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10 Pitfalls Companies Should Avoid Before Implementing Big Data Projects

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Big Data Projects

Big Data Projects

Big Data Projects- Requiring A Business Case

One of the biggest requirements is coming up with a suitable business case. The relevant business case should include a clearly developed requirement for the gaps.

Transfer Everything Before Devising A Project

When an organisation realises that their current architecture is not equipped to process big data effectively, management is open to adopting advanced technologies, and they are excited to get started. They shouldn’t just dive in without a plan. Migrating everything without a clear strategy will only create long-term issues, thereby resulting in expensive ongoing maintenance.

Understanding The Business Reason And Implied Value Of A Project

When the company implements Big Data solutions for the first time, we can expect a lot of error messages and involves a steep learning curve. Dysfunction, unfortunately, is a natural byproduct of the Big Data ecosystem unless a company has proficient guidance. Successful implementation starts by identifying a business use case, considering every phase of the process, and clearly ascertaining how Big Data will create value for the business. Taking an end-to-end, holistic outlook of the data pipeline, prior to implementation, will help improve project achievement and enhanced IT collaboration with the business.

Reducing Data Pertinence

Big data is accessible all around us in multiple shapes and sizes. Recognising the relevance of each of these data sets to business needs is a key feature to succeed with big data initiatives. The following categories of data are available today. The categories are unstructured data which incorporates text, videos, audios, and images. The second category is semi-structured data which covers email, earnings reports, spreadsheets, and software modules. The last section is structured data which involves sensor data, machine data, actuarial models, financial models, risk models, and other mathematical model outputs.

Minimising Data Quality

Data quality is a highly important consideration. Bad quality can reduce analytics in any organisation. For big data, overall data quality can deteriorate as unstructured and semistructured data are integrated into data sets. While recognising the impact of data quality and taking the relevant steps to resolve problems prior to preparing big data are extremely important, organisations need to know how to improve data quality for data that it may not own or have produced.

Same Skill-set Is Not Required For Operating A Traditional Database Are Portable To Big Data

Believing companies can do everything with Big Data the way they did things with relational databases is a common mistake made by business people who are implementing Big Data technology for the first time. Companies should understand that, once they enter the new world, they can’t do things the same way.

Neglecting Security

For any enterprise, protecting sensitive data should be the top priority, especially after recent data breaches that affected large organisations. Companies should realise that security is important in the long run, and it is also important to consider it before they deploy.

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Article Credit: Analytics India

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