Implement Network Segmentation

One of the first things organizations should do to protect their sensitive data is implement network segmentation. This is a flexible and highly effective strategy for keeping vulnerable information safe, even in big data environments. It can also minimize the threat of cyber incidents in worst-case scenarios.

Network segmentation involves breaking up your organization’s network into multiple chunks with layers of security isolating them from each other. They all live under the roof of one system, but movement between the segments is restricted. You can customize the safeguards for different layers, allowing you to have more open and heavily protected data groups in the same environment.

There are a few ways you can implement network segmentation. Most strategies employ a combination of physical and virtual tools. For example, firewalls are a core technology for segmenting networks. You can also use software like VLANs and network overlays, as well as identity management products like access control lists.

Segment your network based on levels of risk. Low-risk data is usually more easily accessible since users often need it regularly. In contrast, high-risk data should have finely tuned firewalls and access controls separating it from the rest of the environment. This will make it harder to access, but that shouldn’t impact the user experience since it shouldn’t be accessed frequently.

Increase Network Visibility

Visibility is crucial for protecting sensitive information. One of the most common drawbacks of a big data environment is the sheer volume to monitor. It’s easy for unusual activity or exposed data to go unnoticed. Hackers often exploit that weakness.

Increasing visibility is vital to eliminate this risk factor. In fact, poor visibility is one of the top indicators your organization might need to upgrade its computing infrastructure. Disorganized networks with poor communication often suffer from low data clarity, leaving them highly exposed.

There are several ways you can improve network organization and transparency. Automated monitoring is a great option, particularly for big data environments. Visibility will naturally be a challenge if you have a lot of information to track. Automating some monitoring tasks can reduce the workload and make effective monitoring more achievable.

Additionally, automated monitoring will significantly improve your threat detection capabilities. It’s all too easy for breaches to go unnoticed in big data environments. Time is critical for minimizing the threat of a hack, though. Automation enables you to detect suspicious activity sooner rather than later.

As a general rule, automating security features as much as safely possible will simplify things in a big data environment. You can automate repetitive tasks like flagging suspicious activity, rejecting unauthorized access, encrypting sensitive information and more. Utilizing automated security tools will make protecting vulnerable data much easier for your security team.

Another vital aspect of improving network visibility is understanding your entire computing environment. Take time to completely map out your data, users, traffic patterns and security protocols. It’s much easier to see where you’re going when you have a road map. Plus, the network mapping process often highlights existing vulnerabilities and weaknesses.

Also Read: Cybersecurity: How To Properly Protect Your Professional Email?

Make Access Control a Top Priority

Identity and access management should be part of every organization’s cybersecurity strategy, but it’s especially important for big data environments. Access control can help with network organization and visibility. It’s one of the foundational methods for keeping sensitive information safe, even in a large, dispersed system.

The principle of least privilege is a great place to start. This approach to access control only grants users the absolute minimum amount of access they need and nothing more. It often goes hand in hand with zero-trust security, which uses continuous verification to confirm user authorization.

Both of these can also factor into your network segmentation strategy. You can restrict access to entire segments and use more granular control for specific files or applications that are especially vulnerable.

With this type of data, it is usually best to create a short white list of approved users rather than a much longer black list of unauthorized ones. As with the least privilege approach, limit your safelist to only the people who absolutely need to access the sensitive data and no one else.

Physical security is also important to address. Big data environments can be fully cloud-based, all on-prem or a hybrid combination of infrastructure models. Regardless, the information is still ultimately tied to a physical server somewhere in the world. When selecting a data center provider or managing in-house servers, it’s crucial to ensure physical access control protocols are in place.

On-site server security can be automated or contracted out, much like virtual security automation. This is the case with most cloud providers and colocation data centers, which often provide in-house security services like 24/7 surveillance, advanced access control, alarm systems and more.

You may be able to have sensitive data stored on one or two isolated server racks if you want to ensure maximum on-site security for specific information. Work with your in-house IT team or data center partner to determine the best way to physically secure servers.

Conduct Tests and Audits Regularly

Testing and audits are essential components of any robust cybersecurity strategy. They’re a great way to regularly check in on the health and effectiveness of your security protocols and ensure you are adapting to new threats.

You can use these tests to verify that your sensitive data has the best security possible. During penetration testing, you can even prioritize certain information so the tester can direct their focus there.

You’ll know you most likely have strong protections if the tester can’t successfully access your organization’s sensitive data. If they do succeed, they can help you identify and eliminate vulnerabilities so real hackers can’t get through. Either way, testing is invaluable for protecting your information.

