Failure of Large Data Projects – Causes and Prevention

    With the ever-increasing competitive nature of almost all business sectors, companies are beginning to amplify their ambitions and goals. Often this over expenditure in such a rapid timescale can lead to failure of the project and ultimately the business itself.

    Different reports come to different conclusions, however, the variable in which they all seem to share is that a project involving large data tends to fail more times than not. This can be a daunting statistic for companies and organizations who are considering working with big data.

    Organizations are faced with a Catch 22 scenario – they could chose not to utilize big data within projects and risk not innovating or being successful compared to their competitors, or alternatively they can attempt to defy the odds and go for big data projects. If this option pays off, companies are able to keep up with competitors and would more than likely continue to become successful.

    Vague Objectives or Outcomes
    Focusing on the end goal is a great start as you need to know where you’re going before you leap, but you need to ask yourself “how exactly am I going to get there?” This is a common flaw with businesses that fail to utilize big data orientated projects. They have an end goal but no real plan or roadmap of how to get there. There is absolutely no point using big data simply because other companies are doing it. This inherently will just lead to failure. Before setting out on this type of project, the business should clearly understand what it is they are setting out to do, and more importantly why they are setting out to do it.

    Lacking Appropriate Skills
    Big data projects should not be spontaneous. Adequate planning and research should be conducted prior to the start of the project. A common fault which leads to failure is inadequate tools to complete a project of that scale. An organization should not expect to be successful in a big data project if it does not possess the employees with the expertise to handle the workload. Workers who specialize in the field of big data projects are few and far between. This of course leads to competition, and even when talent does become available, naturally they will command a higher rate of pay. Failure to be able to afford or acquire a specialist or numerous specialists will ultimately result in project failure or inadequacies.

    False Expectations of the Effect of the Data
    As contradictory as it may sound, the data aspect of big data should not be the sole focus of the project. There has to be an element of human integration within the project. Computer generated numbers are not going to make it a success. Humans succeed where computers can’t. We know how to ask targeted questions based on the data produced. So making sure the two are utilized together is vital.

    Failure to Get the Go-Ahead
    Good communication between executives and the employees who are trying to get the necessary approvals for the project is critical. If one of the two parties do not effectively convey their ideas or understand what is being proposed and why it’s beneficial for the company, it’s doomed for failure.