Volume, variety, and velocity of data have exponentially increased, and now every company is potentially a data company. Organizations strive to leverage data for competitive advantage, taking advantage of an abundance of tools and well-trained talent – whether from in-house teams or SaaS support – designed to manage this volume and streamline analysis. Why then are data teams facing unprecedented challenges in managing and extracting value with this abundance of information and solutions? The answer is the complexity of today’s data landscape, something that a combination of people, processes, and modern technology can address.
Data teams face a lot in pursuit of managing data.
- It’s not just volume; it’s variety: Teams must efficiently collect, organize, and integrate data from diverse systems and formats, creating a time-consuming and resource-intensive ecosystem.
- Lack of streamlined data access and integration: Teams may struggle with accessing and integrating data from these disparate systems. Siloed data and disparate tools hinder the data flow/workflow and lead to delays.
- Conventional data analysis lags: Slow query performance and data transformation processes delay the generation of insights and value. Even worse, continued (unnecessary) manual processes like data cleansing hamper productivity.
- Continued governance and privacy concerns: Data teams must navigate increasingly more complex regulatory requirements and protect sensitive information. This adds extra pressure on teams before they can ever get insights.
- Talent shortages and employee attrition: Even with more people training for data science-related jobs, technology changes quickly, and talent moves on just as quickly. One study suggested talent tends to stay in a single position for less than two years, which makes consistency a challenge.
How can organizations support data teams? It is possible to overcome productivity challenges with the right support.