How modernization of data environment is helping to accelerate business outcomes
Modernization of the data platform: What does it mean?
The attitude towards the word “Data” is changing rapidly from that’s just employee/ sales data to expecting a straightforward answer for “what is my sales revenue last quarter?” with help of BI systems. Modernization of the data platform is becoming one of the underlying digital transformation drivers for every business.
Data platform modernization can be a kick-starter for digital transformation. Achieving a modernized data platform by following a proven method and platform, to source all the data that matters to the business and deliver enterprise-wide intelligence is the key.
Explosive data volumes over the last years and obsolete systems with no ability to support data growth are one of the reasons why businesses should look after data platform modernization. Data systems are changing rapidly, and long-term support of platforms is ceding. Once the database vendors stop supporting older versions of their databases, and enterprises running those systems will have no choice but to upgrade to the latest releases if they wish to have continuous technical support.
Any business needs to stay in line with the market is by adapting a process where data and its platforms are concurrent with growth.
Why modernize thedata platform?
On whatever the data platform business is running, it’s always necessary to ensure that the data platform is secured and reliable for business needs. With the ever-increasing amount of data available, companies are gaining much better insights into what customers want, what they use (and how), how they purchase goods, and what they think of those goods and services. And this information can be used to make better decisions across all areas of the business, from product and service design to sales and marketing and aftercare.
Here are six issues you should consider as indicators to modernize your platform
If your reports and business intelligence queries take too long to refresh or run
Higher infrastructure costs to run, maintain, and low levels of operating efficiency
Have no current secondary site to deliver high availability or disaster recovery
If you are struggling to maintain and patch the variety of database versions currently in operation
Delayed application development due to difficulty in creating and configuring test and development environments
Database developers struggling to co-ordinate development across large or geographically dispersed teams.
Four benefits for your business upon modernizing the data platform
You will be able to transform data into reliable information
You witness an improved performance, optimized costs, and improved flexibility.
Improved security for yours and your client’s data
You will gain real-time analysis with the help of data warehouse and big data solutions that scale on-demand
Getting Started on Data platform modernization: Modernization Framework
Modernization of data platform is defined by new technology, cost, legacy data platform integration, new data types, flexibility, application integration & data security. The long-term success of any enterprise-wide modernization initiative depends on the methodology or a framework customized as per the individual business needs and fitment.
Organizations can develop a well-defined governance structure for managing and curating growing volumes of data, implement security measures to protect against cyberthreats, and define processes to continually maintain data quality.
Organizations should also ensure they have a proper team in place with the right mix of resources, such as individuals with technical, analytic, communication, and business acumen.
Assess & Plan: You need to investigate and develop a fact-based prioritization and sound business case to optimize value and minimize risk
Architect & Build:
Establish an “as is” baseline of systems and processes. Then identify transformational elements needed for the “to be” scenario.
Leverage leading-edge tools and methods to accelerate code and data migration, refactoring, architecture modernization, and mobility of data.
QA & Deploy: Incorporate quality by design, performance, and latency management and tuning.
Maintain & Evolve: Integrate innovation with the modernization lifecycle to optimize the portfolio landscape.
Data platform modernization challenges
While organizations are implementing their digital transformation strategies, many are experiencing setbacks. To succeed, organizations must have the scalable and ever transformative architectures in place to address market demands and stay ahead of the rest. Here are a few challenges that you may encounter while modernization.
1. Lack of documentation Some organizations lack proper documentation for their business rules and incremental changes made in the legacy code. Without the documentation, extracting the business rules from legacy code is risky that some of the business rules will be missing.
Also lack the tools, strategies, and methodologies necessary for a successful data platform modernization project can be a major challenge for any organization. Most data platform modernization projects may fail due to a lack of standard methodologies, frameworks, templates, and guidelines.
Enterprises need an appropriate questionnaire and templates to arrive at the best decisions for data modernization. They must choose an appropriate data modernization tools and replacement database(s) along with the skills for data conversion and data migration.
2. Cloud costs incurred with data modernization An enterprise that is running on any version of the SQL server and wishes to migrate to the cloud, a few scenarios will change unlike on-premise when compared to the cloud. The key difference will be in the pricing structure. If on-premise enterprises will pay for the database as a package wherein the case of cloud, they need to pay based on usage of data space and time spent on the server. Here, developers need to optimize the cloud space utilization by using minimum time to complete development on the cloud.
Enterprises may not afford the costs if they are having large volumes of data (might be of decades-old) to be migrated to other versions or to cloud. The phasing approach may help them in this case. There is always a scope of arising technical challenges while migrating. Data redundancy, data duplication, data merge, and data integration issues must all be addressed.
3. Data security challenges Ensuring data security is one of the biggest challenges that businesses face. Data might be on Premise or Cloud; the data security comes first for any enterprise. Ranging from low risk to high risk, there will be many security challenges to be checked and fixed. The risk of cyber-attacks is high for cloud data platforms when compared to on-premise. New ransomware is being created every year targeting top businesses. According to the 2018 State of Cyber Resilience, If the software solution had been the same and around for years, the cyber attackers most likely had enough time to get familiar with the code and find vulnerabilities of the software.
4. Post-data modernization challenges Business users must be trained on the new systems and applications impacted by data modernization. Training should not be avoided to reduce expenses. It is necessary to ensure the adoption of the applications and systems and to ensure that users can properly use and manage the new database tools and applications. Also, enterprises will incur costs associated with information changes related to the change management program.
How Technovert can help you:
To leverage innovative technologies, enterprises need faster access to data, seamless data integration services, and highly scalable, flexible, and low-maintenance databases. Unfortunately, many enterprises continue to run legacy databases, which fail to meet these requirements. These organizations need to act fast and modernize their data and applications sooner rather than later.
The key to a successful data modernization project is to partner with a company like Technovertthat has experience working with a variety of clients and has a proven data modernization strategy, methodology, framework, and roadmap.