BigID Next

BigID  automated the process of Data Subject Requests (DSR) and data discovery of databases containing personally identifiable information (PII).

I worked on a project using BigID  for automating DSR and data discovery for PII. The project was divided into several phases. The first phase involved business contact and socialization, where we reached out to different stakeholders and learned about their databases and which tables contained the most PII. The next phase was the technical gathering phase where we understood the IP address, tables, port numbers, and whether the IP was dynamic or static. The subsequent phase was the build and connectivity phase where we created data sources from those databases.

The most valuable part of BigID was the scan and result phase, which provided nearly accurate results of the databases containing PII numbers and the metadata of the PII present in the database. It also described what the tables contained. We had the asset owners verify the results, and the next phase was the test phase where we collected primary data sample test cases such as email IDs or employee IDs to conduct test DSRs on the process. When scanning results were correct and accurate, we closed them out.

BigID should focus more on the unstructured data part because many organizations have significant amounts of unexplored unstructured data containing large quantities of PII that they are not even aware of. If BigID could scan unstructured data in a seamless way as they do with structured data, it would be very useful for companies to discover the PII data hiding within unstructured data.

The best features BigID offers are data discovery and DSR, along with the policies they provide, including custom policies that we can create to ensure scans and results are accurate.

The custom policies were based on regulations such as GDPR, CCPA, CPRA, and India's LVDPA. Some policies we created were custom due to business requirements and information that was considered PII based on the company's specific context. In those cases, we created special custom policies so that during scans they capture information as PII. BigID also gave us the advantage to create custom policies for the scan process.

The challenges with BigID were not primarily from BigID itself but rather from the database side, where scans sometimes failed due to issues originating from the database rather than BigID. BigID's user interface was problematic as it was not very user-friendly, though I believe it improved over time. Apart from that, BigID deserves recognition for the data discovery part, which has been wonderful and quite accurate, along with the confidence level process that allows us to fine-tune results for better accuracy from the database.

The user interfaces can be improved, and not only for DSR or BigID scans but also for other aspects of data privacy. BigID can also improve its focus on unstructured data and make certain enhancements in that area.

I have been using BigID for around two and a half years.

The initial setup and configuration of BigID for my client was seamless because folks from BigID helped us set up the configurations.

I would prefer to work on more features that BigID offers, which I have not yet explored.

Although I was serving the client rather than my own organization, BigID has made scans faster and more efficient, and the DSR results are much more accurate. We can obtain results in a very short amount of time. The manual DSR process would take days and maybe weeks, but with DSR automation, results come in minutes or hours. This made a significant improvement and saved considerable time for my client. I have mentioned everything I am aware of regarding areas where improvements are needed. There may be other cases, but I am not aware of them.

Since the data sources were mostly on-premise and structured, the integration of BigID with other tools or platforms my client uses is smooth.

I would rate this product a nine out of ten.

My main use case for BigID  includes data discovery and classification, DSAR automation, and data remediation.

I use BigID  for data discovery and classification in my work by identifying where personal information lies within organizations, which often don't know where their personal information is stored. Organizations may be using or storing client data or any data of their users. When we do data discovery and classification, we get an actual picture of where there's personal data, and sometimes we find places where personal data shouldn't be, raising compliance issues. Regulatory compliance can be costly for organizations, and doing data discovery and classification helps us identify where most of this personal data is located.

In addition to data discovery and classification, I can also use BigID for data DSAR automation. Whenever somebody requests their data to be deleted, it's a quicker process than manually retrieving all the information about where their personal data lies, allowing DSAR automation requests to be fulfilled within 72 hours or so.

The best feature that BigID offers is data discovery and classification, which is the most powerful engine. It allows connecting to many different data sources, ranging from cloud to on-premises to structured to unstructured data. If there is no connector available, you can build your own classifiers as well.

Regarding the custom classifier option, you can build custom classifiers using regular expressions, and I have done that if you know how to create regular expressions. Custom connectors are something you create to connect to a database where the connector is not available.

BigID has positively impacted my organization as it's a very powerful tool, especially with the increasing regulatory compliances for different countries such as GDPR, CCPA, and India's recent DPDPA act. Having these tools in place greatly helps organizations avoid any penal charges for not being compliant with the regulatory compliances.

