So many companies have undergone a significant digital transformation over the past two years. For starters, they now need to support shifts in remote work, automation, and even how and where customers interactions happen. As a result, the IT infrastructure of America’s companies is under more strain than ever. This has kicked off a cloud migration boom and accompanying data modernization efforts.
Brands must deal with managing, protecting, and acting on rafts of new data. The digital landscape is forcing more and more business leaders to harness their organization’s data in a way that produces competitive advantages as well.
For this reason, cloud migration initiatives have become a top priority in many IT departments. Mauricio Vianna, as CEO of MJV Innovation, a consulting firm tasked by Fortune 500 companies with supporting their cloud migration initiatives and which recently hosted a virtual summit on the topic, sheds light on how companies are tackling it.
What is the driver behind a lot of Cloud migration/modernization initiatives that companies undertake?
Mauricio Vianna: Traditionally, organizations have focused on transaction data and reports extracted from legacy databases. This is no longer the current scenario, with the arrival of new types of data, especially unstructured ones, such as images, audios, comments on social networks, email content, and data from sensors and smart devices. Conventional databases cannot handle large volumes of data, especially unstructured ones.
This is where the infrastructure and tools contained in data modernization come into play. As a first step, data modernization complements legacy databases with other types of databases such as Big Data, Data Lakes, and NoSQL databases. This brings new advantages in terms of scalability, cost reduction, and ease of use compared to conventional data infrastructure.
It’s important to mention that legacy databases and data warehouses are still present in the corporate infrastructure of companies. This still occurs because some databases are very well structured to work with transactional applications. However, the infrastructure used in data modernization provides the means to develop applications based on Big Data, generating new business opportunities and new revenue lines.
What are the biggest challenges companies should prepare for during a Cloud migration boom/modernization program?
MV: There is still no silver bullet on how corporations allocate their Cloud and data modernization budgets. However, some practices help companies in decision-making to modernize their data in the Cloud environment.
It’s always vital to avoid migrating data to the cloud without planning. Companies should invest in creating a data migration strategy for cloud. It is also paramount to reengineer the processes implemented with cloud data source/destination.
Business Objectives in the Foreground
Often, companies, especially in the IT area, are so excited to automate data processes and implement them on a large scale in the cloud that they forget about practical issues such as the costs of the cloud environment.
That’s why it’s always important to analyze what should be migrated, always prioritizing what can add value to the business. For this, stakeholders need to be involved in the project from the beginning and be aware that they are the decision-makers in these cases.
One of the main goals of a data modernization project is to generate insights and reports that the traditional model cannot provide.
Therefore, it is important to invest in data science and machine learning techniques. You’ll want to extract maximum value from large structured and unstructured data volumes. The knowledge that can be extracted by combining this data with the already known data of companies can provide prescriptive and predictive capabilities for companies that are not possible with the traditional BI model alone.
Investments in education, training, change management, and data modernization projects will not succeed unless stakeholders engage. They must participate in the right way through training that enables a new stage of maturity in data analysis.
It’s important to consider change management given the impact that data modernization can have on the company, affecting the daily activities of several employees.
Can you explain some of the most important benefits gained from the cloud migration boom/modernization?
MV: Data modernization has a host of benefits to the business such as:
- data collection and processing with great speed and efficiency;
- analytics with real-time data access and low-cost machine learning application; and
- security and data governance with more reliability, high availability, and generation of backups in different world regions.
- This makes it increasingly difficult to collapse through an invasion.
The Role of Cloud Computing
Cloud computing is currently the primary enabler for data modernization as it offers several attractive features.
Cloud computing infrastructure allows companies to access a vast amount of computing resources needed for applications based on Big Data, for example.
Cloud infrastructure provides the means to automatically provision more or less computing resources needed to run a given application. There is no longer a need for manual intervention to provision or de-provision resources.
Pay for Consumption
With Cloud, companies can pay for exactly what they consume in terms of computing resources, and this creates flexibility and avoids a high capital and investment cost.
Tools as a Service
Cloud providers offer a range of tools to use in your data modernization strategy — data management tools and even machine learning tools — as services.
This greatly facilitates the modernization of data and the automation of applications that consume this data. It allows companies to have access to the necessary tools without the bureaucracy of conventional software licensing.
Updated and Automated Data
Cloud infrastructure allows high-performance implementations and automates the data transport process with great speed and scalability.
Facilitating the consolidation of data in a single repository, creating analytical workflows, making the data available for analysis with a frequency close to the real-time in which the transaction took place, generating faster responses for the business.