The Big Data Gray Area – Do’s and Don’ts

In my previous posts about Big Data, I told you Basic Steps for designing the Big Data Architecture and Functionalities of each layer in Architecture.

Big Data carries a lot of promise for all types of industries. If this big data is leveraged effectively and efficiently, it can have a significant impact in decision-making and analytics. But the benefit of big data can only be achieved if it is managed in a structured way.

If you’re looking for a perfect answer to the big data ethics dilemma, you ‘re not going to find it in this article – or any other article, for that matter. That’s because there is no perfect answer. If there were a perfect answer, an obvious answer, or an easy answer, we wouldn’t be facing a dilemma – there would be no gray area.

Many firms and companies are beginning their journey towards Big Data and are in early stages of execution. Consider these “Do’s and Don’ts” as a part of your strategy.


  1. Do involve all business units in your big data strategy

Big data initiative is not an isolated or independent activity, and hence to get a useful insight all business units should be involved. With the help of Big Data, businesses leverage huge volumes of data to know about the customers, their behaviors, processes, events etc. As a result, organizations are concentrating on all types of data coming from all possible avenues.

  1. Do evaluate all infrastructure models for Big Data Implementation

Big data deals with petabytes of Data. Management of this data is a major concern. With that cost component is also to be considered before selecting any storage facility. Data Centers and Cloud services emerge as solutions. Storage is one of the most important factor that should be evaluated very carefully.

  1. Do think about your traditional data sources as part of your big data strategy

Traditional Data is important component for the success of any big data story. It is critical that you plan to use the results of your big data analytics in conjunction with your data warehouse. The data warehouse includes the information about the way your company operates.

Therefore, being able to compare the big data results against the benchmarks of your core data is critical for decision making.

  1. Do plan for consistent big metadata

One of the Characteristics of Big Data is “VARIETY”. In big data environment the data is coming from various sources varying in format, structure and types, hence the data is not cleansed. Check the incoming data for consistency by repetitive observations and analysis. Once the data is consistent, it can be considered as Consistent Big Metadata.

  1. Do distribute your big data

Managing this huge volume of data on one server is a far-fetched dream. Find out techniques to apply Distributed computing in your system such as Hadoop to effectively manage the size, variety, and required speed to manage your data.

  1. Do Validate

Be the biggest skeptical of your own when it concerns the data and analysis. There is no faster way to lose the creditability and the confidence of your managers than presenting bad data or the resulting invalid analysis.


  1. Don’t rely on a single approach to big data analytics

Various technologies are available in the market for processing of big data, Hadoop being the foundation for all. Hence, it is important to evaluate the correct technology for the correct purpose. Examples of good analytic approaches re predictive analytics, prescriptive analytics, text analytics, stream data analytics, etc.

The best approach to select one is to investigate about all available approaches. Experiment to select the perfect technology solution for your business.

  1. Don’t start Large Big Data initiative before you are ready:

The potential of big data is very impressive, but the real value can only be achieved once we reduce our mistakes and gain more expertise. Beware, don’t start with it all-together. Walk before you run. It is good to be ahead of your competitors, but it would be better if you would do it with some smartness and experience.

In order to set up a full stack, you’ll have to start small. It is always recommended to start with small steps for any big data initiative. So, start with pilot projects to gain expertise and then go for actual implementation.

  1. Don’t overlook the need to integrate big data

Sources of data are scattered around us and they are increasing day by day. Effective analytic output can only be achieved if all the data sources are integrated together. Good technologies are available in the market for data integration, but they should be evaluated properly before use.

  1. Don’t forget to manage big data securely

Data security is a major consideration in big data planning. When companies embark on Big Data analysis, they often forget to maintain the same level of data security and governance that is assumed in traditional data management environments.

Security to petabytes of data is not strictly implemented. But after some processing, you will get a subset of data that provides some insight. At this point Data security becomes essential. More the data is fine-tuned, more valuable it becomes.  This fine tunes data becomes an intellectual property and must be secured. Hence, Data Security must be implemented as a part of the big data life cycle.

With security the privacy concerns should also be taken care of.

  1. Don’t overlook the need to manage the performance of your big data

Results of analytical tool are useful only if they are performing well. Big data offers more insights based on the processing of a huge amount of data at a faster speed. This capability to gain more insights is a huge benefit. Hence, this data should be managed effectively and efficiently. Therefore, you need to build manageability into your road map and plan for big data.

  1. Don’t let bad Data or records go unresolved

This means removing duplicates, understanding why you have nulls, standardizing your data formats, and maintaining your key fields. Consistent pruning of your data will ensure its effectiveness and accuracy while at the same time keeping it up to date.


The ability to harness the power of big data requires more than technology. It requires business and IT collaboration. There is no silver bullet when it comes to big data analytics, but success starts with a solid strategy. I hope you can use these tips to glean valuable insights ranging from process optimization to customer-facing improvements.

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