The applications of Big Data is immense. Not only the big companies even the smaller companies are foraying into it. It comes with nightmares as well, explore how not to fall into such big data pot holes.

The reason these companies put forth is not big enough scale of operations. With the passage of time, these companies pay a heavy price as their competitors get benefited from Big Data.

Implementing big data analytics to the way these companies did businesses brings more secure results.

These small companies may implement the learning's from the experience of the major companies and get benefited from the Big Data implementation right from the outset.

Minimal harmony with the stakeholders:

Major stakeholders must be taken into confidence about the prospects of big data analytics.

Big data is not a playing field for big companies only. Even the smaller companies are getting into it. This is transforming the way they do business. Some companies do not implement big data solutions to their business operations.This proves that big data analytics has no set bar for the scale of operations. It is beneficial for a varied range of scale of operations.The reason is these companies have a cushion of learning from the mistakes committed by those bigwigs which have Big Data analytics already in place.That being said a careful study of the mistakes in the implementation of big data analytics is very important. Not just a careful study, but coming up with means to avoid those mistakes is even more crucial for success from the start.Some of those important lessons to be learned are as follows:Implementing a big data analytics project does not suffice for the success and overhaul of a business process.Stakeholders have the means and the decision-making power. By means of stakeholders, the necessary resources for the implementation of big data analytics can be arranged easily. Team i.e. suitable human capital is of the utmost importance. Apart from this, the budget is also an important component which is easily arranged by means of stakeholders. These are merely the physical aspects required for the successful implementation of Big Data analytics. The even more important aspects are the will to act on the results of the Big Data analysis. Big Data analytics is of no use at all if there is no will in the concerned stakeholders to take action on the results brought forward. At the first sight of it, stakeholders might not find the results worth enough to be acted upon. They also may deny the data access and thus put a permanent hold on data analytics project. Thus having stakeholders in confidence is very important for the success of Big Data analytics project.Implementing a big data analytics project has its own costs associated with it. The benefits reaped after the implementation not only cover the costs but make the implementation a profitable move. As discussed earlier the stakeholders have the means to make available the resources needed for an analytics project. They may be unwilling to contribute to the added costs and focus on infrastructure savings at the expense of the analytics project. They may decide to move their data off the major and reliable databases with licensing costs to almost free databases. They may find this move very lucrative at the beginning. Soon enough they realize the gravity of the situation they end up in. These almost free databases help save on the licensing costs only. Other costs come into the picture because of this. These are the labor costs. These additional labor costs are associated with the extra work to be done to deploy, analyze the data, and manage the new almost free database. So the overall result of this move is just the shift of the costs from expensive databases to labor costs. If a cost benefit analysis is done then it becomes obvious that reliable but expensive databases provided far better results.Workforce required for the successful implementation of a big data analytics projects is quite significant. These projects require specialist human capital because of the intricacies involved in the implementation and analysis. There is a huge difference in the supply and demand for this specialized workforce. Most of the universities also do not have an all-encompassing curriculum to produce data scientists. Even the major companies find it difficult to get their hands on the required number of data scientists which suit their need. Thus it is quite obvious that for smaller companies getting the required set of resources is even more difficult. The resource even if available come at a price which is mostly out of budget for these companies. Thus, they should shift their focus from looking for these resources outside, to building the proficiency in internal resources. The fit set of resources should be selected from the pool of already available staff. They must be developed and given simulation training. This strategy saves a lot on cost and time spent on looking for the suitable resource.All the departments in a company should coordinate especially with the IT to reap the benefits of Data analytics project. Data reports from the stand-alone departments do not make any sense. The data from one department has to be shared with the other departments for meaningful analysis. Department leaders of the respective departments should leave the job of data analysis to the IT team. This is something which is not their expertise and needs special IT background. Once the coordination between different departments is in place and IT project management team 4is consulted for data analysis the results are outstanding. Amid market or small company should not commit the mistakes discussed above. If these companies are able to avoid these pitfalls then the successful implementation of big data analysis project becomes a reality. Once they start seeing the data through the lens of big data analytics, success is guaranteed. This helps these companies to come above the competition and serve their clients better. Lesser competition results in wider customer base which results in higher revenues. Customer satisfaction enhances the overall brand value. The future path to data analytics becomes easier.