Organizational leaders around the world agree on the importance of data management. However, most companies’ data management programs are still in the planning or ongoing phase. In a 2020 Dataversity report, only 12% of companies were running programs fully, while 38% of programs were still in progress and 31% had just started.
This is because companies are often faced with the implementation of data management. Below are five common barriers that organizations face.
According to a recent study by Gartner², the silo mentality in business areas is one of the most challenging aspects of data management and analysis. Legacy and robust data architectures promote data isolation and prevent information from being shared and disseminated throughout the organization. Heritage architectures also make it difficult for companies to organize information consistently. Isolated and disorganized information makes it impossible to apply data management, whether to detect data derivation, catalog data, or apply a refined security model.
Data control involves monitoring the quality of the data that leaves the company, as well as its use throughout the organization. Data controllers must be able to identify when data is corrupted, inaccurate, out of date, or analyzed out of context. They must be able to define rules and processes easily. The ability to trust data is the foundation for data-driven organizations that make decisions based on information from many different sources. Fifty-eight percent of companies in the Dataversity report said that understanding the quality of source data was one of the most serious bottlenecks in their organizations’ data value chain. According to the report, “Automating and combining business terms with data sources and documentation from derivation to column level is an important step in optimizing data quality”.
Data management requires companies to achieve data transparency: what data do you have and where is it located? But the old systems hide the answers to these questions. Data management is the key to conducting this type of data inventory – having a strategy and methods for accessing, integrating, storing, transferring, and preparing data for analysis. According to Forrester Research, “effective data management comes from the maturity of data management”. However, many organizations face deficiencies in data management.
In addition to the increase in data sources inside and outside companies, data leaks are increasing. Data management is essential to improve data security. Like successful data management, data security depends on traceability: knowing where your data comes from, where it is located, who has access to it, how it is used, and how to get rid of it. Data management defines rules and procedures to prevent possible leakage of confidential business information or customer data so that the data does not fall into the wrong hands. Heritage platforms, however, create isolated information that is difficult to access and track. These silos are often exported, sometimes to spreadsheets, and duplicated to match other isolated data, making it even more difficult to know where all the data is going.
Lack of control over data
Companies often start thinking about data management when they need to comply with regulatory policies like the US GDPR, HIPAA, PCI-DSS, and Sarbanes-Oxley (SOX). In the Dataversity report, 48% of companies see regulatory compliance as the primary driver of data management. These regulations require organizations to be able to locate their data from source to retirement, identify who can access it, and know-how and where it is being used. Data management defines rules and procedures related to data ownership and accessibility. Without this information, confidential information can fall into the wrong hands or be improperly discarded, resulting in financial fines, lawsuits, and even imprisonment.
Snowflake’s cloud data platform provides the right foundation to support data governance programs. Snowflake helps companies break data silos and has features that enable companies to achieve compliance, as well as better decision-making using protected and controlled data. This includes availability in the top three data clouds, elastic storage and computing; data encryption, access controls and tracking features; and integration with external data management tools.