In today’s economy, speed is a key driver of competitive advantage. How quickly you get your data ready for analysis can directly impact your business’s success. But infrastructure and processes are not always able to handle these demands, as many of our current systems and processes were not built to meet today’s demands.
This is of course a technical challenge, but technology alone will not solve it.
Getting to best practice data use requires effective processes as well as tools. So where can companies start when it comes to overhaling their processes and maximising the value of their data?
The trouble with data: where and how are companies struggling?
The explosion in data volume across nearly all parts of work and society is creating expectations of availability, speed and readiness. However, most existing infrastructure was not built for modern data volumes, and as a result many companies continually find they are battling with needlessly slow data applications.
Traditional infrastructure using batch and extended cycles are often not up to the task. Neither are the legacy processes and siloed approaches that some organisations have become accustomed to, where data scientists and analysts are separated from line-of-business teams.
As a result, businesses everywhere are suffering from a data bottleneck, resulting in a huge disadvantage for enterprises as they cannot derive the insights they require in time to act upon them. As speed of insight and analysis becomes a vector of competitive advantage, companies with faster access to their data will be more successful and gather even more relevant data, resulting in a virtuous cycle that could be catastrophic for those who cannot keep up.
How can DataOps help?
While many enterprises are suffering from a data bottleneck, there remains huge untapped potential in their raw data. Improved processes are just as important as technology for accessing this value, which brings us to DataOps. While it has been around for a while, DataOps has seen a spike in uptake recently as data governance and analytics has become a vital source of competitive advantage.
As a methodology rather than a product or a platform, DataOps is a set of practices for building data and analytics pipelines to meet business needs quickly. As these pipelines become more complex and development teams grow in size, organisations need better collaboration and development processes to govern the flow of data from one step of the data life cycle to the next. Quicker processing from data ingestion and transformation to analysis and reporting is becoming essential to operations. DataOps can make this happen faster by streamlining the process.
The underlying idea for DataOps is inspired by the DevOps movement, from the software engineering world, which bridges the traditional gaps between development, QA, and operations so that technical teams can deliver high-quality output at a faster pace. DataOps brings together stakeholders across the data landscape, from data architects and engineers to data scientists and IT operations workers who build, maintain and model the data infrastructure.
How streamlining your data will benefit the bottom line
The key commercial benefit DataOps affords businesses in the data-driven era is speed, where consumers expect real-time experiences and where business advantage can be measured in fractions of a second. By automating and simplifying data delivery, DataOps acts as a great leveller for teams skilled at different levels of data literacy.
Users are freed to ask deeper questions sooner, thanks to automated, repeatable requests and AI technologies offering visualisation options for a given data set. It also reduces processing costs as teams can reach insights more quickly, helping them to stay ahead of the competition.
In this way, using DataOps opens up the use of data across the business, which helps encourage a culture where data can drive decisions across even large enterprises. These decisions will also be more collaborative, with the responsibility for data insights no longer concentrated among data engineers or data owners.
Making teams more aligned on data-driven decisions also improves the quality of decisions, which can have benefits from customer satisfaction to cost savings.
The value of faster data insights
In every era, speed has given businesses a competitive advantage. But in our current data-driven era, where consumers expect real-time experiences and where business advantage can be measured in fractions of a second, it’s more valuable than ever. Companies who are not able to analyse what their customers are looking for and their own abilities to deliver against this will fall behind.
Bringing together people, processes, and technologies to optimise data pipelines in line with the considerable demands of the modern economy helps get data ready for analysis, monetisation and productisation.
Using DataOps to streamline data processes and drive collaboration is helping open the door to better customer intelligence and new business opportunities, offering companies the chance to stand at cutting edge of their industry.