How to Develop the Essential Data Architecture for Your Digital Transformation Strategy

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As digital technologies are dramatically reshaping consumer behavior, markets, and enterprises, CXOs must focus on occupying leadership positions or catching up with competition. The ability to deploy cutting edge technologies fast to deliver products and services in ways that were not possible before has become a business imperative. On the other hand, any business that does not have a digital marketing strategy and is not leveraging modern technologies in an integrated manner risks becoming irrelevant to the modern consumer. 

Some of the fastest-growing global brands such as Netflix,  Amazon, Uber, Facebook, and Airbnb are ‘born digital,’ which means that their business models intrinsically draw on the power of digital by design. These and other innovative brands have shaped consumer expectations in a variety of ways. It’s no wonder that consumers have grown to expect that a cab will be available within a few minutes, and will reach exactly where they’re waiting, by just pressing a few buttons. Or that the entertainment platform they choose will recommend shows they are most likely to enjoy. Or that social media channels will deliver the video or other content they’re looking for practically instantly. 

What technology components enable digital transformation, meaning, what should you consider to take your business to the next level? The cool, new ones get the most attention, and while you should acquire those that will give you the edge, it’s also important to consider the data architecture needed to enable them. Data architecture is the essential but often overlooked ingredient of digital transformation.

Data is at the heart of the digital experience 

To understand the need for data exchange and analytics at high speed and scale,  let’s consider how today’s digital consumer shops. She is browsing on social media or a search engine and sees an ad for something that was on her mind. The ad takes her to an e-Commerce site and while she is looking at the product that caught her eye, the site recommends other products that she may like. Once she has chosen her items, she can check when they would be delivered to her address and place her order.  

While she waits for her shipment to arrive,  the site is fulfilling her order by optimizing delivery routes and warehouses. Her order is delivered, and her choices are also analyzed by the e-Commerce site to offer her products or deals in future.

Shopping this way is possible thanks to the data that is being analyzed practically in real-time, for example,  her browsing history is used to decide which ad should be displayed to her at a particular moment. Real-time analytics and predictive modeling are at play to generate recommendations, drawing on data from multiple systems such as current session search, user profile, and purchase history. Order fulfillment is optimized by analyzing her address and the locations of warehouses to work out the best route and schedule of the delivery.

Retailers that are successfully fulfilling customer expectations and continuously innovating with marketing, design, distribution, support, and more have achieved true digital transformation, built on scalable, connected, and smart models. Some traditional businesses have adapted successfully, embracing an omnichannel approach and integrating digital and physical processes. This physical and digital integration is no longer limited to sales channels but extends now to marketing and even supply chain management processes. 

An integrated view of clients, inventory, and promotions facilitates the effective functioning of online and physical operations. Data flow across business functions and decisions driven by real-time analytics are essential for true digital transformation. When we consider the volume and velocity of data, the critical role of data architecture in enabling these new business models comes into sharper focus.

Data architecture challenges

1. Complex technology landscape

When business leaders conceptualize new ways of working,  they can draw on the best available technologies. A cloud environment is essential for digital transformation projects. Artificial Intelligence (AI), machine learning (ML), and robotic process automation (RPA) capabilities are being increasingly adopted to automate a variety of processes. Big data and advanced analytics are practically essential to gain insights from the huge volume of data generated and enhance the speed of doing business. The internet of things (IoT) is also extremely relevant to track processes, assets, and infrastructure.  
When you chart your road map to adopt some of these technologies and create your digital transformation strategy, the journey must start with an assessment of legacy technology and data architecture. That is your starting point and will help you to map the substantial restructuring needed to build the capacity, security, scalability, performance, and usability that your digital business processes will demand. You need to plan for the applications and infrastructure that will support your chosen technologies and process the volume of data they generate. 

2. Defining requirements

Before you can build or purchase any data or technology components, you need to define the requirements for all types of users. These include functional and non-functional requirements and must consider technical, operational, and support needs. An example of a functional requirement is that users must be able to see the entire portfolio of stocks at a glance and get real-time updates about losses and gains. Non-functional requirements define identity and access rights, security, performance, availability, reliability, scalability, configuration, user experience, etc. Digital transformation projects are often enterprise-wide and not limited to specific functions, so defining requirements can be challenging.

3. Big data

Big data is invariably an essential component of digital transformation, meaning that insights from data steer the transformation roadmap and your business strategy. For this reason, your data architecture must support the ability to collate data from multiple sources and systems, ensure data quality, perform analysis, present reports, and visualizations.

4. Load

Do your business processes involve large volumes of digital media such as images, audio, and videos? This has become very common today, and your networks, infrastructure, and applications must be designed to handle this load. Your system may also have to be prepared to handle large loads if you have a huge number of users or products.

5. Mobile computing

Data architecture today must also plan for mobility as customers and employees interact with your systems using various mobile devices such as smartphones and tablets. In order to ensure the best experience to all stakeholders, mobile technologies need to be integrated into your systems. 

Creating the right data architecture

The power, speed, and flexibility that a cloud environment offers help organizations innovate with business models and reduce time-to-market. It is practically impossible to imagine digital transformation without cloud technologies. How these will be deployed is no longer a question for the IT function alone, but is considered a strategic business directive and discussed by CXOs.

Computing power on the cloud enables high-speed data analytics and the ability to handle larger volumes of data. Cloud services allow you to ramp up or scale down computing power and infrastructure as needed,  leading to cost optimization and business agility. Application deployment, computing power, and storage capacity become available on-demand and practically on a pay-as-you-use basis, minimizing capital expenditure. The cloud environment provides the flexibility to share data amongst different functions and deploy system resources as required. 

You may have migrated some of your databases and applications to the cloud, and your digital transformation strategy may require others to be migrated. If you are using a combination of on-premise and cloud infrastructure, then data synchronization must be planned and managed. You can also choose a cloud-first architecture approach, in which processing, management and storage of multiple applications are all on the cloud environment, and data integration is again happening on the cloud.  

Your data architecture may be based on a public or private cloud, or a combination of the two. Larger enterprises can afford the investment in a private cloud, while public clouds usually work better for smaller businesses. 

Many companies are now using a hybrid cloud approach with multiple public cloud service providers as well as their private cloud. Different providers such as AWS, Google, Azure, and others could be used for specific services. As these cloud providers have different standards, policies, processes, and APIs, your application must work on these different operating environments. This challenge is overcome by a process called containerization.  A container is a package that bundles an application and everything that it needs to work, like configuration files, libraries, utilities, etc. Containerization enables you to run applications seamlessly across multiple operating environments. 

You can optimize your investment in cloud resources by identifying idle resources and scaling them down as far as possible. You should also identify low demand and high demand zones in your applications and ensure that your data architecture is mapped accordingly. Distribute workloads between public and private clouds to get the best performance and cost optimization.

With the right data architecture in place, you can boldly embrace true digital transformation and continuously innovate with your business models. You can usher in a culture of data-driven decisions taking advantage of the availability of insights in real-time. You can also collaborate with customers and business partners to maximize opportunities. 


Speak to experts at Treehouse Technology Group to design a bespoke digital transformation roadmap and data architecture based on your existing assets and future plans. 

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