A fast, reliable flow of actionable data is mission-critical to many enterprises. Yet, many companies are failing in their efforts to become data-driven.
According to an executive survey of fortune 1000 companies by New Vantage Partners. Only 24% of respondents thought their organization was data-driven in 2020- a decline from 37.8% the year before.
These organizations experience a significant barrier to unlock their data for insights with their conventional data pipelines. Data explorers must have specialized skills to navigate the programs and tools that make up their pipeline, and their tech stack lacks critical components to create success.
6 Limitations of the Conventional Data Pipeline
While conventional data pipelines can technically get the job done and gather and connect the information to provide insights, they are still too complicated to be an optimal solution. The limitations can be detrimental to business success.
1. Conventional data pipelines & data stacks fail to facilitate data culture.
We know many companies are failing in their efforts to become data-driven, according to New Vantage Partners, but why? 92% of respondents attribute the principal challenge to becoming data-driven to people, business processes, and culture. While only 8% of executives surveyed claimed technology was the challenge. According to Gartner, culture and data literacy are the top two roadblocks for data and analytics leaders to create a data driven environment.
While the technology exists to perform the necessary analysis. The tools are too complicated to be widely used and accessed and fail to breed data cultures.
2. The entire process requires specialized skills and knowledge.
From creation to analysis, the entire pipeline must be thought through carefully and set up by individuals with specialized skills.
These individuals, such as data engineers or data analysts are not only required to have a high level of problem solving and mathematical ability, they must also understand programming languages such as SQL, Python, Java, and more to connect data and programs within their tech stack.
3. You run the risk of starting from scratch time and time again.
Whenever you create a specialized solution that requires a particular skill set you also run the risk of spending a large amount of time setting up the process each time reports have to be run or losing knowledge as individuals leave the team or company. All knowledge of how or why a data pipeline was set up in a particular way is gone and your team starts the analysis process again from scratch.
4. Conventional data pipelines are expensive to set up and maintain.
As you set up an in-house data pipeline, you must consider the cost of all the programs in your data stack as well as the human resources required to run and maintain the pipeline. The more complicated the data stack, the more the costs accumulate to maintain it.
First, you have to source and keep personnel with the technical skills required to make a data stack work. Then there is an extensive process to determine the path you need to take, working cross-departmentally to ensure you are looking for the right tools for the right type of insights and negotiating contracts, and getting buy-in for each tool in the data pipeline. Then your team is committed to a specific way of processing data that requires personnel to run and maintain within the programs’ use and ensure that they are processing data correctly and that each tech and renewal are accounted for.
Then there is training on internal teammates to use the data to facilitate data-driven decisions and repeating the process as needed for when requirements shift for data insights.
5. Slow to react to business challenges.
To make data digestible, you need to ensure that it is accessible. Even as technology has been evolving, the process of ingesting data and gleaning insights has been relatively limited. It requires a lot of manual labor to get data prepared for processing. By the time data and records are updated, the data is out of date.
6. Lack of automation, standardization, and monitor-ability.
By creating a complicated data stack run by a dedicated team you rely on individuals to do all the work involved in facilitating insights. Every time there is a change in your project, your team is going through an intensive process just to get your data/visualizations, etc. updated to perform your data analysis on the changes.
The major drawback to data platforms is how they require complicated coding knowledge in setup, exploration, and maintenance.
The reality is that the process to get actionable insights has been lagging for a long time. But we have reached the point where how we interact with data is rapidly changing thanks to no-code interfaces. It’s now possible to create a single data pipeline and make it easily accessible to drive data-driven decisions. See how you can achieve a single data pipeline.