DQ Series: The Data Quality Dilemma. Beyond frustrating data inconsistencies and missing values, many struggle to access and utilise essential data. Rebecca Kelly, Head of AI & Analytics dives into the root causes and introduces INQDATA's approach to streamlining data pipelines and empowering data-driven decisions.

Many end users experience the same data problems again and again:

  • You notice missing data (if you’re lucky, during inspection and if you’re not, post deployment);
  • You scratch your head because data from one source says one thing whilst another disagrees;
  • Avoiding available data completely because it’s “too messy”;
  • Spending hours wrangling the data into the format you need before you can even begin your work; or
  • Receiving data that has been processed to meet other users needs and not your own.

The root cause of these issues? Poor data quality practices and controls. This often neglected aspect of the data pipeline is precisely where INQDATA comes in, providing much-needed attention to ensure a thriving data ecosystem.

Herein lies a misalignment in priorities - data quality is prioritised internally as "nice to have" whilst data delivery as a "must have". Teams tasked with extracting value from data are constantly diverted from their analysis due to pervasive data quality issues across numerous datasets without addressing the issue at its core. Instead of addressing the root cause, they're relegated to firefighting. This burden further amplifies in larger organisations where data infrastructure is treated as a core utility, leading to politically charged timing decisions about onboarding new datasets, even if you have budget and agreements already in place with the market data provider of your choice.

The real tragedy here is that getting the data into the system is, for most organisations, undifferentiated labour. While crucial, data ingestion rarely delivers competitive edge or direct revenue. It's true there are some organizations out there with gleaming infrastructure who reap the rewards, but by-and-large the problem of rapidly onboarding new datasets while proactively managing data quality and making data accessible at scale to end users is, for many teams, more of a "necessary evil" ahead of the value-add work they do.  

INQDATA focus on perfecting the data delivery pipeline to ensure high quality data, available at scale across your organisation. We take on that undifferentiated labour and work directly with your preferred Market Data Providers to rapidly ingest your data, handle all of the storage and delivery infrastructure and perform data quality. Most importantly, our generalisable framework for data quality allows you - the end user - to configure your own data quality landscape across all your data.  Interested in our unique approach? Stay tuned for our next blog post where we’ll go into more detail about our Data Quality Methodology.

If INQDATA sounds like something you or your team are interested in hearing more about, contact us at info@inqdata.com.

Alternatively if you just want to chat Data Quality feel free to contact me directly at rebecca.kelly@inqdata.com.

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