Complexities around data infrastructure are surging as companies gear to get a competitive edge and out-of-the-box offerings.
Every company goes through a data maturity matrix. In order to reach a level where you deploy AI models or self-service models, you need to invest in a robust foundation.
In my opinion, the foundation begins with a reliable data source or defining source of truth. Your data models won’t be impactful if it’s ingested with bad data. You know it’s garbage
On a high level, here are a few checks you can implement to ensure data reliability:
destination. This could be effective in running some financial recon too, like payment gateway to the sales table.
The most common question people face with:
Build versus Buy
I am a big fan of open source tech, however, in some critical modules, I prefer buying an out-of-the-box solution because it’s scalable and already tested in the market. Developing in-house might cost you around US2k per month and it includes a few hours of engineer’s time along with cloud cost.
If you are inclined toward buying an out-of-the-box solution, here are a few factors that should be part of your checklist.
It should be in a position to automatically detect my critical data assets and apply hygiene checks.
At last, the solution should help you reduce data quality incidents and make your data more reliable.
If your answer to any of the below questions or scenarios is “Yes”, then you should procure or deploy a data observability solution right away.
As software developers have leveraged on DataDog, Dynatrace, etc kind of solutions to ensure web/app uptime, data leaders should invest in data observability solutions to ensure data reliability.
You’ve likely heard about ELT — Extract Load and Transform… the Modern Data Stack’s evolution on ETL. This is a game changer by nature in that it enables organizations to ingest raw data into the data warehouse and transform it later. ELT gives end-users access to the entirety of the datasets they need by circumventing downstream issues of missing data that could prevent a specific business question from being answered.
A majority of business leaders believe data insights are key to the success of their business in a digital environment. However, many companies struggle to build a data-driven culture, with a key reason being the lack of a sound data democratization strategy.
Just like data mesh or the metrics layer, active metadata is the latest hot topic in the data world. As with every other new concept that gains popularity in the data stack, there’s been a sudden explosion of vendors rebranding to “active metadata”, ads following you everywhere and… confusion.
As the amount of data rapidly increases, so does the importance of data wrangling and data cleansing. Both processes play a key role in ensuring raw data can be used for operations, analytics, insights, and inform business decisions.
Do you know the current status — quality, reliability, and uptime — of your data and data systems? Not last month or last week, but where they stand at this moment. As businesses grow, being able to confidently answer this question becomes more important. That’s because data needs to be clean, accurate, and up-to-date to be considered reliable for analysis and decision-making. This confidence comes through what’s known as data observability.
In the past years, organizations have been investing heavily to convert themselves into data-driven organizations with the objective to personalize customer experiences, optimize business processes, drive strategic business decisions, etc. As a result, modern data environments are constantly evolving and becoming more and more complex. In general, more data means more business insights that can lead to better decision-making. However, more data also means more complex data infrastructure, which can cause decreased data quality, a higher chance of data breaking, and consequently erosion of data trust within organizations and risk of not being compliant with regulations. The data observability category — which has quickly been developing during the past couple of years — aims to solve these challenges by enabling organizations to trust their data at all times. Although the category is relatively young, there are already a wide variety of players with different offerings and applying various technologies to solve data quality problems.
Data governance is more than just having a strategy – it is about establishing a culture where quality data is achieved, maintained, valued, and used to drive the business. Modern-day businesses are supported by data and information in many ways and forms. In recent years, data has become the foundation for competition, productivity, growth, and innovation. We are seeing successful organizations shift their focus from producing data to consuming it, and data governance strategies becoming increasingly important to support their crucial business initiatives. Executives and shareholders are starting to realize that data is a strategic asset and data governance is a must if they want to get value from data.
Data matters more than ever – we all know that. But at a time when being a data-driven business is so critical, how much can we trust data and what it tells us? That’s the question behind data reliability, which focuses on having complete and accurate data that people can trust. This article will explore everything you know about data reliability and the important role of data observability along the way, including:
The term “data lineage” has been thrown around a lot over the last few years. What started as an idea of connecting between datasets quickly became a very confusing term that now gets misused often. It’s time to put order to the chaos and dig deep into what it really is. Because the answer matters quite a lot. And getting it right matters even more to data organizations.