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What tools or solutions would you recommend to improve data strategy and governance?

When combined with internal data, using technology platforms to integrate open source data will send signals that will allow us to discover trends, insights, and anomalies. The platform, quality of data and user experience all shape how much value is derived. Data fabric, natural language interrogation, machine learning operations (MlOps), DevOps, cross cloud interrogation, containerization, no-code/low-code tools, and visualization tools should be woven into the overall data strategy & governance plan. Data affords us a high level of confidence in the decisions we make, so it’s essential to figure out how you can get more data to help you plan. Before the Russia-Ukraine war started, open source data was used by regular civilians to detect troops mobilizing to the border through Google Maps imagery. UAVs and drones can be leveraged for this imagery. This data can even be the input into VR/AR, which can be incorporated at large for combat, but also for use cases such as forklift training.

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Anonymous Author
When combined with internal data, using technology platforms to integrate open source data will send signals that will allow us to discover trends, insights, and anomalies. The platform, quality of data and user experience all shape how much value is derived. Data fabric, natural language interrogation, machine learning operations (MlOps), DevOps, cross cloud interrogation, containerization, no-code/low-code tools, and visualization tools should be woven into the overall data strategy & governance plan. Data affords us a high level of confidence in the decisions we make, so it’s essential to figure out how you can get more data to help you plan. Before the Russia-Ukraine war started, open source data was used by regular civilians to detect troops mobilizing to the border through Google Maps imagery. UAVs and drones can be leveraged for this imagery. This data can even be the input into VR/AR, which can be incorporated at large for combat, but also for use cases such as forklift training.
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Anonymous Author
If you are a Microsoft shop, then you might want to look into Microsoft Information Protection.  Otherwise, Code42 is also a good tool. 
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Anonymous Author
Data governance is the most concern of Infosec professionals. That scenario is changing dramatically. A data governance strategy is imperative. With good governance, enterprise data can be more consistent even across divisional boundaries, more accessible and easier to use, enabling better informed business decisions. There are lots of tools available in market but below are the few best practices to improve data strategy and governance. 1. Measure an effectiveness of your data governance program 2. Create a virtual compliance team with the data practitioners 3. Secure your data close to the source. 4. do not take data privacy on trust. 5. Commit to openness, awareness, communication and training 6. Review your data governance policy regularly.
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Anonymous Author
My Take is very simple on this: 1. Identify and catalogue your data well. 2. Have effective storage which cuts the need to locally copy the data when it is accessed by different organizational units 3. Once the storage and sharing is figured out, have policies in place for handling of the data. the policies must explain what is allowed during any of the data operation. 4. Perform audits to verify compliance.
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Anonymous Author
I think it starts with understanding what you are trying to protect.  Start with establishing a data inventory.  Understanding what is collected and stored is needed to justify what you are collecting and how long you keep specific elements of data for.  Once you have and understanding of what you are trying to protect then you can look at access control, monitoring, encyrption etc, based on the business needs to protect the data.
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Anonymous Author
I would recommend IBM data strategy and governance solution/tools.
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Anonymous Author
One of the problems we have not yet been able to figure out is the impact of quantity on the sensitivity of data.  As an example; if you would share the price of a product with a customer, then that is absolutely fine.  If you share a price-list then that data may be more sensitive, and if you share the complete pricing structure that may be super-sensitive.   Mechanized solutions (DLP) have problems catching those nuances, which is where for now user education still seems to be the best line of defense.
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