Solving Three Data Problems with Data Observability

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Read more about author Rohit Choudhary.

Data collection, while crucial to the overall functionality and health of a business, does not automatically lead to success. If data processes are not at peak performance and efficiency, businesses are just collecting massive stores of data for no reason. Data without insight is useless, and the energy spent collecting it, is wasted.

Effective use of data collection for analytical and insight purposes requires forethought and strategy. If enterprises want to see more success, they need to address three of the most common data problems: data silos, poor data quality, and over-reliance on manual intervention. 


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Gartner predicts that approximately 20% of collected data in 2022 will result in real business outcomes. The day-to-day basics of data analytics are no longer enough. Achieving digital transformation will require data teams to reach beyond cleaning data records. Multidimensional data observability may be the right approach to solving these three significant issues within data analytics. 

Data Silos 

Organizations are collecting more data now than ever before. We are a data-driven society, and the trends that organizations see within datasets will often predict where we will be in a year or two. What this also means is that enterprises are overwhelmed with data. 

Data silos have become commonplace in the world of data. Isolated data segmented away from useful datasets becomes costly and creates integrity problems. Data teams spend more time forcing these silos into data pipelines that span various platforms and technologies. These data silos only cause inefficiencies and data buildup within organizations. 

Data observability can help businesses avoid data silos by implementing a single, unified view of data and data lifecycles. Data teams will be given the option to view data transformations throughout the data lifecycle, as well as a holistic view that provides them the opportunity to easily spot and debug problems throughout the process. 

Poor Data Quality and Lack of Visibility

According to a recent Harvard Business Review Survey, these are some of the biggest obstacles businesses face to generating actionable insights:

  • Poor data quality (42%)
  • Lack of effective processes to generate analysis (40%)
  • Inaccessible data (37%)

Depriving data teams of a simple and unified view of the entire data lifecycle is hurting businesses. Something as simple as being able to access data should not be a problem data teams face, but it is. 

Another survey by Gartner indicates that only around 15% of Data Science projects ever reach production. Data and analytical capabilities are not readily available in all levels within an organization, meaning enterprises are not seeing the full potential of their data. 

This scenario creates a paradox where businesses are collecting more data than ever, yet the expenses of storing and analyzing data are also at their highest. Lowering data handling costs and enabling real-time analysis can be as easy as: 

  • Creating more observable data and infra layers
  • Optimizing data consumption by identifying data bottlenecks
  • Offering real-time decision-making at every level 

Manual Data Interventions

In order to debug problems, detect oddities, and write queries/scripts for downstream consumption and analytics, data teams currently provide manual solutions and intervention. Not only is this not sustainable for the data teams, but it’s no longer serving businesses. With an increase in data volume, AI and automation are the best solution. 

However, despite businesses financially backing big data and AI, very few companies have taken the leap and continue to require manual interventions. 

With AI capabilities, enterprises would be able to automate these manual interventions and accelerate data analysis. 

Leveling the Playing Field: Data Observability 

While some of the top tech companies can afford to continue these expensive data practices, it’s not only behind the times but also beyond the reach of other companies. Data analytics and collection should not be behind a paywall, nor should it continue to be living in the past. In some ways, smaller tech companies can compete with, if not outperform, larger enterprises just by utilizing smarter, faster methods. 

Automating manual tasks with multidimensional observability can help data teams make data and its infrastructure more observable, useful, and achievable. 

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