For example, regarding compliance or reduced risks for my clients, the DSAR process I was talking about allows organizations to respond quickly to user data deletion requests under GDPR law, which traditionally has a 30-day or 60-day timeline. In larger organizations, when the number of requests is high, it becomes tedious. However, using DSAR automation with BigID, it's almost instantaneous; instead of 30 days, you can respond in just one day to what users have requested.

One area where BigID can be improved is the UI, which has a lot of bugs. I believe that if they improve their UI experience, that would greatly help.

In general, sometimes you see bugs, but there's nothing specific that I can point out right now regarding needed improvements.

I have been using BigID for almost five years.

BigID is indeed stable.

BigID is scalable, allowing you to purchase as many scanners as you want.

The customer support for BigID includes L1, L2, and L3 support, which is quite responsive.

I would rate the customer support a six because you cannot directly reach out to L3 or L2 support if there's a major issue. Their standard procedure requires going through L1 first, which can be time-consuming.

I have used other tools such as OneTrust and Security.ai for other organizations, but I haven't seen organizations switch from one tool to another before using BigID.

I have seen a return on investment from using BigID, particularly as it is a regulatory and compliance tool that helps avoid potential penalties for non-compliance. You can save time when it comes to finding where your data lies or automating your DSAR and RoPA automation, among other things. When measuring the amount of penalty that could have been incurred, it was avoided.

My experience with pricing, setup cost, and licensing is limited since, as a consultant, I do not really engage in these discussions. These details are mostly handled by the vendor and the client side, while we focus on the implementation.

Before choosing BigID, I did evaluate other options, specifically OneTrust and Security.ai, but based on the use case of data discovery and classification, we determined that BigID was the best option for us.

I don't have anything else to add about the features. I don't have any specific advice that I can think of for others looking into using BigID. My overall rating for this review is eight.

Data discovery is the strongest use case for BigID .

Data classification is highly effective due to its automatic capabilities. One of the best features of BigID  is its strength in data discovery and governance.

I want them to focus on data mapping, assessment, automation workflow, and privacy incident management. The privacy tools have not been widely used, and they have not invested much in privacy code privacy tools.

I have used the solution for five years.

I find it really stable.

BigID is scalable, so the size of the environment can be adjusted based on my organization's requirements.

BigID has one of the best technical support teams.

A lot of customers have moved to OneTrust since they have not given enough time to BigID.

The initial setup is not complex; it is pretty straightforward. Once connected, it becomes easier to connect to other data sources.

BigID is really good in terms of getting out the ROIs. It is one of the best tools in the market.

The pricing is competitive in the market, however, I need to ask for the right price.

I would rate BigID at eight out of ten. I would advise users to go with a small proof of concept, connect to their on-prem or cloud instances, and then scale over time.

BigID is part of our data governance project. We onboard data sources in our environment and scan for specific sensitive data sets, such as SSN numbers, passport numbers, and medical information, while working with a US client.

BigID provides a large number of predefined classifiers. For example, when scanning SSN numbers, there are around four to five different classifiers available. We can choose based on our use case, such as SSN near term, where the term 'SSN' or 'Social Security Number' ahead or behind the number can trigger a match. It offers multiple options like narrow and wide SSN. Since BigID is on Cloud, we do not need to manually upgrade anything; any feature upgrades from their side are automatically reflected.

BigID does not currently support Kerberos authentication for DataStax. On the user interface, analyzing true and false positives can be challenging, as I can only view 2,000 characters at a time. When tagging scan results, all objects get tagged without the option to untag specific ones. This requires reverting changes through MongoDB, which can result in data loss.

I have been using BigID for less than one year.

There are no significant bugs. There are issues with incident tagging where all objects are tagged instead of a selected number of scan results. If modifications are incorrect, they can only be reverted through MongoDB, which may cause data loss.

We have a professional service contract with BigID and meet with them weekly. Direct support from the people we work with is satisfactory, but interaction with the general support team can be delayed. Overall, I rate this support around six or seven.

I was not present during deployment, and BigID manages Cloud deployment. Setting up the scanner for on-premises data sources like SQL or file shares requires configuring a Linux machine, which can occasionally encounter errors.

BigID may be expensive, with various services and applications that require separate payments. Some features and applications such as Data Retention and Subject Rights Management also have additional costs.

BigID offers many capabilities and features. Operating on Cloud, it scans data repositories without affecting end-user systems, making it more of an IT-side operation. I rate the overall solution as nine out of ten. The tagging and support process have room for improvement, and efficiency is necessary for support once the professional service contract ends.