You can hire a white hat hacker to put your big data environment to the test. This is someone with hands-on experience in hacking who uses their knowledge to help security teams rather than commit cybercrime.

White hat hackers know how cybercriminals would look at a network. This unique perspective allows them to see weaknesses others wouldn’t notice. They may be able to identify vulnerabilities even a penetration tester might miss.

Additionally, consider adopting a formal cybersecurity framework. NIST is among the most popular today, particularly in the United States. It has a large community that offers best practices, tips and guidance, as well as audit support. Security frameworks can help you stay ahead of emerging threats and leverage expert advice in your strategy.

Ensuring Security in a Big Data Environment

Managing a big data environment can be daunting, especially when it includes pockets of sensitive information requiring more protection. You can utilize several strategies to protect high-risk data, including network segmentation, automated monitoring, least-privilege access control and penetration testing. These tactics will build layers of security and increase visibility.

Also Read: Apps And Data Protection – How To Secure Your Data

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How to do Big Data in your company, for what purpose and with what technology. After having defined the concept of Big Data, the manager’s blog summarizes its usefulness for the company and gives you its prerequisites for implementing one or more Big Data technologies in your organization.

Reminder of the facts: What is Big Data?

Big Data has gained momentum as the web has entered our lives. Indeed, the term defines all the new technologies that make it possible to collect, store and analyze data that are a priori independent of each other, in order to come up with correlations or decision-making aid conclusions for companies. By “data”, we mean all the personal information that we sow on the Internet (social networks, connected objects, Web browsing, etc.), which serve the commercial and marketing departments of a company, but it is also its financial information and reporting data. specific to each service… In short, Big Data makes it possible to gather a set of data, analyze them and correlate them. Big Data is also possible thanks to new technologies, which optimize the collection and storage of an increasingly large volume of information. Finally, the new artificial intelligence algorithms have the ability to analyze in real time and in a refined way all the data they are asked to process.

Big Data in my Company: What for?

The challenge for companies is to use Big Data to improve the profitability of their services:

• Improve knowledge of customer behavior for the marketing department
• Anticipate purchasing behavior
• Personalize and automate communication campaigns for the sales department
• Anticipate recruitment and training needs, better manage the age pyramid of your company for the human resources department
• Optimize costs and deadlines
• Make strategic decisions based on trends and statistics from a large volume of data (which improves the possibility of results)

Also Read: 5 Keys That Make Big Data The Perfect Ally For The Retail Sector

How to do Big Data in my Company: Prerequisites

1. Identify the Objective

The real challenge is to identify for what purpose you want to integrate Big Data technology into your business. To do this, ask yourself:

• Which position or service should be optimised/profitable
• How should the position be optimized (saving time, automating a time-consuming task with low added value, lowering costs, better understanding of a problem, etc.)
• What variables influence the optimization of your position/department
• What data should be collected and correlated to obtain a relevant result / analysis

Note that Big Data is generally only effective if it takes into account variables from multiple sources. You will need to take into account data that is outside the scope of the position or department you are looking to optimize. For example, a better understanding of customer behavior (optimization of the marketing department) requires data from social networks, web browsing, data collected in store (use of the loyalty card by your customers, for example – sales department), their interactions with the call center (sales department), the amount and dates of their purchases (invoicing department), etc. An effective Big Data system will have to include a cross-functional organization of your company; your identified data sources must leave their usual base to match towards the same analysis system.

2. Identify the appropriate Computer System

Some software and Saas offer Big Data technologies , particularly in the field of marketing where many tools are available. The analysis platforms set up by Google are Big Data tools, for example. You can visit the Google-Trends website to view the most popular keywords and web queries in France and around the world. Google Analytics is also a Big Data tool used by anyone who manages a website and wants to track their popularity on the Web. For specific purposes, you will need to deploy your own computer system and approach an expert.

3. Found a Big Data Service or Position

Ideally, a Big Data manager will be able to manage the procedures to be put in place for the proper management of company data: establishing data platforms, simplifying the tools for collecting and transmitting information to the main system. This position is called the “Chief Data Officer (CDO)” ; essential for good fluidity in the transmission of data from services that are a priori independent of each other. Finally, a draconian IT security policy is essential and a user charter must be established for your entire team. Found a Big Data department or position. Thus, setting up efficient Big Data requires extensive upstream thinking, or even a reorganization of your services. According to a study by Capgemini Consulting, 74% of the companies surveyed that have implemented Big Data have not clearly defined the criteria allowing them to identify, qualify and choose their use case in terms of Big Data (study carried out out of 250 executives and business leaders). To get out of the game and not invest at a loss in Big Data technology, we can only advise you to be accompanied by experts in the field!

