How to Implement Big Data for Your Organization in the Right Way

Let’s start with an introduction to what IT across the globe calls “big data.” From a use case perspective, few terms are so overused and hackneyed as big data. Some people say it’s the entire data in your company, while some others say it’s anything above one terabyte; a third group argues it’s something you cannot easily tackle. Well, in essence, big data is a broad term applied to data sets so large and complex as to obsolesce traditional data processing systems. This means new systems have to be implemented in order to get a grasp of such large volumes of data.

One might ask what benefits can be drawn by analyzing such deluge of data. The answer to that can be quite broad. Businesses can draw huge benefits from big data analytics, and the primary and the most sought-after of them all is key business intelligence that translates to high profits.

Traditional business intelligence systems can not only be revamped but also made more beneficial by the implementation of big data systems. Look at Google’s Flu Trends, which gives near real-time trends on flu across the globe, with the help of the search data that Google captures from its data centers. This certainly qualifies as big data, and with its help, Google is able to provide an accurate worldwide analysis of flu trends. This is one of the major use cases of big data analysis. When it comes to your organization, a big data analytics implementation can make all the difference in profits. Many organizations have identified that just by implementing a recommendation engine, they are able to perceive huge difference in sales. Let’s look at some key best practices in implementing big data.

1. Analyze Business Requirements

As the first step, you need to know what you’ll be using big data tools for. The answer to that is your business requirements. You need to gather all business requirements and then analyze them in order to understand them. It’s important that all big data projects be aligned to specific business needs.

2. Agile Implementation Is Key

Agile and iterative techniques are required for quick implementation of big data analytics in your organization. Business agility in its basic sense is the inherent capability to respond to changes rapidly. Throughout implementation, you may see that the requirements of the organization will evolve as it understands and implements big data. Agile and iterative techniques in implementation deliver quick solutions based on the extant needs.

3. Business Decision Making

Big data analytics solutions reach their maximum potential if implemented from a business perspective rather than an engineering perspective. This essentially means the data analytics solutions have to be tailor-made for your business rather than being general. Tailor-made business-centric solutions will help you achieve the results that you are looking for.

4. Make Use of the Data You Have

A key thing that many organizations need to do is understanding the potential of the data they already have. One of the reasons why this is essential is to keep up with the ever-increasing competition out there. Gartner survey says 73 percent of organizations will implement big data in the next two years. The other 27 percent will lose out in competition, for sure! According to IDC, visual data discovery tools will grow 2.5 times faster than the traditional business intelligence market. You already have an edge if you make use of the existing data in your organization. This is a key business decision that you need to make.

5. Don’t Abandon Legacy Data Systems

Abandoning expensive legacy data systems that you may already have in place may be a big mistake. Relational database management solutions will not end their race soon, just because a new smarter kid is in the block. Although RDBMS may be erstwhile, the cost of complete abandonment may be large enough to render it unnecessary.

6. Evaluate the Data Requirements Carefully

A complete evaluation of data that your organization gathers on a daily basis is essential, even if big data implementation is not in your immediate business plan. A stakeholder meet that clearly consolidates everyone’s opinion is necessary in this.

7. Approach It From the Ground Up

One key thing that you should not forget is it’s nearly impossible to tackle an entire organization’s data in one shot. It’s better to go at it in a granular way, gradually incorporating data in sets and testing thoroughly the efficiency of implementation. Taking too much data in the first step itself will yield unreliable results or cause a complete collapse of your setup.

8. Set Up Centers of Excellence

In order to optimize knowledge transfer across your organization, tackle any oversights, and correct mistakes, set up a center of excellence. This will also help share costs across the enterprise. One key benefit of setting up centers of excellence is that it will ensure the maturity of information architecture in a systematic way.

Conclusion

Associate big data analytics platforms with the data gathered by your enterprise apps, such as HR management systems, CRMs, ERPs, etc. This will enable information workers to understand and unearth insights from different sources of data.
Big data is associated with four key aspects: volume, variety, veracity, velocity, according to IBM. This means you have very little time to start analyzing the data, and the infrastructure needed to analyze it and provide insights from it will be well worth your time and investment. This is the reason why there is such immense interest in big data analytics.