As the entire world is embracing a digital-first approach, data management has become an essential part of organizations of every size, type, and industry.
Organizations that are truly capable of gathering storing, and processing data efficiently have a competitive advantage in the industry. According to a recent report published by Experian, titled “Global Data Management Research 2022,” nearly 87% of enterprises consider that digital acceleration has made them more reliable in quality data and insights.
However, Enterprises that do not have an effective data strategy implemented will not be able to make the most out of the data that is generated through all channels.
Technical debts and complexities due to obsolete and legacy architecture restrict the capabilities of the enterprise to adopt new technologies and processes. Gaps in the data architecture will make it challenging for enterprises to monetize the data gathered.
The DataOps teams need to set efficient workflows, tools, and governance policies to ensure smooth data flow throughout the organization.
Here are a few challenges that CDOs should be aware of:
With the advent of industry 4.0 and big data, information is generated in multiple channels, types, and forms. There is a tremendous amount of data that is generated in the cloud and other tools in the enterprise tech stack, such as the Internet of Things (IoT) and edge computing. Many organizations find it challenging to gather the entire data available through DataOps because trying to gather all the available data will increase the administrative burden and costs. This is why many enterprises hinder investing in data management tools in their IT infrastructure. CDOs should consider determining and segmenting data in its initial stages, enabling organizations to prune data faster and minimizing costs.
Data management gaps
The tremendous volume of data gathered and its various types create gaps and make it challenging for data scientists and analysts to evaluate that data to generate valuable insights for decision-makers to access it. Moreover, organizational culture and workflows can result in additional silos because every department has its own objectives for gathering data and need to have governance over the data generated through its channel.
Organizational culture can result in additional silos because competing groups have their own objectives and thus want the ability to keep and control certain data for themselves. DataOps teams need to overcome all the technical and manual challenges to minimize the data management gaps. Enterprises should consider implementing automation tools to set a unified policy mechanism to govern the data throughout the enterprise.
Data security is one of the significant challenges in enterprise data management. Inconsistent visibility throughout the cloud environments and lack of orchestration of different security components make multi-cloud security a challenge. Vulnerable data ecosystems increase the risk of data breaches that might result in financial losses and legal implications.
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Storing the data
As the data today is generated through various sources, it becomes a challenge for the DataOps teams to gather and store data. Moreover, storing data increases the administrative efforts and costs that businesses have to bear to ensure effective data management. Enterprises that aim to have holistic visibility into data storage across legacy and cloud architectures need to have effective data management strategies integrated to ensure success. CDOs should consider modernizing and unifying data management capabilities throughout the enterprise to store the data in a central location.