Big Data Analytics Failures and How to Avoid Them

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Big Data Analytics Failures and How To Avoid Them

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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

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Introduction

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

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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.

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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.

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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.

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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.

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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.

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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.

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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.

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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.

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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.

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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.

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