Security Analytics in Age of AI: In-depth Guide [2019 update]

Every day 2.5 quintillion bytes of data are produced. Some of it is important (and private), like the most essential of financial and medical records. Some of it, like the cleaning path taken by an IoT vacuum is largely useless (or is it?). And all of it can become vulnerable without the right security effort.

Subsequently, there is a growing need for us to find new and better ways to protect our most sensitive data from a host of digital threats rise. Cybersecurity analytics can ultimately help to lay the foundation for large scale data protection.  Some questions that this post sets out to answer include:

What is cybersecurity analytics?

What are the benefits of cybersecurity analytics?

What are some common cybersecurity use cases?

How do AI and cybersecurity analytics go together?

What are some best practices for cybersecurity analytics?

What are some pitfalls/challenges in cybersecurity analytics?

What are tools to aid in effective cybersecurity analytics?

How can I start to establish a cybersecurity analytics program in my organization?

What is Cybersecurity Analytics?

Every day millions of cyber attacks are successfully executed around the world. Though it may be impossible to 100% prevent them all, we can certainly learn from them to help develop better ways to stay protected. Cybersecurity analytics studies the digital trail left behind by cyber criminals to help better understand weaknesses and how to prevent similar losses in the future.

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Workforce Analytics: Guide for Businesses [2019 update]

Employees are one of the biggest investments that an organization can make, so it’s not surprising that the field of workforce analytics has emerged to help support it. And with the amount of data that is being generated and recorded about not just employees, but the organization as a whole, the time is ripe for analysis. This is becoming even more true as our working population expands to include alternative forms of employees in chatbots and similar. 

In this post we will introduce workforce analytics by answering the following questions: Read more

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How to Choose the Right Data Quality Tool [2019 update]

Data cleaning is part of a greater effort to achieve the highest data quality possible in used in business decisions and operations. It requires organizational effort and participation throughout a business and when done correctly, can help to provide valuable insights and analytics for decision making. A few additional benefits associated with data cleaning include:

    • Streamlined business practices
    • Increased productivity
    • Faster sales cycle
    • Better analytics

Given the ever growing quantity of data for many businesses, automation is required in data cleaning. The right data tool can fill in these gaps and manage a number of issues automatically before they have a chance to become truly problematic. This can ultimately help businesses to become more efficient and more profitable in their efforts.

Choosing the right data cleaning tool for your organization is essential to getting the most utility for your investment. To help in your decision making, this post answers the following:

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34 Data Visualization Tools to Get Data Insights [2019 update]

Humans are naturally visual creatures, so it is no surprise that we search for better ways to portray and understand what data is telling us. Data visualization is one of the most effective ways to achieve this because it enables us to take data or ideas that are abstract and difficult to understand as written, and turn them into something easily understood in a more graphical or visual format. To learn more about data visualization and how organizations use it, see our blog post.

However, for any data visualization to be effective, it requires the right tool – which in many cases can mean a multifunctional tool that includes capacity for both visualization and analysis.

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