By Vikash Kumar, manager at

It’s clear that the new age is entirely dependent upon us. Entrepreneurs have started emphasizing evidence decision making where there are seemingly endless choices that require a bottomless supply of data. Unfortunately, very few have realized the true value of the gold mine they possess.

In the present scenario, organizations are required to make workflow changes on the fly or else your customer or trading partner won’t last long. As a result, organizations find themselves pressured to automate business operations so that each and every activity can be adjusted without any disruption. There was a time when data was processed by either a request-response system or by an overnight batch reporting process. With the help of powerful mainstream systems with a simple user terminal, reports were presented only after the request had been submitted.

As the technology advanced, a major shift took place in the field of data management. Sophisticated intra-day reporting and ad-hoc insights were created to drive maximum business opportunities. Now, due to the three non-functional characteristics— velocity, volume, and variety, streamlining platforms require a different storage and processing architecture. This has led to more complex processing requirements. And that’s the reason why it’s believed that several aspects of data management may not be as sexy as the predictive models and colorful dashboards they produce, but they are pretty much crucial for vital performances. Do you think it’s easy? Probably not or else everyone would be doing that. So what needs to be included in a robust data architecture.

Understanding the Data Architecture

The way data is acquired, stored, processed, distributed and consumed is what data architecture is all about but for data strategy, it means the overall vision and underlying framework of an organization’s data-centric capabilities and activities. It is a safe bet to say that data strategy is an umbrella term featuring all significant data-related policies and principles ranging from data governance to data stewardship, Master Data Management (MDM), Big Data management, etc.

It’s time to start! Your first and foremost step is data transformation where you can kill two birds with one stone—i.e to achieve your business objective in such a way that it scales to further improvement. Apart from this, just make sure that the project you are seeking falls into the below-mentioned categories:

  • Discrete: As the name implies, the project must be sufficiently small that it can even run as a single exercise whose results can clearly be measured.   
  • Significant: Capable enough to solve a current business problem and provide a quick bottom line in such a way that is recognized by business leaders.  
  • Scalable: Everyone seeks success and business owners are no exception. Therefore, it becomes very important for you to know how you will get this project to the next level as soon as its value is proved.  
  • Unsiloed: Of course the end goal of technology is to make sure that data moves easily throughout the organization and this is done in such a way that IT systems or data formats don’t create roadblocks. That vision starts here.
  • Measurable: What’s the point in doing such hard work if the results cannot be measured? Whether it is in terms of increased sales conversions through a specific channel, it’s very important to measure how the project’s IT improvements contribute.

Although data management systems have indeed come as a boon they even have a fair share of controversies as well. Focusing on a single priority can cause companies to stumble on a few potential pitfalls such as:

  • One-Size-Fits-All Storage Solutions—Most of the enterprises have this weird tendency to select a one-size-fits everything approach for both data storage and management. What I mean is I have found several companies storing lots and lots of data, the low tier on their most expensive storage media. As a result, it may increase cost but at the same time also decreases data storage performance. So what companies should be doing is this: admins must put some extra effort into using the right tier of storage for the right application. This will allow one to meet several storage requirements at a lower cost.
  • Unchecked Data Redundancy—- Enterprises have this really bad habit of keeping unnecessary reductant copies of data. When storage costs were high, departments were compelled to prioritize reducing redundancy. Today with the magnitude of data growth, storage requirements, maintenance issues, and security concerns, the cost factor became quite a big concern.
  • Data Protection—Afterthought– Another bad practice is treating your backup strategy as a separate component from storage. Technically, primary storage is considered as the layer of infrastructure that responds to the needs of primary applications, and with the use of secondary storage for backups. Whereas in reality, users, as well as owners, expect no downtime and high performance on a continuous basis. Due to opposite forces, an unknown pressure is created for data management professionals to solve the need for better performance while often ignoring or leaving less time and resources for the backup & recovery component.

So that’s all for now! Overall, data management is not about using every bit of data originating from every source, it’s about making smart decisions that accelerate business growth.


About the Author:

Vikash Kumar is a manager at, a CMMi Level 3 and Microsoft Gold Certified Software Development company that offers custom software development services on diverse technology platforms, like Microsoft, Java, PHP, Open Source, BI, and Mobile.