics team, depending on the use case.
Data Virtualization Software Trends
Cloud computing
With the ability to store data in remote servers and easily access them, businesses can focus less on building infrastructure and more on their data, both in terms of how to derive insight from it, as well as to ensure its quality. With the move to the cloud, businesses have easier access to their data, but also more places their data can be. This makes data management even more important.
Volume, velocity, and variety of data
As previously mentioned, data is being produced at a rapid rate. In addition, the data types are not all of one flavor. Individual businesses might be producing a range of data types, from sensor data and IoT devices to event logs and clickstreams. As such, the tools needed to process and distribute this data need to be able to handle this load in a way that is scalable, cost efficient, and effective. Advances in AI techniques, such as machine learning, are helping to make this more manageable.
