Imagine what your organization would accomplish if it had accurate, detailed information regarding the processes, products, market, and customers.
Well, this is the age of big data, and it demands organizations to bring data engineers and scientists on the same page to construct efficient and accurate insights to gain a competitive edge.
A relevant solution - data operations - was coined back a few decades back but the last 5 years have been proven to bring significance understanding and working in the field.
DataOps, or data operations, is an emerging discipline in the data science field which brings data scientists and engineers together to provide organizational structures, processes, and tools for a data-focused organization.
What is DataOps?
As Gartner defines, DataOps is a practice of collaborative data managers that focuses on improving communication, automating data flow and integrating 3rd party applications for managers as well as the other consumers of data within the company.
The main aim of DataOps is to eliminate the causes of miscommunication between stakeholders and developers. It is one of the most recent agile operation methodologies directed towards big data professionals. DataOps focuses on improving the data management process which not only enhances the accuracy but also the efficiency of analytics, including quality control, automation, integration, and model deployment.
How does it work?
As discussed earlier, the main goal of DataOps is to combine agile methodologies in DevOps to align and manage data with the goals of the business. For instance, an organization’s business goal could be to raise the lead conversion rate; in such a scenario, DataOps can position data to make recommendations regarding better marketing of products to convert more leads. On the whole, building new codes is just one part of DataOps; it also includes improving and streamlining the data warehouse.
DataOps Processes
When it comes to data analytics, majority teams fail because they focus solely on tools and people while forgetting the processes. It is important to understand that the process of gathering useful data is where the main emphasis is required. This is where DataOps comes in since it is a combination of processes and tools that enable high-quality results with accuracy and efficiency. The working of DataOps is derived from various other fields which are as follows:
DevOps Approach: DataOps is one of the methodologies derived from DevOps - a popular software development approach - which applies continuous analytics on data models. In the case of DevOps, operation specialists and software developers work under agile methodologies to manage quality control in software development. However, in the case of DataOps, operation specialists work with data scientists - under agile methodologies - to specifically work on data and its associated solutions derived from communications apps and analytics software.
Agile Development: Just like utilized in DevOps, DataOps goes under agile development which is an iterative methodology of project management that aims to complete software projects efficiently while reducing errors to minimal. DataOps, itself, modifies the entire data lifecycle in Agile data engineering processing since it ads scaling, verification and monitoring to data pipelines after the data has been put into operation phase. The main idea is to build operational concepts within data analytics pipelines to get data that is ready to be put in production - and is available for broader use.
Lean Management: Conceptually, lean manufacturing is mainly a pipeline process in which raw material enters the manufacturing floor, flow through the various workstation, and exit as completed products. The process of DataOps is very similar to lean manufacturing since DataOps uses SPC (statistical process control) to verify data analytics in the pipelines continuously. The final goal is to ensure data quality and keep check of errors.
Building a DataOps Team
In a DataOps team, there are three key roles; which can either be performed by a single person in case of a smaller organization, or more than three people in case of larger organizations. The common requirement for all these roles is to have an understanding of data analytics. So, without further ado, the following are the three key roles required:
Data Scientist: The main work of a data scientist is to research and answer open-ended queries based on the insights that are driven via the results of data warehouse analytics. Data scientists are required to have domain knowledge and expertise so that they can create new algorithms and models for the solutions based on insights.
Data Analyst: A data analyst provides analytics - based on data warehouses - to stakeholders. The main focus of this role is to synthesize and summarize big data sets to create visual representations of data. Doing so allows data analysts to communicate information in a useful manner so that useful data insights are extracted.
Data Engineer: The role of a data engineer is to lay the groundwork for performing analytics on a huge amount of data. Data engineers mainly move data from operational systems - such as ERP systems or CRMS - into data lakes and then write the code which populates data schemas into data warehouses. A data engineer also implements tests on data to ensure quality control.
Please note that building a DataOps team doesn’t require new specialists. a data scientist or a data engineer or simply anyone with data training can be entered here. The important part is to focus on improving the collaboration between efficiency, better use of people’s expertise and of course, skill sets.
Final Words
Although traditional data management techniques have remained adequate and relevant in the past for static data sets, their time has now ended. Now we have big data - containing greater volume and higher complexity - which calls for significant manual efforts alongside the traditional techniques. This is why we need a better solution - an advanced tool - just like DataOps.
As discussed in this article, DataOps is all in for managing more data, curation needs of SMEs and technological advancements - all singlehandedly. The bright future of any organization demands them to not only adopt but also master this new approach.
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