Pitfalls to Avoid When Implementing a Big Data Program in Your Business

Big Data has been fascinating industries of all stripes with its promise of improving business outcomes and facilitating an ingrained understanding of customers. While there were some who managed to grab the headlines owing to their uber-successful data science projects, there have also been others who could not make a mark in this domain and landed with sub-optimal results. Of course, there is a third category too, who seem to hesitate to take the plunge, since they feel that the technology is highly capital-intensive and thus can be leveraged only by the large corporate houses.

It is important to note here that a data science program does not merely imply the availability of a massive volume of data, but, it entails a careful integration of strategy, tools, resources, expertise, and the willingness to adopt the change. The success of the project primarily depends on upon the organization’s ability to capture, sort, and analyze the relevant data, and finally utilizing it to bring about a transformation in the manner in which the organization works. However, during this process, overlooking details, massive or minute, may lead to the flawed hypothesis, and in turn, minimal results.

Here’s a rundown of the common pitfalls which organizations should avoid while approaching Big Data integration initiatives in their business.

  1. Rushing into infrastructural investment without a proper plan

Companies looking at Big Data often presume that the implementation starts with a hefty investment in data infrastructure. They focus all their energies on purchasing sophisticated tools, thinking that once their employees are provided with the requisite resources, they will be able to deliver actionable and innovative insights. However, in due course of time, they realize that all they have is a chunk of graphs, with practically no valuable and actionable information. What they fail to comprehend is that like any other business strategy, Big Data implementation stems from a well-defined and coherent plan, which takes into account the needs, objectives, requirements, timelines, and allocation of resources. It is only after devising a proper strategy, that one can expect measurable results from the data science project.

  1. Absence of a ‘data-science friendly’ culture

A data science project can never thrive in an incompatible culture. The company may succeed in determining crucial information about customer behavior, but the data may not be factored into decision making if the organization as a whole is not ready for the change. Rigid administrative processes or interdepartmental politics often interfere with the creation of a data-science friendly culture. The key is to foster an environment, wherein each one embraces the idea of collating and respecting data, thereby making data-driven decisions the norm of the operational workflow.

  1. Not understanding the relevance of data

Big Data comes in a variety of forms, shapes, and sizes. It can be primarily classified into three categories – Unstructured (text, audio, video), Semi-structured (e-mail, spreadsheets), and Structured (sensor & machine data, risk models, statistical model outputs). This avalanche of data needs to be mapped to the particular analytical need of the enterprise. By misinterpreting or not understanding the context and relevance of data to their required analytics, organizations land up with insights that are highly distorted or fallacious.

  1. Lack of organizational maturity

For any data science project to succeed, it is imperative that the team owners possess the requisite amount of maturity in terms of domain know-how and data knowledge. In other words, they should be well-acquainted of what their expectations are, what the project means to their organization, and how the analysis will enable them to drive decision support. Organizational maturity also calls for a high-level of interdepartmental coordination, where insights are shared across all functions. One needs to remember that data analysis is not a technology initiative, but a strategic opportunity for the business to thrive. Success is possible only when there is a sharp business involvement and drive on the whole, and not when a bunch of IT guys is designated the task of continuously working on data, graphs, and more data, and more graphs!

Like any other strategy, Big Data also comes with its own share of complexities, in terms of volume, velocity, variety, and much more. However, with the guidance of the right expertise, and by following a disciplined & carefully planned out approach, enterprises can surely set their Big Data initiatives up for success!


Acer Innovation is a leading edge data analytics firm providing consulting services across a diverse range of industries. For more information on its use of technology and services provided, one may visit http://www.acerinnovation.com/

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