Data Drives Business

Data will drive business as long as it is the right data. Just having a lot of data does not guarantee success. Along with having the right data there is also the need to mine the data. Companies use data mining software to learn more about their customers, products and/or services. It can help them to develop more effective marketing strategies, increase sales, and decrease costs. Data mining relies on effective data collection, warehousing, and computer processing.

How Data Mining Works

Data mining involves exploring and analyzing large blocks of information to glean meaningful patterns and trends. It is used in credit risk management, fraud detection, and spam filtering. It also is a market research tool that helps reveal the sentiment or opinions of a given group of people. The data mining process breaks down into four steps:

1. Data is collected and loaded into data warehouses on site or on a cloud service.
2. Business analysts, management teams, and information technology professionals access the data and determine how they want to organize it.
3. Custom application software sorts and organizes the data.
4. The end user presents the data in an easy-to-share format, such as a graph or table.

Data Mining Techniques

Data mining uses algorithms and various other techniques to convert large collections of data into useful output. The most popular types of data mining techniques include association rules, classification, clustering, decision trees, K-Nearest Neighbor, neural networks, and predictive analysis.

• Association rules, also referred to as market basket analysis, search for relationships between variables. This relationship in itself creates additional value within the data set as it strives to link pieces of data. For example, association rules would search a company's sales history to see which products are most commonly purchased together; with this information, stores can plan, promote, and forecast.
• Classification uses predefined classes to assign to objects. These classes describe the characteristics of items or represent what the data points have in common with each other. This data mining technique allows the underlying data to be more neatly categorized and summarized across similar features or product lines.
• Clustering is similar to classification. However, clustering identifies similarities between objects, then groups those items based on what makes them different from other items. While classification may result in groups such as "shampoo," "conditioner," "soap," and "toothpaste," clustering may identify groups such as "hair care" and "dental health."
• Decision trees are used to classify or predict an outcome based on a set list of criteria or decisions. A decision tree is used to ask for the input of a series of cascading questions that sort the dataset based on the responses given. Sometimes depicted as a tree-like visual, a decision tree allows for specific direction and user input when drilling deeper into the data.
• K-Nearest Neighbor (KNN) is an algorithm that classifies data based on its proximity to other data. The basis for KNN is rooted in the assumption that data points that are close to each other are more similar to each other than other bits of data. This non-parametric, supervised technique is used to predict the features of a group based on individual data points.
• Neural networks process data through the use of nodes. These nodes are comprised of inputs, weights, and an output. Data is mapped through supervised learning, similar to how the human brain is interconnected. This model can be programmed to give threshold values to determine a model's accuracy.
• Predictive analysis strives to leverage historical information to build graphical or mathematical models to forecast future outcomes. Overlapping with regression analysis, this technique aims to support an unknown figure in the future based on current data on hand.

The Data Mining Process

To be most effective, data analysts generally follow a certain flow of tasks along the data mining process. Without this structure, an analyst may encounter an issue in the middle of their analysis that could have easily been prevented had they prepared for it earlier. The data mining process is usually broken into the following steps.

Modern businesses have the ability to gather information on their customers, products, manufacturing lines, employees, and storefronts. There is also a massive amount of information available on topics such as weather, traffic (both vehicle and pedestrian), holidays and special events. These random pieces of information may not tell a story, but the use of data mining techniques, applications, and tools helps piece together information into useful patterns that can enhance decision and strategies.

The ultimate goal of the data mining process is to compile data, analyze the results, and execute operational strategies based on data mining results.

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What is data security?

Data security is the practice of protecting digital information from unauthorized access, corruption or theft throughout its entire lifecycle.

This concept encompasses the entire spectrum of information security. It includes the physical security of hardware and storage devices, along with administrative and access controls. It also covers the logical security of software applications and organizational policies and procedures.

When properly implemented, robust data security strategies protect an organization’s information assets against cybercriminal activities. They also guard against insider threats and human error, which remain among the leading causes of data breaches today.

Data security involves deploying tools and technologies that enhance the organization’s visibility into the location of its critical data and its usage. Ideally, these tools should be able to apply protections such as encryption, data masking and redaction of sensitive files, and should automate reporting to streamline audits and adhering to regulatory requirements.

