The Need for Flexible Data Management: Why Is Data Flexibility So Important?

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Click to learn more about author David Leivesley.

We live in an era of big data. Amazingly, statistics show that around 90 percent of this data is only two years old. That indicates that businesses, moving forward, will need to anticipate using more and more of it.

However, Data Management and structuring are notoriously complex. Further studies indicate that only around 5 percent of businesses feel they have Data Management under control. For industries such as sports, education, and hospitality, numbers are always rising.

Data flexibility is becoming a significant concern. While automated data services are coming into the broader mainstream, it is Data Quality, too, which is concerning to big business. Big data will be worth at least $274 billion by 2022. Therefore, it stands to reason that it is worth managing and processing such data correctly.

What are some of the common challenges companies face with data flexibility? Why is flexible Data Management so crucial for the years to come? Let’s take a closer look.

More Flexibility = More Scope for Analysis

Data, of course, plays a central role in analytics. Marketing, usage, and budgeting, for example, all rely on data pooling to some extent. However, how companies use and manage this data may impact the quality of their reports.

There are two rival concepts with regard to big data. One is the data warehouse, where information is stored in a refined and logical fashion. It may, for example, be akin to a library organization system.

The other is the data lake. This, as you may imagine, is more flexible. It is where data is free to move around, much like the molecules in water. Data in a warehouse faces pigeonholing and a lack of movement.

In previous years, data warehousing has proven useful. This type of data filing may, on the surface, appear organized. However, there are arguments that data is not so easy to file away.

A data lake model considers the statistic that at least 80 percent of data in the corporate sphere is free moving. This statistic is, perhaps bizarrely, more than 20 years old. Some may argue that data is freer moving now than ever before.

A management platform based on a data lake model gives analysts unlimited scope. At least, that is the hope. While some organization is healthy, data warehousing through outdated systems can choke your possibilities. Companies may benefit from a more flexible platform, offering you access to historical data as well as current numbers.

More Flexibility = More Scalability

It may be an obvious point to make, but scalable Data Management is immediately scalable. Data in the value of up to 2.5 quintillion bytes is generated every day. Further research shows that data storage will need to make room for more than 44 ZB worldwide.

A ZB is a zettabyte. For scale, a single zettabyte equates to approximately a sextillion bytes. This is a truly incredible number, and if statistics are worth believing, the numbers are getting bigger.

Therefore, corporate Data Management must be scalable. The education system must make room for new students every year. The sports industry must make room for ticket sales and stadium space. The healthcare industry perhaps has it hardest, as there are roughly 250 births per minute.

Data Management must be flexible so that it does not reach a maximum “breaking point.” An outdated data warehouse system will only stretch so far. A lake, however, will keep filling and filling.

More Flexibility Now = More Flexibility Later

As some sources suggest, the best Data Management systems keep the user in mind. In the case of corporate data, the user is, of course, the consumer. Different people have different roles to play and also have different user preferences.

An excellent example of this may be how people browse the internet. Estimates show that at least 90 percent of internet users can browse via mobile web. That has a huge knock-on effect on marketing, consumer outreach, and more besides.

A flexible Data Management plan will, therefore, adapt automatically to changing demands. Even if tastes and usage patterns change drastically in the years to come, companies with data lakes will be ready.

Some commentators believe that fighting change is a quick route towards obsolescence. Therefore, it may be in the best interests of global companies and organizations to instead “meet” change. This could be a key reason why artificial intelligence, for example, appeals to 90 percent of business investors. AI is actively helping to automate and auto-scale Data Management for the years ahead.

Keeping Data Clear and Flexible

Data Quality relies on accuracy as well as flexibility. We are always discussing ways in which data cleaning and management shifts are helping companies meet new demands. Big data is not something to fear. In fact, many agree that its growth is inevitable.

Common problems facing big companies and public bodies include data duplication. There is also the matter of outdated data providing users and consumers with poor quality experiences. In the healthcare industry, in particular, this could prove to be disastrous.

What You Need to Do

Rather than enclose all of your data into tight spaces, we encourage flexibility and broader thinking.

Cleaning data and upgrading your management system means looking at the bigger picture. You must be certain that the data you retain is not only accurate but relevant, too.

As statistics show, big data is growing. For companies and public services, this means embracing a more flexible platform as well as mindset. Many household names and leading corporations are taking strides to embrace the future of flexible Data Management systems.

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