What is the scientific process of transforming data into insight for making better decisions

From today’s smart home applications to autonomous vehicles of the future, the efficiency of automated decision-making is becoming widely embraced. Sci-fi concepts such as “machine learning” and “artificial intelligence” have been realized; however, it is important to understand that these terms are not interchangeable but evolve in complexity and knowledge to drive better decisions.

Distinguishing Between Machine Learning, Deep Learning and Artificial Intelligence

Put simply, analytics is the scientific process of transforming data into insight for making better decisions. Within the world of cybersecurity, this definition can be expanded to mean the collection and interpretation of security event data from multiple sources, and in different formats for identifying threat characteristics.

Simple explanations for each are as follows:

• Machine Learning: Automated analytics that learn over time, recognizing patterns in data.  Key for cybersecurity because of the volume and velocity of Big Data.
• Deep Learning: Uses many layers of input and output nodes (similar to brain neurons), with the ability to learn.  Typically makes use of the automation of Machine Learning.
• Artificial Intelligence: The most complex and intelligent analytical technology, as a self-learning system applying complex algorithms which mimic human-brain processes such as anticipation, decision making, reasoning, and problem solving.

Benefits of Analytics within Cybersecurity

Big Data, the term coined in October 1997, is ubiquitous in cybersecurity as the volume, velocity and veracity of threats continue to explode. Security teams are overwhelmed by the immense volume of intelligence they must sift through to protect their environments from cyber threats. Analytics expand the capabilities of humans by sifting through enormous quantities of data and presenting it as actionable intelligence.

While the technologies must be used strategically and can be applied differently depending upon the problem at hand, here are some scenarios where human-machine teaming of analysts and analytic technologies can make all the difference:

• Identify hidden malware with Machine Learning: Machine Learning algorithms recognize patterns far more quickly than your average human. This pattern recognition can detect behaviors that cause security breaches, whether known or unknown, periodically “learning” to become smarter. Machine Learning can be descriptive, diagnostic, predictive, or prescriptive in its analytic assessments, but typically is diagnostic and/or predictive in nature.
• Defend against new threats with Deep Learning: Complex and multi-dimensional, Deep Learning reflects similar multi-faceted security behaviors in its actual algorithms; if the situation is complex, the algorithm is likely to be complex. It can detect, protect, and correct old or new threats by learning what is reasonable within any environment and identifying outliers and unique relationships.  Deep Learning can be descriptive, diagnostic, predictive, and prescriptive as well.
• Anticipate threats with Artificial Intelligence: Artificial Intelligence uses reason and logic to understand its ecosystem. Like a human brain, AI considers value judgements and outcomes in determining good or bad, right or wrong.  It utilizes a number of complex analytics, including Deep Learning and Natural Language Processing (NLP). While Machine Learning and Deep Learning can span descriptive to prescriptive analytics, AI is extremely good at the more mature analytics of predictive and prescriptive.

With any security solution, therefore, it is important to identify the use case and ask “what problem are you trying to solve” to select Machine Learning, Deep Learning, or Artificial Intelligence analytics.  In fact, sometimes a combination of these approaches is required, like many McAfee products including McAfee Investigator.  Human-machine teaming as well as a layered approach to security can further help to detect, protect, and correct the most simple or complex of breaches, providing a complete solution for customers’ needs.

Think about the number of business decisions you face daily. When you consider the options, are you using data, facts, and the best possible evidence available to you to inform the decision-making process?

Traditional business decision-making may involve reviewing information from multiple sources to figure out a path forward. If you need additional information from any of the reports, you need to request a new report to be generated. That adds up to a process that’s neither efficient nor insightful.

It was much more difficult to find the facts and data you needed to make a decision in the past. Today, technology brings a wealth of data that can be collected, shared, organized, and analyzed in ways never before imaginable.

Enter fact-based decision-making, your most useful tool for delivering better business performance. Companies that prioritize the use of fact-based data analytics boast a revenue growth that’s more than double the rate of companies that don’t have use data analytics, and they grow employment at a rate that’s four times faster than their less digitally-driven peers, according to a report from the National Center for the Middle Market.

Why does fact-based decision-making work so well? We looked at the reasons why this strategy is so necessary right now to drive business performance.

Analytics and fact-based decision-making

Analytics is defined as “the scientific process of transforming data into insights for the purpose of making better decisions,” by INFORMS, a professional association dedicated to best practices and advances in operations research, management science, and analytics.

What that means: In the same vein as the scientific method, analytics follows a predetermined pattern. Digital technology analyzes data and turns it into insights that can be used for better decisions. Using analytics doesn’t take emotion or passion out of business. It is no substitute for strong leadership, but it adds a powerful layer of awareness that can lead to better decision-making.

Analytics are best used to achieve a strategic goal – which is why many organizations invest their resources in software that offers data analytics capabilities to achieve business growth and solve key issues in the company.

The end of bias

The days of “going with your gut” or relying on the opinions of a few select stakeholders are long gone. With the help of data analytics software, you can find non-obvious answers and remove bias. When we use the facts at our disposal, there’s no need for arguments or disputes over numbers. The unbiased nature of the data allows you to make drama-free decisions that benefit departments, and the company as a whole.

For example, sales departments looking to increase sales can use study data, including real-time product, pricing, and customer details. Or the finance team can examine their expenses data to identify where spending is over budget, which enables them to make informed decisions about cutting certain costs.

Customer service teams can use data to assess Key Performance Indicators (KPIs), allowing them to improve forecasting and determine which customer service methods are working and which are not.

Efficient, repeatable analysis

Modern data analytics software can crunch large datasets very quickly, taking into account hundreds of market variables. The automated nature of the software enables it to adopt a methodical, repeatable process for analysis. That takes out the guesswork, and the long hours the finance department used to spend manually inputting all of these figures into a spreadsheet.

There’s simply no substitute for a system that can quickly pull together relevant data and generate analyses.

Clear and accurate insights

Some decision-makers may doubt the ability of data analytics software to consistently produce accurate, actionable insights that lead to better decision-making. Give it time. As the outcomes of the decisions become more reliable and predictable, managers become more and more comfortable with fact-based decision support systems. Over time, the strategic long-term benefits and ROI will appear.

According to the Center for the Middle Market report, 85% of middle market companies deploy analytics to some degree in their decision-making process. To remain competitive in this market, it’s essential to use data-driven insights to give you a complete view of your business.

Fact-based decision-making can revolutionize the way your company operates. When you have the data and the analytics driving your insights, your decision-makers can make better decisions that lead to business growth. To learn more about how to transform your business and carry-out  fact-based decision-making, download this free e-book here.