How to Fix Your Data Quality Problem

Introducing a better way to prevent bad data.

Image for post

Data quality is top of mind for every data professional — and for good reason. Bad data****costs companies valuable time, resources, and most of all, revenue. So why are so many of us struggling with trusting our data? Isn’t there a better way?

The data landscape is constantly evolving, creating new opportunities for richer insights at every turn. Data sources old and new mingle in the same data lakes and warehouses, and there are vendors to serve your every need, from helping you build better data catalogs to generating mouthwatering visualizations (leave it to the NYT to make mortgages look sexy).

Not surprisingly, one of the most common questions customers ask me is “what data tools do you recommend?

More data means more insight into your business. At the same time, more data introduces a heightened risk of errors and uncertainty. It’s no wonder data leaders are scrambling to purchase solutions and build teams that both empower smarter decision making and manage data’s inherent complexities.

But I think it’s worth asking ourselves a slightly different question. Instead, consider: **“what is required for our organization to make the best use of — and trust — our data?”**

Data quality does not always solve for bad data

It’s a scary prospect to make decisions with data you can’t trust, and yet it’s an all-too-common practice of even the most competent and experienced data teams. Many teams first look to data quality as an anecdote for data health and reliability. We like to say “garbage in, garbage out.” It’s a true statement — but in today’s world, is that sufficient?

Businesses spend time, money, and resources buying solutions and building teams to manage all this infrastructure with the pipe(line) dream of one day being a well-oiled, data-driven machine — but data issues can occur at any stage of the pipeline, from ingestion to insights. And simple row counts, ad hoc scripts, and even standard data quality conventions at ingestion just won’t cut it.

#data-science #data-analysis #data-quality #towards-data-science #data #data analysis

What is GEEK

Buddha Community

How to Fix Your Data Quality Problem
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

How to Fix Your Data Quality Problem

Introducing a better way to prevent bad data.

Image for post

Data quality is top of mind for every data professional — and for good reason. Bad data****costs companies valuable time, resources, and most of all, revenue. So why are so many of us struggling with trusting our data? Isn’t there a better way?

The data landscape is constantly evolving, creating new opportunities for richer insights at every turn. Data sources old and new mingle in the same data lakes and warehouses, and there are vendors to serve your every need, from helping you build better data catalogs to generating mouthwatering visualizations (leave it to the NYT to make mortgages look sexy).

Not surprisingly, one of the most common questions customers ask me is “what data tools do you recommend?

More data means more insight into your business. At the same time, more data introduces a heightened risk of errors and uncertainty. It’s no wonder data leaders are scrambling to purchase solutions and build teams that both empower smarter decision making and manage data’s inherent complexities.

But I think it’s worth asking ourselves a slightly different question. Instead, consider: **“what is required for our organization to make the best use of — and trust — our data?”**

Data quality does not always solve for bad data

It’s a scary prospect to make decisions with data you can’t trust, and yet it’s an all-too-common practice of even the most competent and experienced data teams. Many teams first look to data quality as an anecdote for data health and reliability. We like to say “garbage in, garbage out.” It’s a true statement — but in today’s world, is that sufficient?

Businesses spend time, money, and resources buying solutions and building teams to manage all this infrastructure with the pipe(line) dream of one day being a well-oiled, data-driven machine — but data issues can occur at any stage of the pipeline, from ingestion to insights. And simple row counts, ad hoc scripts, and even standard data quality conventions at ingestion just won’t cut it.

#data-science #data-analysis #data-quality #towards-data-science #data #data analysis

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

1618137000

Data Observability: How to Fix Data Quality at Scale

Companies spend upwards of $15 million annually tackling data downtime, in other words, periods of time where data is missing, broken, or otherwise erroneous, and 1 in 5 companies have lost a customer due to incomplete or inaccurate data.
Fortunately, there’s hope in the next frontier of data: observability. Here’s how data engineers and BI analysts at Yotpo, a global eCommerce company, increases cost savings, collaboration, and productivity with data observability at scale.
Yotpo works with eCommerce companies across the world to help them accelerate online revenue growth through reviews, visual marketing, loyalty and referral programs, and SMS marketing.
For Yoav Kamin, Director of Business Performance, and Doron Porat, Data Engineering Team Leader, having consistently accurate and reliable data is foundational to the success of this mission.

#data-analysis #data-observability #data-engineering #data-quality #data

Virgil  Hagenes

Virgil Hagenes

1602702000

Data Quality Testing Skills Needed For Data Integration Projects

The impulse to cut project costs is often strong, especially in the final delivery phase of data integration and data migration projects. At this late phase of the project, a common mistake is to delegate testing responsibilities to resources with limited business and data testing skills.

Data integrations are at the core of data warehousing, data migration, data synchronization, and data consolidation projects.

In the past, most data integration projects involved data stored in databases. Today, it’s essential for organizations to also integrate their database or structured data with data from documents, e-mails, log files, websites, social media, audio, and video files.

Using data warehousing as an example, Figure 1 illustrates the primary checkpoints (testing points) in an end-to-end data quality testing process. Shown are points at which data (as it’s extracted, transformed, aggregated, consolidated, etc.) should be verified – that is, extracting source data, transforming source data for loads into target databases, aggregating data for loads into data marts, and more.

Only after data owners and all other stakeholders confirm that data integration was successful can the whole process be considered complete and ready for production.

#big data #data integration #data governance #data validation #data accuracy #data warehouse testing #etl testing #data integrations