DataOps and You: 4 Substantial Benefits for Any Data-Driven Business
Every minute, 69 million Messenger and WhatsApp messages, almost 700 thousand Instagram stories, and 500 hours of YouTube videos are streamed, shared, and uploaded on the internet, according to Statista. As we generate and consume more and more data, leaders of all industries seek answers to their business challenges in data science.
But promising as it may be, data science is not always guaranteed to be the perfect solution. For example, 85% of AI projects fail, mostly due to miscalculated preliminary assessment, the dissonance between business and data needs, and an excessive focus on data modeling instead of the problem itself.
That’s not to say that you should discard data science. On the contrary, it offers tremendous potential to improve business performance and enhance decision-making. However, since the process of extracting valuable insights from large volumes of information is extremely prone to mismanagement, the trick is to set the right priorities and navigate the traps that await on the path to success.
How to achieve this? One effective way is by applying DataOps and understanding how this segment of data management services can remove barriers.
DataOps — essential facts
DataOps (Data Operations) is a methodology focused on streamlining and automating data analysis. It was conceived to answer the ever-growing volumes of business information by applying agile principles to data management. The philosophy operates in multiple fields: data science, DevOps, and data engineering, and its main goal is to improve the quality of data analytics while optimizing and speeding up workflows throughout the entire data lifecycle.
All that wouldn’t be possible without the effort of qualified professionals. Due to the multidisciplinary character of the field, DataOps specialists must possess a gamut of both technical and soft skills. The roles involved fall into three categories:
● Data suppliers — This role is often assumed by data administrators, who process and manage information and data access across the organization.
● Data preparers — A group of roles that adapt the supplied data to the needs of consumers. Data engineers (responsible for creating efficient data pipelines), curators (who adjust data to the business context), and stewards (tasked with data governance policies and compliance) all belong to this group.
● Data consumers — They work with refined data assets used by data scientists to invent new ways of overcoming business challenges.
If all this sounds a little bit complicated, that’s because data itself is becoming increasingly complex. But the ultimate goal of DataOps is to facilitate data operations across the board, helping your organization get the most out of them in the most efficient way possible.
Here are four key advantages of embracing the DataOps methodology to maximize data value across your business.
#1. Improved data quality
Data quality management is the paramount goal of DataOps since it determines the outcome of all subsequent processes. Unfortunately, low-quality data puts even the best-managed projects at risk.
AI, ML, predictive models — they all depend on quality data. When information is corrupted, duplicated, or incomplete, these technologies become unreliable and essentially useless. If you’re lucky, an error resulting from low-quality data is spotted in time and can be fixed. In most cases, however, inferior data quality can lead to serious consequences, including compliance breaches, missed business opportunities, and customer complaints and attrition.
DataOps helps to avoid these issues while improving the accuracy of all data-based analytics in your organization. Data suppliers and preparers primarily guarantee the satisfying quality of data assets. However, they are not the only data owners — in the DataOps framework, all stakeholders are responsible for data quality, ensuring high standards throughout the pipeline.
#2. Better data distribution
In some industries, it is enough to ensure data flow between a single system and the touchpoints. Nevertheless, most verticals rely on information gathered and exchanged within a complex software, apps, and programs ecosystem. Think manufacturing, for example, with MES, SCADA, ERP, maintenance software, QA systems, and an IIoT infrastructure. Retail similarly depends on BI, EDWs, stock management software, predictive customer analytics, and other types of software.
Traditionally, as the company’s data environment grows more complex, it also gets more decentralized. As a result, each new application makes the maintenance and restructuring much more difficult, resulting in integration downtime or failures. DataOps resolves this by redistributing integrations from the code of individual systems to a single central hub. From there, they can be easily maintained, automated, and deployed without the risk of breakdown or lack of data access.
#3. Faster, more efficient processes
While being legally obliged to follow strict data privacy policies, organizations from regulated industries are also among the most progressive DataOps adopters. The reason is simple: entities subject to stringent data protection laws and practices are more likely to have dedicated data engineering teams already in place for compliance. Additionally, they are more inclined to invest resources into data.
Let’s take banks, for example. Applying the DataOps methodology helps them streamline creditworthiness evaluation and instantly provides personnel with all the necessary details to make decisions about customer accounts. Additionally, the reporting processes benefit from automated data pipelines and greater data transparency, while quick iterating and deployment allow adding new features to the banking app and platforms swiftly. All these benefits can be extended to other industries, regulated or not.
#4. Tighter collaboration
At its core, the DataOps methodology pursues the broadest possible interaction between all members of an organization. The goal is to disrupt siloed data and make it widely accessible, enabling teams to cooperate despite the barriers or distributed work, departmental divisions, and competing goals and KPIs.
On top of enabling collaboration, a standardized approach to data governance also helps breed innovation. For example, access to free-flowing data allowed a team of three engineers at Spotify to develop the Discover Weekly feature in just a few weeks, which wouldn’t have been possible with a more rigid data governance policy.
Bottom line
No matter what industry, data is here to stay as the dominant factor shaping the future of business. Consequently, the DataOps approach becomes essential for the success of today’s data-driven enterprises. By providing all parties with a bird’s eye view of a connected data ecosystem and enforcing the right use of information, it helps organizations reap the full benefits of information analytics. These include standardization of tasks, increasing project velocity, ensuring compliance, strengthening security, and much more.
