Data architecture is one strategy that can be utilized to remove obstacles like misunderstandings of the data generation, processing, and transformation. In order to show business professionals the value of data architecture, in this article, we will discuss its benefits and steps of how to build it.
What is data architecture?
Data architecture is a visual representation of an organization’s data structure.
It shows which data is collected, how it is collected, where it is stored, and how it is used and will be used. In doing so, it helps companies govern data in a more comprehensive way and help companies to develop better data-driven strategies.
Why does data architecture matter?
Turning collected data into actionable insights is crucial to driving and accelerating growth. However, data is kept in various storage by businesses. For instance, most businesses store their data in different departmental databases. It causes problems such as:
- Lack of control over the whole business data,
- Data duplication across systems,
- And data inconsistency.
Data architecture shows data flows, such as how data is categorized, integrated, and stored, with models or schemas. It helps make data current, consistent and comprehensive for decision-makers.
Utilizing data collection tools is one of the ways to get accurate data. Bright Data’s Data Collector is one of the data collection tools that completely automates the data collection process. Additionally, you can purchase ready-to-use datasets that enable companies to save time.
4 Benefits of data architecture for businesses
1. Provides a fully comprehensive view of a company
Data has a lifecycle that includes several phases, from data creation to data usage. Having effective management of the data in its different stages of lifecycle requires a unified view of data that flows.
Data architecture provides businesses the most unified and consistent view of data flow including each stage. Here are some crucial insights that companies can derive from data flow diagrams:
- What kind of data is collected,
- How it is collected,
- Where it is stored,
- Who owns or manages data
- How it is used and will be used.
All these enhance data management processes across a business and decrease costs related to data management.
2. Helps companies to make more accurate decisions
According to McKinsey, 64% of B2B companies anticipate increasing their investments in predictive analytics. Their expected return on investment, however, falls short.
The unified and consistent availability of data enables businesses to get more relevant information from data. It helps professionals to take better decisions since data architecture makes data more usable. A comprehensive & unified data source:
- Helps the sales team to understand customers to provide the right products and services.
- Assists sales team in understanding the current customer life cycle
- As the selling process starts with lead generation, it helps companies to generate new leads.
- Helps decision makers prioritize their commercial actions.
The longer the decision-making process for strategic decisions suggests that you are giving your competition greater room to maneuver. Data architecture is supporting decision makers to make fast and effective decisions. Decision-makers do not have to waste time on inconsistent and outdated data since data architecture provides a single source for it.
3. Enables business analysts to improve their processes
Data architecture provides a comprehensive understanding of current business data across systems. It enables businesses to monitor the flow of data between systems and identify changes that occur as data moves.
A comprehensive observation of business data allows analysts to analyze each phase of the data life cycle step and detect bottlenecks. It gives business leaders insight into how overall processes execute and helps to evaluate options to improve processes that exist effectively.
4. Helps CMOs to get more accurate customer intelligence insights
Existing customers are 50% more likely to spend more on products compared to new customers. Focusing on existing customers is more cost-effective than acquiring new customers. Because of this, increasing customer satisfaction and putting the needs of the customer first will boost marketing ROI.
Data architecture enables marketing teams to access and collect the most accurate data within the business. Marketing departments can analyze customer interaction data to gain insights into customer behavior in a consistent way since data architecture provides insights about each data stage.
A detailed and comprehensive data-flow visualization enables marketing executives to understand current customer pain points and give opportunities to improve the customer experience. They can improve their customer journey and prioritize their customer-centric strategies. Better customer experience leads to higher levels of customer satisfaction, which boosts sales.
3 steps of developing data architecture
1. Correct existing data sources & eliminate data silos
When data is not appropriately shared between systems, extracting data from one location and transferring it to another may cause various data issues.
- Understand the root cause of data issues in your data storage,
- Identify and replace common data issues such as inaccurate, irrelevant, duplicate, and not cleaned data.
Silos may arise for many reasons. One of the most common ways is that each department in an organization stores its own data in a different data storage.
- Establish a centralized data storage to present a single view of business data,
- Implement an effective data governance practice
2. Establish a plan for data architecture
- Data architecture strategy must be clearly defined and aligned with the business’s goals and data governance policies.
- Evaluate the potential risks and challenges to gain the ability to monitor and control data.
3. Keep up with data lineage
Tracking data lineage allows companies to understand where the data comes from, and how it is processed and transformed. It provides a map of the data journey including from source to destination.
A quick note: Unlike data architecture, data lineage is a more specific term and is defined as the life cycle of data elements. It focuses on data movement in an organization.
Data architecture, on the other hand, aims to create a framework of models and policies for data management.
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