5 Data Encryption Best Practices To Follow

Any device that is connected to the internet is vulnerable to data breaches. McAfee estimates that more than 780,000 records are lost to hacking each day.

Data encryption is one of the best available protection against unauthorized access to your files. Data encryption converts plain-text information into an unreadable form, which can only be accessed by a user with the correct decryption key.

However, in a world where cybercrime is on the rise, no security measure is impenetrable. But, by following the best practices, you can mitigate the risk.

Here are the five data encryption practices you must follow to reduce the risk of breaches.

1 — Create a Strategy for Securing Data

Depending on your business size, your security strategy might differ. For example, enterprises with many users should be using cloud servers to store their encrypted data, while small businesses can use their workstations as storage media.

Here are some things to keep in mind when creating a security strategy:

Understand the rules and regulations: PII (personally identifiable information) needs special encryption to meet US government compliance regulations. Check what other governing policies apply to your business and how they impact your security strategies.
Encryption tools: Determine which encryption tools are best suited for your business (according to data volume and business needs).
Encryption algorithm: Check if the technologies or algorithm used by your encryption vendor meets international standards.
Key management: Decide on ways to generate, store, and replace keys. Also, create strategies to destroy the encryption keys in case of a security breach.
Auditing data: Determine how you will track irregularities or identify unauthorized access to your encryption keys.

Another thing to check is the speed of the encryption. You would not want to wait for hours to get your data encrypted, especially when you have to send it over the network urgently. Ask your vendor how quickly the tool can encrypt the files without compromising on security.

2 — Protect Data In Transit

Data collection and storage are essential activities for every business. Data stored in your system or dedicated servers is much easier to protect than the files in transit. Since the data is moving back and forth from different locations, it is best to use a VPN to hide your IP address.

Services like Urban VPN protects your online identity with the strongest encryption and DNS/IPv6 data leak protection. Here are some more reasons to use a VPN while transferring data:

VPNs create an encrypted connection between your device and the internet, hiding all your online activities.
Has security protocols to safeguard your devices and data against attacks on public Wi-Fi.
Since it changes your IP address, even the prying eyes won’t be able to see if you have files in transit.
Secures access from workstations to your storage device (cloud network, servers, etc.).

3 — Determine Which Data to Encrypt

When it comes to determining which data to encrypt, it is essential to consider the worst-case scenarios.

How much loss and damage it would do if a specific set of data is compromised? If your answer is too much, then you should be encrypting those data.

Sensitive information like names, contact details, credit card details, and social security numbers are examples of data that need to be encrypted no matter how robust your security system is.

Tip: Encrypt the files you are transferring over a network or accessing remotely.

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5 Data Encryption Best Practices To Follow

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Minimum educational required – 10+2 passed in any stream from a recognized board.

The age limit is 18 to 25 years. It may differ from one airline to another!

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Your Data Architecture: Simple Best Practices for Your Data Strategy

If you accumulate data on which you base your decision-making as an organization, you should probably think about your data architecture and possible best practices.

If you accumulate data on which you base your decision-making as an organization, you most probably need to think about your data architecture and consider possible best practices. Gaining a competitive edge, remaining customer-centric to the greatest extent possible, and streamlining processes to get on-the-button outcomes can all be traced back to an organization’s capacity to build a future-ready data architecture.

In what follows, we offer a short overview of the overarching capabilities of data architecture. These include user-centricity, elasticity, robustness, and the capacity to ensure the seamless flow of data at all times. Added to these are automation enablement, plus security and data governance considerations. These points from our checklist for what we perceive to be an anticipatory analytics ecosystem.

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Gerhard Brink

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Getting Started With Data Lakes

Frameworks for Efficient Enterprise Analytics

The opportunities big data offers also come with very real challenges that many organizations are facing today. Often, it’s finding the most cost-effective, scalable way to store and process boundless volumes of data in multiple formats that come from a growing number of sources. Then organizations need the analytical capabilities and flexibility to turn this data into insights that can meet their specific business objectives.

This Refcard dives into how a data lake helps tackle these challenges at both ends — from its enhanced architecture that’s designed for efficient data ingestion, storage, and management to its advanced analytics functionality and performance flexibility. You’ll also explore key benefits and common use cases.

Introduction

As technology continues to evolve with new data sources, such as IoT sensors and social media churning out large volumes of data, there has never been a better time to discuss the possibilities and challenges of managing such data for varying analytical insights. In this Refcard, we dig deep into how data lakes solve the problem of storing and processing enormous amounts of data. While doing so, we also explore the benefits of data lakes, their use cases, and how they differ from data warehouses (DWHs).

This is a preview of the Getting Started With Data Lakes Refcard. To read the entire Refcard, please download the PDF from the link above.

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Uriah Dietrich

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What Is ETLT? Merging the Best of ETL and ELT Into a Single ETLT Data Integration Strategy

Data integration solutions typically advocate that one approach – either ETL or ELT – is better than the other. In reality, both ETL (extract, transform, load) and ELT (extract, load, transform) serve indispensable roles in the data integration space:

ETL is valuable when it comes to data quality, data security, and data compliance. It can also save money on data warehousing costs. However, ETL is slow when ingesting unstructured data, and it can lack flexibility.
ELT is fast when ingesting large amounts of raw, unstructured data. It also brings flexibility to your data integration and data analytics strategies. However, ELT sacrifices data quality, security, and compliance in many cases.

Because ETL and ELT present different strengths and weaknesses, many organizations are using a hybrid “ETLT” approach to get the best of both worlds. In this guide, we’ll help you understand the “why, what, and how” of ETLT, so you can determine if it’s right for your use-case.

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Rusty Shanahan

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