Also Read: Big Data Analytics: What Is It, And Why Is It So Important?

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To make the most of all the information provided to us, it is essential to know how to measure and interpret it to understand the past, analyze the present and predict the future, a task that Big Data Analytics is responsible for.

What is big data?

Big Data is a concept that began to gain momentum in 2000 when Google and Yahoo started to use this resource to improve their platforms and expand their reach.

In summary, it describes the amount and complexity of structured and unstructured data generated and stored every second (such as access logs, emails, locations, social media data, among others). This data can be analyzed and interpreted to extract ideas that can support strategic decision making in companies.

To extract value from this data, Big Data focuses on some bases known as the 5 V’s :

• Volume: Big Data allows the storage of large volumes of information from various sources, such as sensors, commercial data, social network data, among others.
• Speed: All collected data should be analyzed as quickly as possible to support decision-making at the right time.
• Variety: Big Data collects a large amount of data from multiple sources that must be structured and organized.
• Truthfulness refers to data from human dynamics, such as search engines and social networks, since it is understood as fundamental interactions.
• Value: Big Data can distinguish data and information that have a more excellent value for the business.

Also Read: 5 Keys That Make Big Data The Perfect Ally For The Retail Sector

Big Data Analytics: what is it?

Big Data Analytics is the study of a large amount of data to draw behaviour patterns, discover unknown correlations, know market trends, and discover consumer preferences.

For this, predictive models, statistical algorithms and analyzes performed with high-performance systems are used that allow the storage, processing and discovery of patterns in the collected data.

Tips for Big Data Analytics

When we talk about Big Data Analytics, we can distinguish four types of analysis that we must consider:

• Descriptive analysis: It allows you to understand what happened in the past and understand ​​the trends to explore in the future. It focuses on comparisons and descriptions and can help spot patterns or segment some data.
• Diagnostic analysis: When you want to know the causes of a specific situation, this is the method to use. It allows analyzing the churn rate (abandonment rate) or the trends of a product/service use.
• Predictive analytics: It is the most used type of analysis and consists of studying forecasts through probabilities. It allows you to predict what will happen in future scenarios and understand which products are best to sell or the risk of losing customers.
• The prescriptive analysis is based on automation processes, or A / B tests, which allow interpreting situations if specific measures are taken. You can provide information on the following action to build customer loyalty: the best place on a website to place a banner or the best vehicle route to avoid traffic.

Advantages of using Big Data Analytics

Using one of the types of Big Data Analytics, companies can benefit from the following advantages:

• Cost reduction – Using Big Data-based cloud services reduces costs associated with storing large amounts of files.
• Improved decision making – Through Big Data Analytics, decision-makers will have a large amount of data at their disposal to analyze their history and predict future actions.
• Improvement or creation of new products and services – By analysing trends and customer needs, companies can improve or develop new products or services and offer real value to their target audience.
• Fraud detection – Financial institutions also use Big Data Analytics to prevent fraud.

In an increasingly data-filled world, Big Data Analytics will be one of the critical tools for business growth, improving customer relationships or optimizing products or services.

Before making any decision, companies can have the result of all possible scenarios in their possession, which minimizes the risk of error and increases the probability of success of each action.

Also Read: Data, Information And Big Data: Basic Concepts

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Aware that Big data offers competitive advantages that allow us to make a difference, Axis points out 5 reasons to promote the implementation of this technology in the retail sector:

Product optimization: controlling the stock of available products is one of the main concerns of those responsible for a store. Big Data allows you to categorize the assortment of products by developing classifications according to different categories, such as based on parameters such as the profit they report or the volume of sales they generate.

Customer loyalty analysis: Another benefit associated with processing massive data is that it allows you to develop customer loyalty strategies. This type of program offers the advantage of obtaining data such as differentiating customers who buy occasionally from those who do so regularly, knowing what their purchasing preferences are, etc. All this information allows retail to design different ways of approaching customer loyalty.

Trend detection: the analysis of the shopping cart (that is, the items that consumers purchase) through Big Data is a key aspect when it comes to detecting trends in terms of product consumption, knowing habits and patterns of consumption by consumers in terms of seasonality, gender differences, etc. In short, it allows us to understand customer behavior, thanks to which modifications can be established in the commercial strategy of retail, in the launch of products, etc.

