Today, every business aims to grow quickly, strengthen its position and capture a sizeable share of the market. However, the market conditions are ever-evolving, with new technologies and approaches emerging daily. It is critical for businesses to adapt to changing market conditions and navigate complexities.
As organizations scale up, they need to reevaluate their processes as the one that worked earlier might not in the long run. This is where metrics come in handy as performance indicators.
Significance of metrics for businesses
Metrics are units of measurement. For example, if your weight is calculated in pounds or kilograms, height is measured in feet or centimeters. Similarly, businesses have metrics to indicate how they have performed over time. A business metric is a quantifiable measurement used to track and assess the performance of a specific business against a particular timeline. For instance, IT administrators use failure metrics to determine the reliability and performance of an IT infrastructure, including PC, software, hardware, etc.
Like any discipline, Data Engineering relies on failure metrics to assess the reliability of data and data pipelines and gauge the effectiveness of their troubleshooting efforts. A data engineer's role involves centralizing high-quality data and eliminating the manual work of extracting and loading data. They also augment capacity and unlock new applications by updating data, making it more reliable and complete to help organizations reap its benefits.
Data Engineering metrics help businesses gauge if they are moving in the right direction or whether there is a need for course correction. As companies increasingly focus on customer experiences, metrics help translate vague customer requirements into tangible numbers, which can be used to map the process for its efficiency. They also aid in evaluating the quality of customer service for businesses and objectively state whether their customers' requirements are being met.
A data engineering course encompasses the study of all these metrics to equip you with the necessary skills to excel in the field. These are also included in data engineer certifications and data engineering online courses to help professionals excel in the field. Although there are several metrics, the key is to identify the suitable ones for your business and use them effectively to improve outcomes.
Here are some metrics a data engineer should care about in order of importance.
Mean Down Time (MDT): Mean Down Time is central to data engineering. Minimizing data downtime caused by bottlenecks or unreliable data is the ultimate objective for a data engineer. Downtime measures the time lag between a stoppage being reported and being resolved. Although Zero downtime is not achievable, it is included in the curriculum of data engineering online courses as it is the ideal situation which every data engineer strives for. Mean Downtime is expressed as the reverse of the uptime in percentage, with 99.999 percent availability being the highest achievable figure.
Mean Time To Recover/Resolve/Restore/Repair (MTTR) The terms 'recover, resolve, restore, and repair may have similar connotations in literature. However, these terms imply a unique meaning in the data science context. Recover or Restore measures how long it takes to bring an interrupted data pipeline online. Resolve and Restore indicate how long it takes to identify and address a data error or data quality. MTTR consists of the time taken to identify the issue, the time taken to perform Root Cause Analysis to ascertain the causes and the time in which the issue was resolved. Hence, it is similar to Mean Time to Restore Services (MTRS). MTTR is perhaps the best failure metric in Data Ops and DevOps.
Mean Time Between Failures (MTBF): Mean Time Between Failures is applied for repairable hardware and software, which can be restarted except when it has been corrupted. MTBF is a suitable metric to connote data applications and server crashes, and it is a more comprehensive Mean Time to Failure (MTBF) which applies to hardware only. This versatility makes MTBF a key indicator for improving team performance and customer experience. However, MTBF does not include the time to repair hardware and recover/restore service. In such cases, data engineering relies on KPIs such as Mean Time Between Service Incidents which provides for both MTBF and either of these- Mean Time to Recovery (MTTR) and Mean Time to Restore Service (MTRS).
Mean Time to Restore Service (MTRS): MTRS is a useful indicator for data engineers to enhance business performance and uptime as well as maximize customer experience. It is similar to Mean Time To Recover/Resolve/Restore/Repair (MTTR) but does not cover data quality issues. It is applied for on-premises data servers and infrastructure run on a public, multi-tenant service.
