CIOs and IT department heads across Southeast Asia are well aware of the importance of modernizing their company’s data and becoming a data-driven business. But it’s often not until they begin the process that the challenges and complexities become manifest.
Interestingly, these challenges are often more budget and people-centric than technological – in a survey of IT personnel at medium to large companies, Deloitte found the most common obstacles to data modernization to be budget concerns, lack of understanding of technology, lack of consensus among decision-makers, and an absence of clarity around success metrics.
Therefore, planning for data modernization is not limited to choosing technologies and migrating data, but requires thoughtful planning and coordination throughout every branch of an organization. That’s why engaging a cloud partner will be essential at every stage of the data migration and modernization process.
In this article, we will walk you through what that process looks like step by step to help you adequately prepare your business for data modernization.
Step 1: Discover & Evaluate
The first step in initiating data modernization is about discovering and assessing your data situation and can be broken down into four areas:
Set your business objectives
Cooperating with a cloud partner on data modernization is not a one-size-fits-all process – every company will have its own unique needs and objectives. Your first task will be to map out what you aim to achieve and the expected impact on your business.
Take the time to clearly identify your objectives, key stakeholders, and KPIs for the program. Consider the impact the program will have on your business, plan for the changes, and identify who will lead the management of those changes.
Arrange your organizational structure
Conway’s law states that the way teams are organized determines the shape of the technology they will develop. Similarly, the way a company arranges its team structure will have a significant effect on whether a data modernization program will succeed.
According to research from McKinsey, relying on fixed internal structures such as IT or cloud teams can lead to siloing of efforts, with each team building different cloud capabilities for themselves without designing them for application by other teams, which can create slowdowns. Cross-team communication and alignment are therefore crucial to data modernization.
Determine the state of your legacy data warehouse
This is where technology enters the picture. As you prepare to migrate your legacy data warehouse to the cloud, you and your cloud partner will need to conduct a thorough audit of which systems, architectures, and processes need to be optimized. You will also need to perform an extensive analysis of your existing data in terms of which data is being used, who is using it, for what, and where is it sourced.
Define and prioritize your use cases
A use case in data warehousing refers to all of the datasets, data processing, and system and user interactions needed to create business value. Defining and prioritizing potential use cases will be vital in aligning your data modernization to your business goals.
According to Google Cloud, a use case is generally made up of the following:
- Data pipelines that ingest raw data from various data sources, such as customer relationship management (CRM) databases.
- The data stored in the data warehouse.
- Scripts and procedures to manipulate and further process and analyze the data.
- A business application that reads or interacts with the data.
Step 2: Design
Once you’ve laid out your plan and taken stock of your current data warehouse, the next step will be to work with your Cloud Partner on designing your strategy:
Design your capability architecture
The term capability architecture refers to a group of simple statements describing what your business wants to achieve in language that is relevant to all departments. For your data modernization, it will be extremely helpful to design several statements to unite the company around common goals for the program. Some examples might be:
- To make our company into a truly data-driven business
- To make better-informed decisions on how to grow our company
- To make data management easier and more efficient for everyone
Design your data modernization strategy
This is where you design the criteria for which technologies you will adopt as well as lay out all of the technical details of your plan and overall data flow architecture. Your cloud partner can provide strong guidance on this part based on their experience and knowledge.
Design your data governance strategy
Data governance refers to how your organization approaches the management of data throughout its lifecycle, from acquisition to application to finally expunging. For example, a data governance plan might include information such as what kind of data you have, where it came from, and who is currently managing it.
Data governance is an extremely important part of any data modernization program. By laying out your data governance strategy in advance, you can help to ensure that all teams throughout your company are using data in such a way that meets your standards for data integrity and security.
Step 3: Data Migration
According to Deloitte research, cloud migration and data modernization are not two trends operating separately – rather, they are two processes that support and overlap each other. How you conduct your migration will therefore have a tremendous impact on the success or failure of your overall data modernization.
It’s important for you to have a clear understanding of the different types of migration before you proceed. Here, we will focus on data warehouse migration and database migration:
Data Warehouse Migration
A data warehouse is not only made up of a database but critical components such as data pipelines, queries, and business applications needed to fulfill your company’s workloads. We advise taking an iterative approach to data warehouse migration, as breaking it down into smaller tasks makes the process easier, decreases risk, and creates opportunities to learn between iterations.
Database Migration
Database migration means exactly what it says – moving data from a source database to a target database. Clients that previously accessed the source database are moved over to the target database, with the source database being switched off. Beyond simply migrating data, database migration also presents an opportunity to redesign your database architecture as a performance optimized database.
Your cloud partner can assist you to decide which forms of migration are necessary and how to implement them.
Step 4: Data Validation & Optimization
Once you have successfully migrated your data into a cloud architecture, you now have the capability to use that data to generate new value for your company, such as business intelligence, AI/ML prediction, and even API monetization. For example, Google Cloud Data Warehouse BigQuery has built-in BigQueryML which can create and execute machine learning models to help clients segment customers and predict the lifetime value and conversion rate of a customer.
Besides, as a recognized Google Cloud premier partner in Southeast Asia, CloudMile assists our clients to implement Business intelligence tools to make better data-driven decisions. This process includes visualizing the data via integration with tools such as Google Data Studio (officially renamed Looker Studio), Looker, and also Tableau.
The Benefits of Data Modernization
Data modernization is a huge endeavor that extends beyond the IT manager or CIO’s office into all aspects of your business operations. However, the benefits of having a real-time data pipeline flowing smoothly through every part of an organization will quickly show themselves, from data analysis and AI/ML prediction to making data-driven decisions that put you ahead of your competition.
Get started on your data modernization journey with CloudMile by clicking here.
