Today’s data race is becoming increasingly competitive, and some companies are already speeding ahead. In a world producing more data than ever, companies need to keep up with larger and more intricate system processes and management. It’s no longer enough to monitor inbound and outbound data segments – companies need a newer, more holistic approach. Rohit Choudhary, CEO of Acceldata, how companies can get ahead in the data race with multi-dimensional data observability.
Whether they like it or not, companies are all racing towards the same finish line: to be a fully-fledged 21st-century data-driven enterprise. Some companies have upgraded their proverbial engines and are ahead of the crowd, while others are still at the start line or slowly cruising with the rest.
However, too many companies are focused on power and have ignored critical infrastructure functionality.Â
While racecars and data may seem entirely different, both require various working parts that all help achieve business and technology efficiency. These companies are investing in accelerating current digital analytical processes while simultaneously underinvesting towards other necessary upgrades.Â
Regardless of how fast a race car or a company is, utilizing every tool and system process available to monitor for blindspots, deficiencies or system errors is the key to reaching that checkered flag.Â
To step away from the race car analogy, companies are suffering from massive data blind spots and require a more focused solution.Â
Business Observability Requires Data ObservabilityÂ
A colossal mistake some companies make is overemphasizing the importance of robust and real-time awareness over inventory and their channel partners. This concept is sometimes called operational intelligence or business observability. Unfortunately, this philosophy helps create scenarios in which bottlenecks, inaccurate data, and system failures can hide until it’s too late. In other words, data blind spots.Â
Companies that may be diligent about business observability often ignore data observability, the what, where, why, and how behind data and data systems.Â
According to a recent SigmaOpens a new window survey report, 75% of data experts spend nearly half of their time preparing ad hoc reports. What’s worse, 1 in 4 business experts expressed in the same report that data analyses take too long, leading to teams giving up on finding answers to anomalies within their data.Â
The data supply chain contains a multitude of information, pathways into various data pipelines and even the occasional data malfunction. Without knowing why a bottleneck has occurred or why a data set is flawed, knowing that there is an issue in the system is only half of the digital transformation solution.Â
Seven Questions to Check if Your Company Is Suffering From Data Blind Spots
Regardless of where someone is on the corporate ladder, data is part of their daily work, and it’s important to ask questions about the accessibility and validity of data. From the CEO to a data engineer, these questions can help guide companies in the right direction:Â
- Do you have complete visibility of your data supply chain?Â
- Are you blind to the context of your data issues?Â
- Are you able to get a good answer if you ask, ‘Can I trust this dataset?’
- Are you the first to know and the first to fix a data problem?Â
- Do you know if your databases and data warehouse are running at peak efficiency?Â
- How much will your cloud storage cost next month?Â
- When do you find out if your data-related SLOs and SLAs are failing?Â
Companies running through a holistic dashboard and 360-degree viewpoint of data observability are capable of answering these.Â
See More: Why the Future of Database Management Lies In Open Source
Eliminating Data Blind SpotsÂ
Admitting that current data management systems may not be optimal and perpetuating these data blind spots can be difficult. However, data processing and storage costs have become overwhelming, and not every data observability tool is created equal.Â
Some so-called data observability systems only provide a portion of system visibility, providing only a slim piece of the whole data pipeline system.Â
Application performance management (APM) tools don’t provide data or infrastructure layer observability, meaning they can’t validate the quality of data pipelines. APM tools also fail to aid data engineers in fixing data bottlenecks or data errors, validating dataset quality reliability, and avoiding skewed data.Â
The good news is that eliminating these blind spots is made simple by deploying a multidimensional data observability solution.Â
Wherever the data is traveling, being stored or processed, multidimensional data observability provides real-time alerts and analytical insights that aid in data flow and prevent errors. When your data is powering mission-critical operations in real-time, your data analytics need to be at the same speed.Â
Current data transformation and platform tools aren’t reliable with the current data usage and output levels. More data, more sources and more data transformations without the proper management tools create more problems, including lack of trust, redundancy, poor searchability and data errors.Â
Even if a company has an APM solution, it will lack visibility and correlation capabilities at every layer.Â
The only way to achieve a singular, unified view of business data and the machinations of the data pipeline is to utilize a multi-dimensional data observability tool. Not only that: it’s the only way to keep your company in the data race, let alone win.Â
How are you ensuring data observability? Share with us on LinkedInOpens a new window , TwitterOpens a new window , or FacebookOpens a new window . We’d love to know!