Importance of removing Silos from Data Science Processes


Every company in this digital world wants to use data science and analytics methods to be as “data-driven” as possible. The McKinsey survey also reports that more than 50% of CEOs believe they run their company with the help of analytics. However, there is a difference in how companies use data analytics. Some collect data and analyze it periodically, while others make data analytics a core part of their business processes. In general, the volume of data is more or less the same, but it depends on how quickly the company can turn data into insights.

When companies work with disaggregated data, different teams, such as IT, cybersecurity and software, have different tools and their own data sets. Ideally, the result should be multiple data sets with the same report. However, if there are too many, chaos and confusion will ensue. This leads to friction in the overall team’s work, adds additional and unnecessary costs, and impacts the project. According to Gartner, only 20% of analytics projects will complete their tasks by 2022. By then, there will be more such tools on the market.

COVID-19 exacerbated the situation because collaboration between teams was not easy. This made it difficult for teams to work in separate groups for which there were no suitable solutions. In this highly competitive business world, a company’s position in the industry depends on its agility. Data can contribute significantly to company growth if “silos” are removed.

According to Ian Chidgey, Sumo Logic’s vice president of EMEA, “centralizing data processing and analysis and then bringing multiple teams together into one data set should help companies get more out of their data, and with different teams.” If a retailer runs an e-commerce website, its business operations team will be notified of any abandoned shopping carts. It is then urgent to investigate the root cause as it has a direct impact on the business. The same data can be used by IT teams to determine if the dissatisfaction is due to poorly executed infrastructures, such as page load times or a bug that is causing the site to crash permanently. The development team will then use this data to find a solution and monitor the site. If the issue is security related, the cybersecurity and compliance team will take the necessary action. This approach is not only cost-effective but also helps to document any issues and their impact on the business on-site. It will help generate performance and development reports and improve the business optimization process.

Removing silos of data is key. This will allow all teams to work in tandem, use the same data source to solve business problems and work more profitably in the long run. Whether teams are decentralized or not, a single data set will prevent chaos and create data for all business units. If companies want to focus more on data science and analytics, this is the way to go.

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