Your enterprise is facing a data management crisis. How can you ensure your data is secure and accessible?

In a data management crisis, a comprehensive strategy is key. Implement strong data governance policies and conduct regular audits. Use data encryption and secure access controls for protection. Adopt a tiered data storage strategy to optimize costs and performance based on data usage. Ensure regular backups and have a disaster recovery plan to minimize disruption during data loss incidents. Remember, data management strategies need continuous effort and adaptation to evolving business needs and threats.

In the most generalised scheme of things, Often Data acquisition is considered more important than anything else which can lead to inaccurate results which then leads to quality of data as the root cause. Even within highly matured enteprise landscape, it is very important to consider the right ‘bounded context’ for the data being acquired. Example the entity say ‘customer’ coming from different domains such as digital vs loyalty could make the meaning of the same data different. Most often, it is not that the data itself has quality issues but the issue is how the data is perceived, understood and used for decision making where data coming from different bounded context is considered the same.

I would address this in three broad areas 1/define data governance and quality by setting the right ownership articulating roles, data validations, and standards. Less high quality data > vast amounts of stale data 2/ regulatory and sovereign needs are playing a critical role in data management. Knowing the basics and seeking legal counsel before designing your data management solution is a must 3/ Applying technology to manage data at scale is the easy part IMO with a plethora of options available to secure data (encryption, monitoring), access data (IdP and authorization), retain data (back up tech), reduce data latency (streaming , ETL, integrators) , aggregate data (warehouses, lakes) or drive intelligence (dashboarding/ML models).

1. Continuously Identify and Classify All Data: Start by identifying critical and sensitive data that needs preservation during a crisis (e.g., financial records, employee data, customer information, intellectual property). 2. Keep Data Accessible but Secure 3. Consistently Validate Data Quality 4. Have a Data-Recovery Plan in Place 5. Have a Communication Plan in Place 6. Consider How to Dispose of Data 7. Regularly Audit to Ensure Regulatory Compliance Review data management policies periodically. 8. Ensure compliance with relevant regulations (e.g., GDPR, HIPAA). 9. Don’t Forget Training and Awareness

Data Quality is the first and - from an outcome and impact perspective - the most critical aspect. Having systems, processes and tools that allow for: 1. Data to be collected directly from source (example consumer data from the consumer themselves), 2. Validate the data collected, 3. Clean and complete it before it hits the database 4. Quality and accuracy check the reports/visualisations and insights generated post processing and analyses 5. Ensuring privacy, security and integrity of data is important

External risks pertain to hacking into the system, by introduction of viruses and worms etc. Internal threats are unauthorised access of data by employees, or taking data out. In today's world, where remote working is a reality, access management is a critical aspect of data security. A proper data management and security strategy needs to be in place, use of ethical hackers to identify possible vulnerabilities in the network, firewall etc help review, modify and upgrade the data security.

Classic data security worries about assessing the risk when (not if) data gets exposed. In the new world of GenAI we also need to worry about new inferences - data sets being blended that weren't previously available in the same domain

To safeguard against data security risks, enterprises must implement robust cybersecurity measures. involves adopting encryption technologies, access controls, and secure authentication protocols to protect sensitive information from unauthorized access or breaches. Regular security audits and vulnerability assessments should be conducted to identify and address potential weaknesses in the data infrastructure. Employee training on cybersecurity awareness is crucial, emphasizing the importance of data security practices and the potential consequences of security breaches. Collaboration with experts and staying abreast of evolving threats ensures that the enterprise's data remains secure, fostering a resilient data management environment.

In most security assessments, the greatest risk is from employees. At the same time employees are the company’s greatest assets. They cannot be treated like risks or threats. Security measures often create much more difficulty during normal use then the value of their protection. Working from home has created a significant problem, because employees use personal devices and computing for company jobs. So the best approach is hiring trustworthy people, and providing training to them on how to protect and respect the value of company data. most companies punish violations of data protection rules. We rarely see a company that rewards employees who continuously protect and respect company data.

1. Assume your data has already been hacked. 2. If encrypted it will take a couple years for Quantum computing to decrypt your AES 256 level encryption. Otherwise, it's already in the hands of your competition in China, Russia and elsewhere. 3. Beyond data breaches, it seems all the comments miss out on RECOVERY. Immutable Snapshots, at least daily, with a Malware Engine that's constantly validating that the data in your file system is the data written by your company and not a bad agent. With these safe copies, the Company can at least have starting point to restart the business in case of a Malware attack.

