Robust Data ArchitectureAbstract big data transfer. Binary processing is handled by the computer motherboard. Futuristic processing of server code. Blue technology background with bokeh. 3d illustration

It’s easy for a trained analytic professional to spend more time getting and organizing the data rather than analyzing it and creating reports and insights. Here right tools play an essential role in solving this problem for the data analysts. How can an organization ensure the right tools and technologies? One of the best ways is to restructure your data architecture. 

Data architecture refers to the data strategy to govern data collection, storage, and data usage. An organization should not overlook the significance of having a streamlined data structure to ensure team members have the right tools for data analysis and insights. The recent developments in the data industry, such as modernization of the data warehouse, centralized repositories of the data, and automation of the process, have cut down the cost and manual intervention, making the entire process easier for the data scientists. However, the fundamental step has a streamlined data architecture to start with your integrated data analytics system to save time and resources. Let’s start with a framework to develop a robust data architecture aligning with your business objectives.

It is recommended to break down the data architecture into measurable steps. It’ll help to track and manage each step to create your data architecture for your organization.

Step 1: Do the assessment of the tools and understand how they coordinate 

The first step is assessing the current stock of tools and systems you are using in your organization. Start understanding how they work together and what are the roles they are playing in the current architecture. Talk with the business stakeholders to add or remove tools or find out which tool or system is working fine, and which is causing trouble? We need to find out the 

Step 2: Create a plan for data structure

If your organization is already using a data warehouse, you would be able to get clear documentation of the data you are capturing in your data warehouse. If you find any data missing in the data warehouse, you need to identify the reason. Moreover, you need to ensure whether it is worth adding ad-hoc data sources. In some cases, it is not necessary to add any other data sources if you easily integrate all of them using a reporting tool.

Here are some examples of the data that you may want to include in the data warehouse: CRM, Digital data, email marketing data, customer service data, and third-party data 

Step 3: Determine your Business Objectives 

While moving through the transition, you need to keep your business goals in your mind. Before developing a robust data architecture, you need to ensure that your business goals are aligned with your process. Determine realistic KPIs for all business units. With this process, you can quickly provide the answers to the questions of the businesses to boost your business. 

Step 4: Make sure there is consistency in your data collection process. 

If there is consistency missing in the data collection methods, you will lose the data validation and quality.

For instance, if a particular company changes how they collect the website data, it won’t be easy to produce accurate companies between the data. 

Incomplete data is also one of the most significant issues with data consistency. While having the data ready for the data processing, you need to ensure that all data sources are aligned and documented thoroughly. For instance, having different names of the duplicate accounts, the wed company might be captured as “wed crop,” “wed corporation” and “wed co” on all platforms. It causes a problem for the data analysts to capture the data automatically. 

Step 5: Determine the best Data Visualization Tool

Do you want your stats and data transformed into engaging pictorial graphs that everybody without having tech knowledge understands? You should have a data visualization tool that converts the data and information into insights in a very engaging format. Moreover, you can automate the entire process of converting your data and information into insights. Don’t spend time developing report dashboards rather than use a business analytic and data visualization tool to have your data reports ready. 

Step 6: Reporting and Analysis

Ensure that the reporting process is automated and includes all the resources and KPIs to help business leaders make the right decisions.