A Changing Business Landscape - from legacy to a truly modern & flexible architecture and why document based data models matter
Summary of MongoDB Digital Boardroom - Thursday 16th July 2020
What are the next big challenges you and your organisations are expecting to face in terms of data?
The common data challenges faced by organisations vary depending on the industry, age of the business and their levels of digital/cloud based solutions. These challenges include ensuring foundations are in place for new businesses for data to be flexible as they grow, merging and moving data into the cloud to have it in one place and making sure all employees can access, share and view the data in a usable format.
Also, data needs to become more integrated with the business as technology grows and evolves and the business moves with it, as well as making sure it is accurate and available in a timely manner for regulatory purposes as technology goes in one direction and regulators appear to move in a more granular way with regulations such as GDPR and a change in focus as a result of Brexit.
Legacy to Leading
Data has always been part of computing. From the 1960’s to the present day, computing power, availability and services have evolved and become more accessible, removing structure, then bringing it back and changing with the introduction of cloud and more layers of governance.
With the introduction of Oracle 6, PCs became much more popular and the key parts of what we know as databases where became readily available with all data being stored in tables. However, the systems were quite complex and so few people could understand them. This was fixed with XML even though implementation of it was very time consuming. And then as the amount of data in the world increased the common structures were removed.
Then when MongoDB decided to get out of using tables but needed some kind of structure, the Enterprise Document Model allowed data reporting to be broken down to branch level. Soon after, Multi Model DaaS brought along its own new challenges, however with the use of Data markets they were able to build a central place for data within the organisation.
Lift and shift
Now that people are looking to move their data into the cloud, the notion of moving 15 separate data centres into it is a painful thought. Also, with everything in tables, it becomes hard to merge that data together. Therefore, the use of document models should allow you to merge knowledge points together.
Get it into the funding cycle
As Technology teams are looking to introduce this model into their budget, it is important for them to understand the requirements needed, especially as regulations regularly change and with Brexit causing the focus to change in many areas.
When build risk compliance information is often taken from many sources and in recent times, people’s needs have changed from simply needing reports at the end of the day to needing data available at all times. Therefore, any information that comes from this model needs to be readable for the business and technology sides of the organisation, which can be done by enriching the data with context and clear understanding so it makes more sense for however reads it.
And by enriching public sources, retailers for example can track things like locations and frequency of purchasing to create a holistic picture of the clients, creating bigger and personal profiles that can create better customer experiences in more ways than ever before.
The quality of data
To ensure data remains at a high level of quality, one should look to utilise additional services to cross collate across departments and types of data. In the past, data quality would only be checked via analysis once it was complete. Now, this analysis can be run through the data at any time, creating new streams of data while keeping hold of the original data should it be needed. After all, who can decide what quality looks like? The document model allows you to expand it and see it in all states.
How long does it take?
There is no set time for how long the process takes, but the advice would be to simply test and try it. So much conversation can take place to try and make it work the first time, but in all likelihood, just attempting it once, looking at the mechanics and a bit of trial and error is much more productive. Then, by taking a step back, learning from what you have done or adding to it will help you get to grips with the system, No one solution will fit all as everyone’s data and how they want to use it will be different.
How do you see the explosion in data evolving in the future?
We are already seeing changes in storage with the increase in ‘organic storage’ including 1cm cubed disks that hold 1PB of data. Data is already growing globally by double every 12 months and it is predicted this will grow to every 6 months by the year 2025 and as a result, we can expect data to be retrievable faster in the near future as regulator, employee and customer demand and expectation grow alongside availability.
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