Mean Time Between Service Incidents (MTBSI): MTBFI is the sum total of the Mean Time Between Failures (MTBF) and Mean Time to Restore Service/Mean Time to Recovery (MTTR). It is a vital metric for assessing the reliability of the infrastructure and responsiveness of the DataOps team for identifying the root causes of issues. Data engineering training aims at minimizing the mean time between service incidents to ensure customer-centricity.
Mean Time To Respond (MTTR): MTTR indicates the responsiveness of a team to an alert or a mail by measuring the time taken to elicit their response. This metric is the best source of motivation for data engineering professionals. This metric becomes more useful when used with Mean Time to Recover/Resolve/Repair as it helps track how long it takes for the DataOps team to respond to issues and how long they take to fix them.
Mean Time To Acknowledge (MTTA): MTTA tracks the time taken to detect the failure and begin the redressal process. It is similar to Mean Time to Know, which helps gauge the responsiveness of on-call DataOps teams. It also helps ensure that customers and users are promptly informed about addressing these issues. When combined with MTTR(Mean Time to Respond), it ensures that on-call data respond to alerts timely and work at a rapid pace without losing time.
Mean Time To Know (MTTK): MTTK measures the gap between sending an alert and detecting the issue. It is an effective way to map the forensic skills of the DataOps team. It is a niche metric in data engineering; it serves useful in training data engineering professionals.
Mean Time To Verify (MTTV): Mean Time to Verify is used during the last step of the resolution process. It tracks the time between the deployment of the solution and the time in which when the latter actually resolves the issue. Reducing MTTV is challenging for most businesses due to complex data pipelines and heterogeneous repositories.
Mean Time To Identify (MTTI): Mean Time to Identify is synonymous to Mean Time to Detect, which is discussed below.
Mean Time To Detect (MTTD): This metric evaluates the effectiveness of monitoring and observability platforms and automated alerts. However, the key is not to overemphasize this metric, as it might lead to excessive monitoring. For instance, a monitoring system tailored to the shortest MTTD will alert too quickly and at an increased frequency. This might lead to the generation of alerts for minor issues and lead to alert fatigue among data engineers.
Mean Time To Failure (MTTF): This metric assesses the average lifespan of non-repairable hardware or a device under optimal operating conditions. It is particularly useful in mission-critical datacentres, and on-site premise data servers, which plan hardware refreshes based on the predicted lifespan of hard discs, solid-state drives, network hubs, switches and cards. It is noteworthy that the responsibility of such hardware lies with IT and network administrators. MTTF has also given way to Mean Time Between Failures which is a more useful indicator for software and hardware crashes.
Realizing the benefits of data engineering metrics
Metrics help track every aspect of business performance. However, businesses can't track everything. Identifying the right metrics for your business is the key to realizing the benefits of monitoring metrics. For this, it is imperative to have a clear idea of business goals and objectives. The first step is to Identify which metrics will add value to your business based on the overall goals. For instance, quantifiable goals like increasing the average sale per person by 5% are preferred because it makes it easier to predict what strategies might work and what might not. These goals should be written down somewhere to provide clarity to the data engineering team. Hence, these metrics add value if only businesses are clear about their ask.
If you possess strong analytical skills and are comfortable working with these metrics, you can choose data engineering as a career option. A background in mathematics, computer science and statistics helps, but the best way to get into this career is to earn a data engineering certification.
Over the past few years, several online data engineering courses have emerged to help you get started in the field or switch to it from other roles. The best data engineer certification not only builds your fundamentals in the discipline but also equips you with the right skill set to land the first job. Earning a data engineering certificate online is particularly useful for professionals working full-time or enrolled in a regular course.
Hero Vired’s Certificate Program in Data Engineering helps learners get acquainted with the latest techniques and skills to solve business problems effectively. The program is accredited by the National Skill Development Corporation. It allows you to use data systems to extract, transform and load data into useful information for decision-making. This is perhaps the best online data engineering certification with placement assurance and comprehensive career services to hone your soft skills, help you build a strong resume, polish your LinkedIn profile and much more.
So, what are you waiting for? Enroll in this program today!