How Big Data Analysis Helps Compliance & Business Leads Make Better Decisions

Contemporary technology is increasingly trying to solve the challenges associated with the business information. It is, therefore, essential to interrogate the role that big data analysis plays in compliance efforts. Cybersecurity compliance increasingly demands businesses to weigh the impact of a wide range of data streams and inputs. To that effect, big data analytics provide the necessary compliance insights to help entities restructure its efforts and enhance profitability.Big data companies make sure that data is stored safely and helps other industries determine problems and resolve it immediately.

Employing Big Data to Steer your Business and Compliance Program

How do we Define Big Data?

The term ‘big data’ is increasingly popular all over the web but what does it even mean? Mostly, big data refers to significant amounts of data sets that systems analysis to uncover trends, patterns, and associations. However, that is not all there is regarding big data.

The term also encapsulates the 3 Vs.’ of Volume, Variety, and Velocity. Volume is not only about the amount of data, but also the number of data collection points. For example, the data may originate from the point of sales, social media and market research. The variety, in this case, is about the format of the data collected. Mainly, it can be text-based, visual, structured or even unstructured. Velocity refers to the speed at which data collection occurs to ensure a timely analysis. For example, RFID tags gather information in real-time so that you are up to date with all that is happening, as it happens.

The Variability Challenge

The real challenge with big-data analysis is that information is not constant. It can change in the process of review. While the term variability can include variety, it is a little different. Variety, in this case, refers to varying data formats collected for analysis such us audio and text. On the other hand, variability is all about inconsistencies arising out of varying data sources and types.

Let’s take the data point ‘ocean’ for illustration purposes. The text collected can include saltwater, ocean, Pacific, Waves, Atlantic, and image. The text ‘wave’ has a variety of meanings. It can mean a hand motion in greeting or movement of water inshore. When aggregating the ocean data, the hand gesture may be an outlier. And while the images for ‘waves’ would provide the same results, there may still be some differences, complex differences.

Such variability in data sets makes aggregating it to find trends and patterns a challenge. The higher the differences, the higher the intricacy in the analysis. Images provide even more complex data than the text. All this variability in data sets, therefore, make analysis a real problem.

Structured and Unstructured Data

The central problem with data analysis lies in the differences between structured and unstructured data.

Structured data is often tabulated for easy collation and processing. For example, you can easily manipulate the spreadsheet for a variety of information.

Unfortunately, the majority of the data collected is not in tables. It is unstructured. The data include images, text and even binary programming which make it difficult to organize the information in a numerical format.

Sometimes, the collected data is a blend of structured and unstructured data. Emails contain structured data such as ‘to’ and ‘from,’ but you cannot say the same for images. In other words, you can transform the text data into tables, but you cannot do the same for images.

How do you Analyze Big Data?

Businesses are continuously evolving and so is the attacker methodologies. The more the data collection points, the more the attack vectors. And while big data provides the necessary information, analytics provides the insights needed to counter any threats and make the right decisions.

Predictive and Prescriptive Analytics

Predictive analytics utilizes machine learning, modeling, as well as data mining to work on historical data and foretell future events. The methodologies make data collection effective and in real-time. For example, an antivirus using cutting-edge technology employs big data and machine learning to not only protect your system but also predict the next ransomware and enhance protection.

Prescriptive analysis, on the other hand, involves using big data to formulate the best decision. Instead of making assumptions as to what may happen to the business, prescriptive analysis helps you take actions to safeguard the entity. For example, big data can collect data regarding attempted intrusions, but prescriptive analytics modeling helps decide which events to prioritize.

How to Employ Big Data and Machine Learning Analytics for Information Security

The statistical methodologies make the collected data useful. Primarily, the predictive and prescriptive analytics helps safeguard the information environment and determine the control efficiency.

The algorithms used by machine learning gather information from the internet to enhance threat detection. Mostly, big data collect millions upon millions of monitored environments and compares it against one another to highlight patterns in the system and network activities for enhanced data protection.

How Machine Learning and Big Data Help with Compliance and Decision Making

Enabling strong security in your business aids your compliance efforts. Mostly, it is possible to focus your program to be consistent across platforms. Ensuring control effectiveness also helps achieve compliance goals. For example, a compliance directive may require patching with the latest software updates. If your monitoring analytics underscore weaknesses in software updates, then compliance is not a problem.

Author Bio

Ken Lynch is an enterprise software startup veteran, who has always been fascinated about what drives workers to work and how to make work more engaging. Ken founded Reciprocity to pursue just that. He has propelled Reciprocity’s success with this mission-based goal of engaging employees with the governance, risk, and compliance goals of their company in order to create more socially minded corporate citizens. Ken earned his BS in Computer Science and Electrical Engineering from MIT.  Learn more at



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