Protecting Essential Data with Security Analytics

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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Exploring Analytics & AI in 2018: A Detailed Primer

It is often said that data is the most valuable asset a business can have; the oil of a digital era. But data itself, while interesting, often leaves out a variety of important details – creating a need for analytics. And how we complete these analytics has evolved – and will continue to do so; particularly with the rapid proliferation of AI tools and technologies.

Some benefits associated with AI and analytics include:

  • Increased productivity via instant access to the right data through better classification processes
  • Improved speed, accuracy, and efficiency throughout analytics and data management practices
  • Facilitating clear compliance with legal and similar requirements
  • Cost saving by finding the best ways to achieve a wide range of tasks

To better understand data, analytics, AI, and how they all go together, we will answer the following questions: Read more

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Workforce Analytics: A 2018 Introduction for Employers & Businesses

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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Retail Analytics: A Guide for Growing Businesses [07/18 Update]

Retail analytics is a process that helps to provide crucial data for businesses with regards to inventory, sales, supply chain activities, consumer demand, and more. By implementing an analytics-driven retail environment, organizations can make better decisions for procurement, marketing, merchandising, and other operational considerations. This, in turn, enables retailers to create a better buying experience and for identifying opportunities for organizational improvement. 

Some questions that we will answer in this blog include:

What are the benefits and importance of retail analytics?

What are some current trends in retail analytics?

How can AI impact retail analytics?

What are some challenges to effective retail analytics?

What are some common tools associated with retail analytics?

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How to Choose the Right Data Quality Tool [06/2018 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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