Data science and its use are exploding in just about every technology and business arena. As consumer-grade technology rapidly reaches unprecedented capabilities for capturing, storing, and analyzing data, many industries are scrambling to understand, apply, and profit from our increasingly data-rich landscape.
Suppose you work with data or plan to incorporate its use more robustly. In that case, it’s imperative to have a good understanding of how data architectures work and whether they might be a strategic option for your organization.
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What is Data Architecture?
“Data” is a widespread, highly general term utilized in various ways. What we’re exploring here is “data architecture.” Data architecture is a specific system, tool, or set used to process large amounts of data.
While many tools and systems exist that are designed for data use, data architecture refers specifically to storing, handling, and analyzing data that amounts to a terabyte or more.
Computing capabilities steadily increase year over year, but the amount of data most entities can conceivably collect or access has ballooned into volumes that have outgrown many conventional data processing tools. These data use cases require robust systems.
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Part of what has contributed to this reality is the diversification of data types that it must incorporate into data processing:
Structured data refers to what we’d more traditionally think of as data – information that you can express in text and numbers.
Unstructured data refers to information conveyed in different formats. These can include video, audio, and more. These data types are often made up of much larger files and thus require more space and processing power.
Data architecture systems can work with vast amounts of data so that conventional data storage or processing tools are not.
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Components of Data Architecture
To understand how data architecture works, it is essential to understand first the elements of how architectures are constructed and, second, the process by which data architectures accomplish their purposes.
The data architecture system is composed of a few different parts. The physical place where data is stored could be a single server, a server cluster, or a cloud computing location. Data processing and analysis can be accomplished by a single tool or done separately through integrated software systems.
And finally, implementation of that data, and the insights it affords, may also be accomplished by one of the tools in the previous step or might involve additional ones. This can sometimes require an integrated family of tools that could range widely in scope and purpose depending on the application and needs of the organization.
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You can organize data architecture into “layers” that define its process one step at a time. Utilizing a data architecture system moves data through these five stages:
1. Data sources
It refers to where data is born or acquired. Types of data range widely depending on the organization’s style and needs. Sources could include an organization’s internal or web-based database(s), documentation, reports, bookkeeping records, social media accounts, email, marketing materials, email or mailing lists, Customer Management System (CMS) data, curriculums, scanned documents or images, other file types, video footage, audio files, scanner or sensor information from IoT sources, and more.
In addition to data created or generated by the organization itself, data sets can include obtained or purchased from other sources. This might consist of user behavior information, directories, customer demographic information, contact lists, case studies, research data, public domain data, and more.
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2. Data storage
Data storage dictates the system(s) that house your data. An additional consideration is the programming language(s) that will facilitate your process from start to finish when designing data architecture. Databases and processing tools utilize a variety of programming languages.
Another critical element of data storage is security. Cyber security and encryption are vitally crucial to your data architecture. They must be chosen wisely, especially when storing sensitive information (e.g., health records, credit card numbers, home addresses, sensitive documents, confidential materials, and more).
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3. Data massaging
Data cleaning will often include gathering data together if stored in multiple places. It may consist of searching for duplicate, incomplete, or damaged records or data entries and repairing or deleting them to avoid causing problems during the analysis stage.
Cleaning or massaging can include converting unstructured data types to forms that you can analyze alongside structured data. Unstructured data requires a NoSQL or Hadoop Distributed File System (HDFS) database to support these varied file types.
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Analytics refers to the process of transforming raw data into usable insights. This can be accomplished using a wide range of tools and strategies. Business Intelligence (BI) platforms specialize in analyzing data for various corporate applications. They almost always include robust data visualization features and dashboards and are most compatible with structured data.
Other, more specialized tools are used to analyze unstructured data types. These might include audio or photographed text recognition, video analysis, artificial intelligence tools, or other software.
Consumption refers to the final step: the act of applying, integrating, or implementing data-driven insights that have resulted from data’s passage through your data architecture.
Like the previous steps involved in this process, how consumption works vary widely from organization to organization based on a particular entity’s unique needs and structure.
