The Four Stages of Data Modernization for Digital Transformation
Digital Transformation (DX) is crucial to every business, from small-scale enterprises to medium-sized businesses to multinational corporations. Our world is becoming increasingly digital, and how a company carries out its DX initiatives determines its competitiveness and relevance today.
Since the term ‘digital transformation’ means different things to different businesses, it is hard to define it. However, in general terms, digital transformation is the integration of digital technology into every key area of a business that impacts the fundamentals of its operations and its value delivery to customers.
Beyond that, DX is a cultural reform in which enterprises continually challenge the status quo and get comfortable with trials and errors. This also means that the business organizations should walk away from legacy business operations on which the company was built and embrace new business practices.
What Is Data Modernization?
In today’s digital Information Age, unimaginable volumes of data get generated every second from countless devices and sources in structured and unstructured formats. The mixture of structured data (such as documents and spreadsheets) and unstructured data (blog posts, videos, and social media comments) brings storage and data processing challenges.
Unfortunately, most organizations struggle to put this enormous data into effective use without realizing two essential things. First, their legacy data architectures stand in the way of generating deep insights for effective decision-making. Second, data modernization is the key to unlocking the limitless potential of data processing.
Data modernization helps enterprises move siloed data from legacy databases to modernized cloud-based databases or data lakes. Legacy systems have several inefficiencies, complexities, and bottlenecks. An enterprise that embraces data modernization eliminates those obstacles and turns into an agile one. So, data modernization is the foundation of digital transformation in an absolute sense.
The Four Stages of Data Modernization
Let’s explore the four stages of an efficient data modernization process toward successful digital transformation.
- Data Migration
Data migration is the first step of data modernization and most DX projects. Unfortunately, in most cases, the professionals complicate the process or tend to prematurely transform the data before data migration. Instead, they could have performed a lift and shift migration: a process that allows quicker data migrations and lets organizations leave legacy systems faster.
- Modernization of Data and Application
After the data has been migrated to the cloud, the data and application modernization phase commences. As it is carried out on the cloud, this phase enables a wide range of capabilities that are difficult to attain from on-premises (on-prem) systems. Some examples are real-time collaboration, easily accessible data sharing, and a more straightforward yet more informative Business Intelligence (BI) dashboard.
- Implementation of Modern Analytics
Data modernization empowers an enterprise to obtain more meaningful insights from data. And with modern analytics, an organization can learn more about their customers, identify customer behavior patterns, and make more informed decisions.
In addition, connecting multiple data sources to cloud-based modern analytics is simpler than connecting on-prem databases to similar data sources. Cloud-based data pipelines are easier to build and smoothly navigate through problems such as data gravity that on-prem databases fail to deal with.
- Apply Artificial Intelligence and Machine Learning for Innovation
Businesses have been using Artificial Intelligence (AI) and Machine Learning (ML) to overcome several business challenges in recent years. The last stage of data modernization is to unlock the innovation potential of AI and ML.
Some AI/ML use cases include manufacturing companies using AI/ML solutions to reduce waste by AI-based predictive maintenance. In addition, AI/ML solutions have been used by organizations to create customer profiles, learn more about customer behavior patterns and devise marketing strategies based on these profiles and forecasts.
Sailing Smoothly Through Data Modernization
According to a study conducted by Statista, 34% of respondents confirmed that their organization had fully implemented data modernization technology, while 50% stated that their organization is currently undergoing the implementation process.
Data modernization may still be a daunting, time-consuming process for some, even after breaking it down into four stages. It is a fact that if conducted in a standalone manner, the data modernization process wastes tons of your time and resources. The lack of continuity is one of the crucial challenges most enterprises face when implementing data modernization.
The best way to overcome this challenge is to shake hands with an Information Technology (IT) consulting firm like GoDgtl. With a focus on helping clients transform their off-line organizational activities and legacy business processes, we can help you modernize your data stack and enjoy modern and rich data experiences.
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In the current business world, data is the most valuable asset, and a disaster involving data loss results in several irreversible damages to an enterprise. Those damages include the loss of revenue, productivity, reputation, and even loyal customers. Disasters and their severity are hard to predict. Anyway, you can control how you respond to a disaster, and your response determines how successful your enterprise will recover from it.
The adoption of cloud computing services has been rising since the COVID-19 pandemic. A recent survey revealed that 67% of enterprises adopted cloud infrastructure by the end of 2021. Cloud computing services deliver on-demand Information Technology (IT) services from storage to applications to processing power via the internet on a pay-as-you-go basis. An enterprise can access all those IT assets by paying a fee rather than owning IT infrastructure or data centers.
Here, let’s take a look at Cloud Disaster Recovery (CDR) and how you can use it to your advantage.
Cloud Disaster Recovery: What is It?
CDR is a cloud-based service that quickly recovers your enterprise’s critical systems after a disaster and gives you remote access to your IT resources in a highly secure virtual ecosystem.
Conventional disaster recovery involves managing a secondary data center, a time-consuming yet expensive process. However, cloud disaster recovery has transformed the status quo by eliminating the need for traditional IT infrastructure and bringing down downtime significantly. According to the 2021 Data Protection Report by Veeam, the average downtime cost is roughly $85,000 per hour. And, the cost entirely depends on the size of the business organization – the larger the size, the larger the cost, and vice versa.
The Working of Cloud Disaster Recovery Explained
To understand the working of cloud disaster recovery, we should compare it with conventional disaster recovery. As mentioned earlier, the crucial element of conventional disaster recovery is a secondary data center where you can store all redundant copies of critical data and to which you can fail over workloads.
A conventional on-premises disaster recovery system generally includes the elements mentioned below.
- A dedicated ecosystem facilitates IT infrastructure, including maintenance, employees, and computing devices.
- Adequate server capacity for higher levels of operational performance that also allow scalability depending on the business needs.
- Internet connectivity with ample bandwidth allows remote access to the secondary data centers.
- IT network infrastructure, including firewalls, routers, and switches, are implemented to provide data availability and a reliable link between the primary and secondary data centers.
Conventional disaster recovery is often too complex to manage and monitor. More than that, maintenance and support of a physical on-premises DR site can be costly and time-consuming. For example, the expansion of the server capacity of an on-premises data center can only be done by purchasing additional computing devices and IT resources, which demands a lot of money, time, and effort.
The Advantages of Cloud Disaster Recovery
Cloud disaster recovery can effectively deal with most issues of conventional disaster recovery. Some of the advantages of CDR are mentioned below.
- Eliminates the need for a secondary on-premises physical site and the purchasing of additional hardware and software to carry out critical operations.
- Scalability of IT resources per the business needs
- The affordable pay-as-you-go pricing model requires you to pay only for the cloud computing services you use.
- CDR can be performed in minutes from anywhere in the world over any computing device connected to the internet.
- The backup of data across multiple geographical locations eliminates the possibility of a single point of failure. Even if one cloud-based data center fails, you can still retrieve a backup copy of your critical data.
- Cloud-based state-of-the-art IT network infrastructure ensures that the cloud services provider quickly identifies and rectifies any issues or errors. Moreover, the cloud service provider provides 24/7 support and maintenance of the cloud storage and consistent updating of hardware, software, and cybersecurity features.
Disaster recovery in the cloud is increasingly becoming a popular choice for small and medium-scale businesses looking to implement a robust business continuity strategy. With CDR, setting up a separate data center for backup is no longer required. Moreover, there is no need to install and maintain separate DR tools, which brings down costs while providing businesses access to continuous, scalable DR services.
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