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Big Data Analytics Failures and How to Avoid Them

Big data is being seen by companies to minimize customer drop-outs and improve rate of retention. Here are some big data failues and how to avoid them.

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Big Data Analytics Failures and How to Avoid Them

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  1. Big Data Analytics Failures and How To Avoid Them

  2. Contents • Introduction • Use Inappropriate Methods • Hiring Challenges • Managing Data • Not Using Big Data As A Whole • Lack Of Planning • Costs Associated With Data Management • Communication Issues • Managing Algorithms Appropriately • Understanding the Requirements

  3. Introduction • Big data is increasingly being seen by companies to minimize customer drop-outs and improve customer experience and the rate of retention.

  4. Use In-appropriate Methods • The use of big data technologies, is not fully relevant while solving problems of big data. Not using the right sort of methods to solve big data problems is one reason of a project failure.

  5. Hiring Challenges • Hiring the most appropriate skill set may become a huge challenge for for a big data project. A big data expert must also understand the business domain, besides technical skill. Strong data and analytics capabilities coupled with mathematics and web development skills are a must for a data scientist. • An individual, lacking in one or more of these skills will not be the best fit for the big data project.

  6. Managing Data • Sometimes, gathering processing and maintaining large amounts of data can become a challenge. IT related issues sometimes create bottlenecks for the success of the project.

  7. Not Using Big Data As A Whole • Everyone is talking about big data, but one has to plan for a big data project carefully. Understanding the business problems that one is looking to solve with the help of big data is very important. • Many companies just dive into a range of big data projects, without really defining the objectives.

  8. Lack of Planning • A company using big data strategy must use it to manage all kinds of data and not just for a single project. All sorts of data in the company is governed by the big data project. • Lack of a holistic view of big data can cause project failure and not provide the desired return on investment.

  9. Costs Associated With Data Management • Some companies fail to realize the quantity of data they require and go overboard with it. • The problems arise, when others are able to maintain the quality of data or keep it secure as desired.

  10. Communication Issues • Data scientists usually have a highly analytical approach as compared to others in the organization. The sales team and the management team may at times find the communication process to be challenging. • This may create problems for the the project.

  11. Managing Algorithms Appropriately • Algorithms are a very important aspect of a big data project. These algorithms have to be constantly changed and managed on a constant basis, to get the desired results from the project. The algorithm must be complemented with a powerful backend, for it to deliver as desired. • A powerful front-end ensures that the results and the analysis are easily understood and used.

  12. Understanding The Requirements • Every company differs in size and scale of business. Many companies do not have a large customer base for them to require a large database warehouse. • Understanding the requirement and then investing in the required infrastructure is important.

  13. Choosing The Right Partner Virtual teams are a cost effective way to grow your business and improve productivity. Hiring an expert agency like ValueCoders helps in hiring the best-fit teams for your project requirements. This simplifies the management process and makes the hiring cost effective as well.

  14. Get in Touch • sales@valuecoders.com www.valuecoders.com • www.facebook.com/valuecoders • www.twitter.com/valuecoders • www.linkedin.com/valuecoders

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