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This could range from scheduled reports delivered to a board room or team to data released as marketing messages or branding efforts. Deliverables could range from aesthetic charts and graphs to lengthy write-ups or publications.
Depending on the size of your entity, you may have hundreds of stakeholders involved in the process that range from executives to managers, employees from any and every department, and even individuals outside your organization. It would help if you took great care to ensure the deliverables your data architecture produces will create value.
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Current Trends in Data
These are important to consider as you weigh what data structures your organization could best employ.
A. Data storage and management
Data storage and management is a rapidly expanding and developing field because data volume increases in just about every application. As technology rapidly grows in storage and processing capability, hardware/software constraints are lessening. New or adapted data storage and management methods constantly adapt to changing abilities and demands.
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B. Data quality
Data quality is an increasing concern in many industries. As data predominantly or exclusively dictate more and more decisions, algorithms, automated processes, and more, the implications of incorrect, missing, or corrupted data become increasingly stark.
C. Data discovery, visualization, and application
Data discovery expands the horizons of possibility by making new types of data or places to collect it possible. These can be interactive or used in conjunction with other technologies like virtual reality to render and communicate data learnings like never before.
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Driving Innovation by Using Data Architecture
The nature of data architectures can make them powerful tools to drive the advancement of innovation for your organization. There are several ways you can put data architecture structures to work to advance just about any area of your business.
Data architecture can allow your team to make decisions based on real-time learnings. When you craft a system that analyzes data and creates insights you didn’t have available to you previously, you and your organization can react in ways and speeds never before possible. This allows you to capitalize on current trends, anticipate needs, or tailor your service offerings in an unprecedented fashion.
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It can also make personalized services and marketing available for your business. This can create significant differences in your customers’ experiences while interacting with your brand and make whole avenues of products or services possible that were not before.
As another example, utilizing data architecture can revolutionize your business’s internal processes. You can use data architecture to remove bottlenecks, disseminate information at the right time or to the right people, expedite inefficient operations, or eliminate entire processes.
Data can change how your team operates and significantly improve efficiency and effective decision-making wherever thoughtfully applied and utilized.
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Tips for Building Your Data Architecture
Are you a decision-maker in an organization? Whether a sole proprietor, a small business, or a large company, you are never too large or small to benefit from the application of data.
But if you currently run analysis processes that exceed 100GB of data and take hours to run, or if the data you’d like to process exceeds your current systems and capabilities, a data architecture could be an excellent next step.
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Another reason you might consider a data architecture is if your organization deals with large amounts of unstructured data or requires large amounts of processing power to make your data workable.
Another important consideration before designing your data architecture is the amount of time and resource it requires to set up a data architecture appropriate for your operation and the ongoing expertise and time it will need to maintain and run.
After considering these aspects of a data architecture undertaking, your organization is ready to implement a data architecture. Here are a few tips to streamline your process and ensure a good, effective fit.
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Know What’s Out There
Boxed solutions are widely varied and can accommodate many user types. They can be expensive – and some companies prematurely opt to build their own from scratch.
But development can sometimes prove just as expensive, if not more so, due to the uncalculated upkeep and maintenance. They may require overtime to keep them operational and up to industry specs. It’s worth your time to know what ready-made solutions are available to you.
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Security is Important
Choosing between a cloud-based or hosted service and between public or private servers is essentially a security consideration. The sensitivity of your data is an essential factor and should inform your decision process.
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Ask the Experts
Data architecture is highly complex and involves many moving parts. Especially when starting, don’t be afraid to pay for an hour or two of a consultant’s or expert’s time to make sure you’re not making faulty assumptions or missing essential parts of the process while constructing your data architecture.
When done well, data architecture systems can create fundamental change and make way for substantial innovation within your organization.
Hello Friends! I am Himanshu, a hobbyist programmer, tech enthusiast, and digital content creator.
With CodeItBro, my mission is to promote coding and help people from non-tech backgrounds to learn this modern-age skill!