Create a competitive pricing strategy: thanks to Big Data, an analysis of the commercial activity can be carried out by areas and type of product most in demand. In this way, a pricing strategy and special offers can be carried out in what is known as “hot zones” or areas with the highest purchasing activity. As a consequence, the number of sales is increased and results are improved at the same time that each consumer is offered the type of product that is most in demand.

Study of the competition: the benefits of this technology also allow companies to analyze the activities of competitors and, therefore, establish a strategy that makes it possible to make a difference. Big Data allows you to anticipate other commercial rivals and thus obtain a leadership position within the sector.

Also Read: Big Data Marketing Strategy: What It Is, Uses And Challenges

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Come on, the matter is simple: if consumers surf the net leaving a trace of their preferences and, above all, providing valuable personal information for companies, why not create a strategy based on it?

What Is Big Data Marketing?

The term “big data marketing” not only refers to the data itself; it also addresses the challenges, capabilities, and competencies associated with storing and analyzing such large data sets to support a more precise level of decision making.

What Is Big Data Marketing For?

Customers create new data every step of the way on the network. This can be structured data, such as that obtained when they click on websites, or unstructured data, such as that generated by posting comments on Facebook. Using big data technologies and analytical methods, marketers can extract, combine, and analyze both types of data in near real-time.

This can help them uncover hidden patterns and understand how different customer groups interact and how this leads to purchasing decisions.

Equipped with this knowledge, companies can develop marketing campaigns aimed at individual customer preferences, putting themselves ahead of the competition thanks to this customization.

Have you ever wanted to understand the true value of a customer? Or the exact costs of customer acquisition and how to reduce them? Big data touches every part of the marketing funnel helping to optimize sales and solve key business problems, turning data into insights to influence actions and drive better business results.

Industry leaders will need to start their journey by asking themselves some important questions to maximize the return on their investment in big data marketing:

• What should our big data analytics roadmap look like to achieve our marketing goals?
• What business outcomes would we like to influence by leveraging the wealth of data around customers?
• What capabilities and services should we develop by taking advantage of Big Data that allows a strong competitive advantage?
• What technology options will make the big data analytics journey possible?
• Do we have the right skills and resources in the company to take full advantage of big data marketing?

How Does Big Data Marketing Affect Advertising, Information And Customers?

Advertising and offers for products and services have always existed. However, perhaps never before has information on this type of content circulated in such quantity and with such frequency.

The challenge for today’s companies is not only about selling and offering a good service to their customers. For this to be the case, it is necessary to carry out a serious job of collecting, classifying, interpreting, and applying information related to consumers, the market, competition, prices, and even the economic framework.

Therefore, Big Data marketing allows you to make a much more precise approach to the markets. Audience segments are now people with their own needs and mass communication is now individual.

In conclusion, it is a tool that allows us to bring our focus even closer to those who are or are close to becoming our clients, something for which good information management is essential. You are ready?

What Are The Advantages Of Big Data Marketing?

The term “big data” refers to huge amounts of data that companies collect, and that comes from either their business efforts or their interaction with customers. This information, obtained by different means, is the basis of a successful marketing campaign.

The reasons for joining Big Data marketing are many, although the following stand out:

• Allow good planning: This is an essential basis for fighting market volatility and predicting trends and changes in customer behavior in time to develop a better and more efficient plan. In addition, using big data as a starting point for your own marketing strategy will allow a better view of existing options that will determine what works and what doesn’t. In this way, the probability of error decreases.
• Increased customization: Versatility is one of the qualities of an effective marketing campaign. Each step that is taken towards a new client or a new segment has to ensure this characteristic, in order to deliver in all cases a product or service that precisely matches the needs, tastes, and expectations of a specific client.
• Flexible pricing: This adaptability makes it possible to reach a wider audience because, although everyone agrees that higher quality demands a higher price, many people see the price of certain products as a major drawback. To overcome this problem, Big Data Marketing provides enough data to understand the user and be able to get the price calibration right.
• SEO optimization: Artificial intelligence and machine learning are combined with the organization’s marketing automation capabilities to ensure better results in customer retention. Big Data marketing allows you to evaluate the habits of the most loyal customers and discover what the company did to attract them. By repeating the same process, they will strike a balance between customer retention and attracting new ones.
• Tight budgets: Most marketers have difficulty allocating their budgets correctly. Using big data right in your business plan will help you calculate a person’s ROI more accurately than ever. By doing so, irrelevant and excessive expenses are reduced, freeing up more time and resources for the most important aspects of marketing campaigns.

Also Read: Reduce Risks And Optimize Costs With The Transformation To Azure

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