Errors that can ruin your big data analysis

errors that can ruin your big data analysis

Long gone are the days when business operations were simple and the data generated by these functions could be easily stored. Modern-day businesses generate huge volumes of information every day. With more organisations becoming data-driven, data processing and analytics tools are becoming popular so that big data can be used to make sense of information and utilised to create meaningful business insights.

If you are interested in starting a career in big data, it is useful for you to know about the common big data mistakes that can prove costly for your company. This article helps you become familiar with these mistakes so that you can avoid them in your career.

Common mistakes in big data analytics that can ruin business predictions

  • Lack of a business case: Big data can draw meaningful conclusions out of seemingly unrelated information. However, you still need a concrete business case to make use of these results. This is the only way to make big data applicable within a business setting. For instance, you cannot simply analyse brand awareness on social media. Instead, you need to use big data to improve brand image by setting clear parameters such as direct and indirect influence, geolocation and engagement. Once you detect followers’ behavioural patterns, you can adjust social media strategy to increase brand awareness.
  • Poor data quality: The outcome of big data analysis depends on the quality of information. This is particularly the case with unstructured and semi-structured data because they need a pre-processing adaptation. You should filter textual information through language correction libraries to polish the content. Image and video data quality are acquired from the source, but you always need quality data to generate accurate results.
  • Life cycle of the data: Timing plays a key role in comparative analytics, but many predictions can go terribly wrong because they don’t take data lifecycle into account. Let’s say you started importing a product in April 2017, so there is no data on this product being sold in Q1. Looking at Q1 for April 2017 might make you predict sales of 0 for Q1 2018. However, context is everything. You may need to find another solution such as comparing this product’s sell-out with similar items you already have in your portfolio. Being unaware of data lifecycle can lead you to form different and inaccurate conclusions.
  • Extrapolation on the same formula: Big data involves processing huge amounts of information and predicting insights based on the results. Extrapolation in the case of missing data is an essential component of the process—yet, you can induce errors if you use the same formula for predicting insights for different data sets. This can make your analysis go wrong which might affect the business decisions dependent on them.
  • Big data complexities: Big data has multiple layers of hidden complexities that are not visible by simply inspecting them from an end-user perspective. Without understanding these complexities, modelling a solution for the dataset can create incorrect results that may skew your business decisions.
  • Data irrelevance: Understanding the relevance of the business data available to you is an important aspect of processing. When data is collected without a thought on whether it is relevant for a specific project, it often leads to wastage of manpower and resources. For example, if you were studying the amount of time an employee spent being productive during his day, you would not simply look at how many hours he spent at his work station. You would need to consider other factors.
  • Improper data contextualisation: The fundamental logic behind processing data and implementing the analytics depends on the context of the data. Without proper context, even the most accurate data can seem meaningless. For instance, abbreviations can mean different things in different situations unless they are standardised. Blind processing of data without providing any context for the dataset can result in varying insights that might not be reliable.

While Big Data has the potential to boost your business, errors in the analysis or collection can lead to false predictions which might impair your business decisions. It is important for businesses to conduct regular discussions and detailed data modelling to properly utilise the power of big data.

If you want to kickstart your professional career in big data, Toronto School of Management offers the Diploma in Data Analytics Co-op which can help you acquire important skills. For more details on the course, click here.