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10 Pitfalls Companies Should Avoid Before Implementing Big Data Projects


10 Pitfalls Companies Should Avoid Before Implementing Big Data Projects

Bharat Adibhatla

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.

Contextualising Of Data

The basic logic behind processing textual data and administering text analytics lies with the contextualisation of the data. Without precise contextualisation, data can be treated with a lot of inaccuracy and exhibit skewed analytics. Processing the data without an extended notation is not valuable for metrics. Without contextualising the business rules for processing each specialist’s notations, will result in garbage data sets. For example, there are a number of steps in text analytics that need to be processed beyond contextualisation such as homographs, alternate spellings, and categorisation to conceive the accuracy of the data and to obtain value from its processing. However, the fundamental business rule for processing data is its contextualisation.

See Also

Skills Gap

The fact is, the skills gap is the main stumbling block for most businesses. Current big data technologies are designed to approach the skills gap, but they favour to support experienced users rather than promote the skills of those who need it most. And regrettably, what works for regular ETL doesn’t translate to a Big Data ecosystem, and the Big Data learning curve is very steep. Basically, companies have two options. Hire people who’ve had the customary training, or Work with experts to instruct and guide the staff through implementation.

Exaggerating Technology As Panacea

A great hype cycle in the industry today is about the Apache Hadoop framework being the remedy for all problems that are related to data. While Hadoop is largely billed as a legacy framework for big data companies, further modifications are on the way. Every time a technology tipping point happened to solve a data problem, a distinct class of data problems arose that emerged along with it. In the case of big data, the problem accompanying open source platforms is the advancement of the technology to support enterprise-scale deployments as the platforms develop to an ecosystem on a continuing basis.

The misunderstanding in this field is not realising the maturity of the technology and its fit within the enterprise. The solutions from the big data stack can be completely integrated into the enterprise for the right purpose; otherwise, the exercise may result in insignificant benefits. More importantly, it can result in mistaken analytical processing that drives to more chaos.

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