Today, organizations are collecting data from a variety of new and old sources. As the volume and variety of data increases, so does the complexity of data management. With so many options available to manage data, businesses must choose the right solution for their organization. Data architecture is one such solution that supports an organization’s business objectives. It defines standards to manage data as well as operationalize it to maximize its value while minimizing risk. Let’s take a look at why this is important and how modern data architecture impacts business outcomes.
What is Data Architecture?
Data architecture is a set of rules and guidelines for regulating how data is collected, stored, and processed. It’s an organization’s blueprint for managing data, as well as a road map for managing architecture change. Data architecture is a top-level data management strategy that includes a variety of components. These include data governance, data modeling, data security, data analytics solutions and a data integration strategy. The components work hand-in-hand to define standards to manage data as well as operationalize it to maximize its value while minimizing risk. By following an organization’s data architecture, teams can ensure that data is collected, processed and stored in a standardized way. This ensures that data is accessible to all teams. It also ensures that the data is reliable and consistent. In addition, it enables data scientists to perform complex analyses.
Why it is important for Modern Businesses
Data is the lifeblood of any business. According to Gartner, organizations will produce about 2.5 quintillion bytes of data annually by 2020. This is an increase of 50% from an estimated 1.9 quintillion bytes in 2018. The volume of data alone is enough of a reason for businesses to optimize how they manage data. However, it’s also important for businesses to follow a modern data architecture. This is because modern data architecture addresses the challenges associated with the volume, variety, and velocity of data. These challenges include unstructured data, data silos, and the need for new skills. Let’s take a look at each of these challenges. Unstructured data – Unstructured data refers to data that is not organized in a consistent format. For example, images and videos contain a variety of metadata. However, the metadata is not consistent across the board. This makes it challenging to integrate with other data sets and use it in algorithms. – Data silos – Data silos refer to the practice of organizations storing data in silos. This means that data is not shared across departments. This results in an inconsistent customer experience across channels. It also means there is less data to analyse and make better business decisions. – New skills – As data becomes more complex, organizations need new skill sets to manage it. This includes data scientists, data engineers, and data analysts. This is because there is a need to process unstructured data and apply algorithms to generate insights.
3 Key Elements of a Modern Data Architecture
- Data governance – Data governance refers to the rules and processes that regulate how data is collected, stored, and shared across teams. It’s focused on optimizing the value of data and mitigating risk. Data governance ensures that data is accurate, consistent, and accessible to all users.
- Data security – Data security refers to the practices and procedures an organization implements to protect data. It’s focused on minimizing the risk of data breaches. Data security includes policies and procedures that govern who can access data and where it is stored. It also includes solutions to protect data in transit and at rest.
- Data analytics – Data analytics solutions refers to the use of algorithms to explore data and generate insights. It is focused on extracting value from data. Data analytics includes tools and techniques to store, clean, and transform data. It also includes algorithms and machine-learning models to analyse data and generate insights.
Benefits of a well-designed data architecture
- Improved collaboration between teams: Having a standardized data architecture allows organizations to collaborate more effectively. This is because teams use the same tools, share the same data, and use a common language when discussing that data. This enables teams to work more closely and communicate more effectively.
- Improved data quality: A well-designed data architecture reduces the risk of inconsistent data. It also reduces the likelihood that data will contain inaccuracies or be incomplete. This is because teams follow standardized processes for collecting data and applying quality checks.
- More insights and insights: A data architecture allows businesses to analyse more data and use more sophisticated algorithms to generate insights. This results in better decision-making across departments.
Key takeaways
Modern data engineering solutions is important because it addresses the challenges associated with the volume, variety, and velocity of data. With data architecture, organizations can optimize how they manage data and generate more insights by following a standardized approach. Modern data architecture requires organizations to adopt a collaborative approach to data management. It involves governing data and the use of technical solutions to store, transform, and analyse data so that it’s accessible to all users. To build a modern data architecture, businesses must follow a standardized approach to data management. This includes data governance, data security, and data analytics.
Author: Muthamilselvan is a Team Lead in Digital Marketing and is passionate about Online Marketing and content syndication. He believes in action rather than words. Have 7 years of hands-on experience working with different organizations, Digital Marketing Agencies, and IT Firms. Helped increase online visibility and sales/leads over the years consistently with extensive and updated knowledge of SEO. Have worked on both Service based and product-oriented websites.
