Gerhard  Brink

Gerhard Brink

1624825860

What are the Best Steps to Effective Data Classification?

Data protection is not only a legal necessity. It is essential for an organization’s survival and profitability. Nowadays, storage has become cheap, and organizations have become data hoarders. And even one day will come when they’ll get around mining all of those data and look for something useful.

But, again, data hoarding causes serious issues. And most of what is collected may become redundant, old, or when it is not touched for years.

Moreover, storage might be cheap, but it is not free. And storing a huge amount of data might cost you and, more importantly, increases your risk.

So, suppose your sensitive data is stored digitally, which includes intellectual property, personally identifiable data on the customers or employees, protected health information or financial account information, and credit card details. In that case, these needs are to be properly secured.

So how to protect your data?

What is data classification?

Here are the seven effective steps to Data Classification

#big data #latest news #what are the best steps to effective data classification? #effective data classification #best #effective

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What are the Best Steps to Effective Data Classification?
Gerhard  Brink

Gerhard Brink

1624825860

What are the Best Steps to Effective Data Classification?

Data protection is not only a legal necessity. It is essential for an organization’s survival and profitability. Nowadays, storage has become cheap, and organizations have become data hoarders. And even one day will come when they’ll get around mining all of those data and look for something useful.

But, again, data hoarding causes serious issues. And most of what is collected may become redundant, old, or when it is not touched for years.

Moreover, storage might be cheap, but it is not free. And storing a huge amount of data might cost you and, more importantly, increases your risk.

So, suppose your sensitive data is stored digitally, which includes intellectual property, personally identifiable data on the customers or employees, protected health information or financial account information, and credit card details. In that case, these needs are to be properly secured.

So how to protect your data?

What is data classification?

Here are the seven effective steps to Data Classification

#big data #latest news #what are the best steps to effective data classification? #effective data classification #best #effective

Siphiwe  Nair

Siphiwe Nair

1620466520

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.

#big data #data science #big data analytics #data analysis #data architecture #data transformation #data platform #data strategy #cloud data platform #data acquisition

Gerhard  Brink

Gerhard Brink

1620629020

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.

#big data #data analytics #data analysis #business analytics #data warehouse #data storage #data lake #data lake architecture #data lake governance #data lake management

Uriah  Dietrich

Uriah Dietrich

1618457700

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.

#data science #data #data security #data integration #etl #data warehouse #data breach #elt #bid data

Cyrus  Kreiger

Cyrus Kreiger

1617731760

An Introduction to Data Connectors: Your First Step to Data Analytics

Modern analytics teams are hungry for data. They are generating incredible insights that make their organizations smarter and are emphasizing the need for data-driven decision making across the board. However, data comes in many shapes and forms and is often siloed away. What actually makes the work of analytics teams possible is the aggregation of data from a variety of sources into a single location where it is easy to query and transform. And, of course, this data needs to be accurate and up-to-date at all times.

Let’s take an example. Maybe you’re trying to understand how COVID-19 is impacting your churn rates, so you can plan your sales and marketing spends appropriately in 2021. For this, you need to extract and combine data from a few different sources:

  • MySQL database that details all the interactions your users are having with your product
  • Salesforce account that contains the latest information about your current and prospective customers
  • Zendesk account that has all support tickets raised by your customers

#data-analytics #data-science #data-engineering #data #data-warehouse #snowflake #data-connector #machine-learning