There are advantages to choosing and standardizing on a single platform for warehousing, data lake, data sharing, data engineering, and data application development. The one platform can help avoid or mitigate many of the data challenges outlined here in this article. Additionally, the important tools leveraged in making use of your data; this approach can help with things such as ETL; by standardizing on these tools e.g. DBT; this enables you to better establish practices and manage policy.

Start by conducting thorough assessment of current data management practices. Identify areas of weakness, like inadequate data governance, inconsistent data quality, or outdated security measures. Then implement granular data access controls to ensure only authorized users have access to sensitive data. Role-based access control (RBAC), attribute-based access control (ABAC), and data masking techniques to help enforce access policies & prevent unauthorized data exposure. Implement data lifecycle management strategies to effectively manage data from creation to archival or deletion taking into account regulatory requirements, business needs, and data usage patterns.

Data loss prevention should be considered in addition to the data latency aspect discussed. Doing so efficiently can help prevent or mitigate some data access delays "due to network or system issues." It can also protect from a more severe impact due to (otherwise undetected) data loss. As far as data availability (defined as the timeliness and reliability of access to and use of data) is concerned, the fewer systems are used, the easier it can be established. This calls for the consolidation of independent systems (which can fail independently) and standardization, as others mentioned.

It helps to make sure that the accounts/people with direct access to the data in question are those that need to actually use the data. A properly implemented ACL can help prevent a critical issue from ever happening.

Democratisation through access can be an organisational blessing and a curse. It is a blessing in that it can break down silos. A curse in that it can promote the cottage industries.

Spot on! Business intelligence software has become an indispensable asset for organizations looking to leverage their data effectively. It's incredible how these tools streamline data management processes, enhance data quality, and fortify data security measures. With the ability to seamlessly integrate, visualize, and analyze data, businesses can unlock valuable insights that drive informed decision-making and ultimately propel growth. Investing in robust business intelligence software is no longer just an option but a necessity in today's data-driven landscape.

The primary objective is to be empowered with information that allows you to make informed business decisions. To monitor trends and to capaitalize on them. In order to do this careful consideration must be applied when considering the quality of the data, i.e. is it correct and so if it was used as part of a model of a situation ensure that model is accurate. Its helpful to start small and and ask questions of your own business and build on the gathering of data points and then bring them in purposefully to support other data. The most successful implementations of business intelligence are normally bespoke, no business is the same, they may be similar, use data to find an edge.

Deploying business intelligence (BI) software is instrumental in managing and extracting value from enterprise data. BI tools provide a centralized platform for data visualization, analysis, and reporting, enabling stakeholders to make informed decisions based on real-time insights. These tools often include features for data cleansing and transformation, addressing data quality concerns. Additionally, BI platforms incorporate role-based access controls, ensuring that sensitive data is accessible only to authorized users. By utilizing BI software, enterprises can enhance data accessibility, empower users with self-service analytics, and leverage data-driven intelligence to navigate the complexities of the modern business landscape.

A data management crisis can occur when an organization struggles to effectively collect, store, process, and utilize its data, leading to inefficiencies, inaccuracies, or missed opportunities. Business intelligence (BI) can play a crucial role in addressing and mitigating data management crises by providing tools and processes to extract actionable insights from data.

Business intelligence software can help if the governance of the wider data lake is pragmatic and practical. It can help with accessibility and security. From a quality perspective it remains garbage in, garbage out unless it is focussed appropriately. This extends to areas like establishing standard definitions for sources, schemas, metrics and measures.

Fostering a culture of security awareness can significantly bolster your data security posture & mitigate risks of cyberattacks. Data security is a shared responsibility, requiring commitment from all stakeholders within your organization. 1. Third-party vendor management 2. Incident response plan 3. Stay informed 4. Seek professional help Consider working with cybersecurity professionals to assess your risks, develop a comprehensive security strategy, and implement best practices. 5. Continuous improvement

Ensuring capture of data at source, ie where it is created, is important as it the most authentic data. Delayed capture and entry results in errors in the data, and its dependability becomes suspect, and hence quality of decision based on the data suffers.

In addressing the challenge "How can you ensure your data is secure and accessible?" you have to be realistic and accept that you cannot 'ensure' it is secure and accessible. 'ensure' is a very emotive and binding verb which will undoubtedly cause impossible challenges in achievement, communication and trust.