A highly efficient business that runs smoothly nearly all the time is what many owners and employees hope to create. That goal includes processes that run like clockwork, project milestones that work in sync, and deadlines that always get met. That’s not to mention delighted customers who benefit from the company’s efficiency and streamlined operations.
While business owners and staff aren’t off base for wanting to achieve this ideal, the reality is delays and setbacks happen. Bottlenecks, visible or hidden, create obstacles that slow a company down or even bring it to a hard stop. Hidden or less obvious bottlenecks are often the most challenging for teams to overcome and eliminate from the organization.
But with today’s technical advancements and solutions, those less visible obstacles can move into plain sight. Data observability tools help identify impediments in businesses with complex operations and information sources. Let’s look at some of the ways observability solutions alleviate or remove the obstacles that can hold your business back.
Predict and Correct Issues Linked to Unreliable Data
Data observability solutions incorporate artificial intelligence and machine learning. These technologies enable the software to monitor and spot potential problems that humans tend to miss. Elaborate data pipelines can lead to downtime or outages due to multiple and varied applications that ingest, process and transform data. Errors like duplicate data or missing data will also slow teams down as they try to untangle intricate webs of information.
With observability tools, your staff receives alerts about problems such as blockages in your pipeline. These notifications reveal where there may be gaps and inconsistencies as data flows through the pipeline.
Data observability removes those hidden obstacles because machine learning looks at the big picture and detects patterns that could cause outages and spell disaster. As an added bonus, observability solutions can also offer ‘play books’ so employees can eliminate manual processes with automated workflows.
Improve Data Quality
Poor data quality can literally drain a business. Bad and low-quality information is thought to be responsible for average annual losses of $15 million among organizations. Despite these significant financial impacts, almost 60% of businesses fail to measure costs linked to bad data.
It’s difficult to manage what you don’t measure. It’s even more challenging to try to measure what you don’t know you have. Low-quality data persists in organizations that gather a lot of information without knowing why. It’s a trap that’s easy for leaders and managers to fall into, given the push towards data-driven decision making.
Data observability solutions help improves data quality by revealing information sources across an entire company. Maybe marketing is gathering customer feedback from new sales activity. But account executives are recording additional client feedback in a CRM application. Yet accounting uses a different database to record which clients purchase specific products and services. None of it syncs, and not everyone is aware all this data exists.
Observability tools show where your information sources are, so you can determine whether they’re necessary. They can also help you decide whether you need to integrate them.
Manage Dynamic Information and Data Links
The fast-paced nature of operating a business means that data sets and information sources don’t remain static. Older applications, information-gathering processes, and databases are phased out, and replacements and additions are brought in. However, aged data sets and information don’t necessarily go away.
Often, companies need to migrate existing data from one application to another. Yet the process is messy and can cause errors due to corruption. One database’s way of categorizing and classifying portions of a data set might not match up with another’s. As a result, pipelines can break, previously associated information can become unlinked, and some records can become lost or misplaced.
Migrating users from one email platform to another is a perfect example. Sometimes manual intervention is necessary because of corrupt items in a person’s inbox. Seemingly minor details, such as the sizes of saved attachments and email account settings, can also cause failures. Other times a user’s credentials and email address aren’t associated with categories a migration tool recognizes.
Data observability solutions monitor and find raw data that’s missing associations or categorizations. Staff can intervene to correct the problems, or they can set up rules for the observability tool to automatically fix them. These rules will classify raw data with categories, labels, and associations that applications recognize and accept. Through process automation, software migrations will become smoother and won’t cause as many delays.
Conclusion
The Theory of Constraints is an idea the manufacturing world uses to relieve and remove bottlenecks from supply chain systems. Knowledge-based organizations can use a similar approach, as information pipelines and systems often function as a supply chain. Bottlenecks can happen because of unreliable and poor quality data and excessive or redundant information. Changes in data set requirements, associations, and software applications also create obstacles.
However, data observability solutions help companies monitor and correct these common problems in complex information systems and pipelines. AI and machine learning technology reveal what constraints exist. By making these limitations visible, observability tools assist business leaders in reducing the most stubborn of bottlenecks.
