Data Modernization Trends: Keep these in mind

According to Deloitte, data modernization and data migration to the cloud are synonymous. To give an example, the transition from legacy systems to new cloud-based business intelligence (BI) solutions is a sign of data modernization. This is something that many businesses are considering right now for a variety of reasons, including security and cost savings.

However, this transition necessitates the creation of a detailed migration plan. What method will a corporation use to transfer data? How will the information be organised? How will a business be able to track data?

Setting goals and expectations for what the firm wants to achieve is the first step in migrating data to the cloud. For instance, how much data does the organisation intend to transfer, and at what point will the amount of data to be moved be reduced? Who will take over the project’s duties and responsibilities? What is the budget for the company, and what cloud platform will be used? The planning phase also includes code development and effective cloud migration strategies. The smoother the relocation process, the more well-planned it is.

Today’s organizations rely heavily on data for decision-making. These new-age data users, who aren’t only business intelligence experts, might make up the majority of an organization’s workforce, which is only growing. Considering this, the automated metadata management platform must be user-friendly in order to modernise data. As a result, the user interface must be so basic that users, regardless of their professional expertise, can readily access data.

Having discussed about the importance of data migration to the cloud for BI, let’s look at some of the data modernization trends.

Data Modernization Trends to look out for

Trend #1 – Uniform Implementation

When migrating to the cloud, the entire organisation must approach data in the same way. Business intelligence teams must teach data concepts, such as how to categorise, insert, and access data, so that the entire organisation can communicate in a common language. The analytics created from the data will be inconsistent if two departments use different categories for the same data. It’s also critical for data teams to teach the rest of the firm how to use and analyse data so that it may be put to the best possible use.

Effective data managers, on the other hand, strive to move the correct data to the right place as they transition their data. Rather than merely scaling up their existing data storage solution, data managers are increasingly attempting to turn the data into more valuable configurations. This shift does more than just storage. Data managers strive to improve analysis, interaction with other systems, and other aspects of data management. Instead of replicating local storage for 20 subsidiary offices, a company might construct global repositories based on data classification, such as worldwide marketing, operations records, logistic records etc

Trend #2 – Multi-functional adoption / Wider adoption

Markets that have previously recognised the value of data migration, such as banking, financial services, and insurance (BFSI), are expected to continue to employ it heavily. Similarly, marketing departments across sectors are expected to maintain their market leadership in data migration adoption and use to strengthen their data analysis capabilities.

However, as the benefits of data migration become more widely recognised, new businesses and professions are seeing the most rapid growth. Human resources is expected to be a fast-growing industry, according to Markets and Markets, as HR professionals seek data migration to integrate multiple data sources for improved data analysis.

Similarly, retail and consumer products are expected to expand the most, as companies strive to better understand customers throughout the product and customer life cycle. As the benefits of data migration and data analysis become more widely acknowledged, we can expect this shifting demand between industries and job functions to continue. While we may not be able to forecast which segments will expand the fastest next year, we can surely expect change to continue.

Trend #3 – Automated storage and storage scaling

Large organisations quickly exhaust the capacity of local hard drive arrays due to the general increase in data volume. Data managers must juggle frequent requests for more capacity while also staying ahead of hardware obsolescence and failure. Migrating data to the cloud will not totally solve these problems, but it will alleviate the urgency and complexity.

The hours of installation, configuration, testing, and troubleshooting necessary to install physical hard drives are readily replaced by mouse clicks on the cloud interface. Scaling storage is simple and perhaps automated, which appeals to data managers and drives the trend of moving more data to the cloud.

The largest corporations continue to exceed their current capacity. Markets & Markets predicts that demand for data migration in total dollars will continue to be dominated by these huge enterprises. However, as the cloud’s flexibility becomes more generally acknowledged in 2022, small and medium-sized businesses are likely to drive the fastest-growing section of the data migration market.

Trend #4 – Capitalizing on Integrational features

Companies that are using cloud for their applications are paying close attention to the integration offerings of cloud vendors. For example, Azure’s integration capabilities are becoming increasingly popular among Azure platform customers, and some organisations have begun to build lightweight integration frameworks on top of AWS’ offerings.

Rapid change will always have an impact on technology; it’s the essence of the business. Still, there’s no disputing that we’re in the midst of a generational transformation, with businesses pivoting faster than ever to stay ahead of the pack. Although digital integration is not new, its impact on a company’s long-term performance and importance has never been greater.

Trend #5 – Data restructuring and refined data transfer

Previous data migrations may have merely shifted data from one repository to another, but with AI, data migration cycles are more optimized. As such, data analysis becomes more efficient and effective. Data modernization, which entails migrating data from legacy databases to modern databases, is driven by this need. Unstructured data is categorised and structured, and old databases are restructured and refined, thanks to data modernization. According to a recent Deloitte poll, 84 percent of respondents have begun data transformation initiatives, with the financial services industry leading the way.

Companies who were among the first to migrate data to the cloud are now actively involved in data migration for data modernization. This contributes to the established industries’ status as market leaders in data migration. Marketing departments, for example, may have been the first to migrate data to the cloud for data analysis, but they are now also leading the way in data transfer from one database to another. Thus they are seeking the benefits of scalability, cost savings, flexibility, performance, and extended capability.

This might interest you: 5 Tips For Successful Data Modernization

Wind-up

We recognise that adopting modern technologies may help firms remain competitive and nimble as they seek out flexible, scalable data infrastructure solutions. Those who have yet to modernise, on the other hand, will face an uphill battle in extracting insights from their data. According to a recent IDG poll, more than 90% of data professionals claimed it is difficult to make data available in a format that can be used for analytics.

Companies should continue to improve their data architecture and keep an eye on industry trends and cutting-edge technology to help them compete with data insights. On-premises and legacy tools, on the other hand, were never designed to handle enormous amounts of data in a flexible and scalable manner. As new trends drive significant changes in data management strategy, businesses must prioritise modernization.