Business challenges

Digital transformation is profoundly altering how businesses operate and compete today. Enterprises are creating, manipulating and storing an ever-increasing amount of data, driving a greater need for data governance. Computing environments have also become more complex, routinely spanning the public cloud, the enterprise data center and numerous edge devices such as Internet of Things (IoT) sensors, robots and remote servers. This complexity increases the risk of cyberattacks, making it harder to monitor and secure these systems.

At the same time, consumer awareness of the importance of data privacy is on the rise. Public demand for data protection initiatives has led to the enactment of multiple new privacy regulations, including Europe’s General Data Protection Regulation (GDPR) and the California Consumer Protection Act (CCPA). These rules join longstanding data security laws such as the Health Insurance Portability and Accountability Act (HIPAA), protecting electronic health records, and the Sarbanes-Oxley Act (SOX), protecting public company shareholders from accounting errors and financial fraud. Maximum fines in the millions of dollars magnify the need for data compliance; every enterprise has a strong financial incentive to ensure it maintains compliance.

The business value of data has never been greater than it is today. The loss of trade secrets or intellectual property (IP) can impact future innovations and profitability, so trustworthiness is increasingly important to consumers.

Read more on data security

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Types of data security

To enable the confidentiality, integrity and availability of sensitive information, organizations can implement the following data security measures:

• Encryption
• Data erasure
• Data masking
• Data resiliency

Encryption

By using an algorithm to transform normal text characters into an unreadable format, encryption keys scramble data so that only authorized users can read it. File and database encryption software serve as a final line of defense for sensitive volumes by obscuring their contents through encryption or tokenization. Most encryption tools also include security key management capabilities.

Data erasure

Data erasure uses software to completely overwrite data on any storage device, making it more secure than standard data wiping. It verifies that the data is unrecoverable.

Data masking

By masking data, organizations can allow teams to develop applications or train people that use real data. It masks personally identifiable information (PII) where necessary so that development can occur in environments that are compliant.

Data resiliency

Resiliency depends on how well an organization endures or recovers from any type of failure—from hardware problems to power shortages and other events that affect data availability. Speed of recovery is critical to minimize impact.

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Data security strategies

A comprehensive data security strategy incorporates people, processes and technologies. Establishing appropriate controls and policies is as much a question of organizational culture as it is of deploying the right tool set. This means making information security a priority across all areas of the enterprise.

Consider the following facets in your data security strategy:

• Physical security of servers and user devices
• Access management and controls
• Application security and patching
• Backups
• Employee education
• Network and endpoint security monitoring and controls

Physical security of servers and user devices

You might store your data on premises, in a corporate data center or in the public cloud. Regardless, you need to secure your facilities against intruders and have adequate fire suppression measures and climate controls in place. A cloud provider assumes responsibility for these protective measures on your behalf.

Access management and controls

Follow the principle of “least-privilege access” throughout your entire IT environment. This means granting database, network and administrative account access to as few people as possible, and only to individuals who absolutely need it to get their jobs done.

Application security and patching

Update all software to the latest version as soon as possible after patches or the release of new versions.

Backups

Maintaining usable, thoroughly tested backup copies of all critical data is a core component of any robust data security strategy. In addition, all backups should be subject to the same physical and logical security controls that govern access to the primary databases and core systems.

Employee education

Transform your employees into “human firewalls”. Teaching them the importance of good security practices and password hygiene and training them to recognize social engineering attacks can be vital in safeguarding your data.

Network and endpoint security monitoring and controls

Implementing a comprehensive suite of threat management, detection and response tools in both your on-premises and cloud environments can lower risks and reduce the chance of a breach.

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Data security trends

In the changing landscape of data security, new developments such as AI, multicloud security and quantum computing are influencing protection strategies, aiming to improve defense against threats.

AI

AI amplifies the ability of a data security system because it can process large amounts of data. Cognitive computing, a subset of AI, runs the same tasks as other AI systems but it does so by simulating human thought processes. In data security, this simulation allows for rapid decision-making in times of critical need.

Multicloud security

The definition of data security has expanded as cloud capabilities grow. Now, organizations need more complex tools as they seek protection for not only data, but also applications and proprietary business processes that run across public and private clouds.

Quantum

A revolutionary technology, quantum promises to upend many traditional technologies exponentially. Encryption algorithms will become much more faceted, increasingly complex and much more secure.