The Significance of Eliminating Silos from Data Science Processes

Make better use of your data science tools and resources by taking an intelligent approach.

Every company in this digital world wants to employ data science and analytics methods to be the most "data-driven". A study by McKinsey also states that more than 50% of CEOs feel that they're leading their company with analytics. But there's a difference in how companies use data analytics. Some collect data and analyze it periodically whereas others make data analysis a core part of their business processes. It's a general assumption that the volume of data might more or less be the same, but what matters is how quickly a company can turn data into insights. This becomes critical when a company is working with siloed data.

The Cause of Friction

When organizations work with siloed data, different teams, like IT, cybersecurity, software,  will have different their own tools and data sets. Ideally, this should generate multiple data sets with the same conclusion. But there are one too many, there is bound to be chaos and confusion. There are several studies that highlight companies using multiple monitoring tools that do the same thing, one or 11. This causes friction in the overall team performance, adds up additional and unnecessary costs, and affects the project. According to Gartner, only 20% of the analytics projects will successfully finish their tasks by 2022. By then, there will be more such tools in the market.

COVID-19 worsened the situation as cross-functional teams couldn't collaborate easily. This made it harder for teams to working in segregated silos with no proper resolutions in place. In this cut-throat business world, a company's standing in the industry depends on agility. Data can significantly contribute to a company's growth, as long as the silos are broken. How can this be achieved? By arming teams with the large raw data sets first which they can use as a part of their tasks.

According to Iain Chidgey, vice-president of EMEA at Sumo Logic, "Centralizing data handling and analysis, and then getting more teams involved around the one set of data should help companies make more of their data with different teams." If a retailer is running an e-commerce website, its business operations team will be notified of all the abandoned carts. Then comes the urgency to inspect the cause as it directly affects the business. The same data can be used by the IT teams to understand if the dissatisfaction is the result of a poorly made infrastructure like show page load times or a bug that keeps crashing the website. Then the software development team will use this data to find a solution and monitor the website. If the issue was security-related, the cybersecurity and compliance team will take the needful actions. Not only will be approach be cost-effective, but it will also help the website maintain a record of all the issues and the impact it had on the business. It will help generate performance and developmental reports and improve the business optimization process.

The crux of the issue is to eradicate data silos. By doing that, all teams will work in tandem, use the same data source to tackle business problems and more profitable business over time. Whether the teams work remotely or not, a single data set will keep chaos away and generate data across all business domains. If companies want to shift more of their focus towards data science and analytics, this is the right way forward.