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Data Science Pain Points

22 August 2022

Top 5 Pain Points for Data Scientists

Every job has its stresses – but some are more stressful than others. With the huge focus on data in today’s organisations, data analysts/scientists are at the forefront of major disruption. And as you know, disruption brings pain. In this article, we’ll look at the top pain points being experienced by data professionals and offer some tips on how to ease the pain.

  1. The massive amount of data

With so much data being available and being collected by organisations, it’s easy for data professionals to be completely overwhelmed by it all. This is made worse by the outdated and meaningless data that’s often in amongst the relevant information. The more data there is, the more difficult it is to dig down and extract the valuable insights that the organisation wants. There may also be a temptation to not fully analyse the data, or focus on the easy bits that don’t add real value.

To address this, it’s vitally important that management uses a sensible prioritisation approach. It should also support the introduction of an automated data system that automatically collects and organises data, sifts out outdated data, and provides automatic alerts, customised reports and strong visualisation. Replacing manual processes in this way will free up time and allow staff to act on real-time information and insights – and save them from drowning in data.

  1. Data from multiple sources

The next pain point involves trying to analyse data from multiple sources – which can be in the cloud, on-premises or out in the field. Different data lives in different systems and platforms (Excel, SQL databases, Google Analytics, SAP Data Warehouse, Salesforce Reports, EPOS systems, etc, etc) and it can be difficult to identify what is where. This can lead to incomplete analysis and inaccurate insights. In addition, integrating the data from the different sources can be challenging. Manual amalgamation is too time consuming, inefficient and prone to error.

Once again, a comprehensive data system can be used to ease the pain. It can pull information from multiple systems into one location, and allow normalisation and cross comparisons. For example, creation of a data warehouse fed by modern ETL tools such as Power Query or SSIS provides a coherent data source that is much easier to report from than multiple, disparate data sources.

  1. Inaccessible and unavailable data

Having a centralised data system is very important, but the data that feeds it must be accessible and available to the organisation’s data professionals. Access to relevant data should be a given – but organisational structures and permissions often get in the way. This can be made more difficult when departmental data silos are isolated from the rest of the organisation, or when ‘one-off’ access is required to deliver specific insights.

Management must ensure an appropriate support infrastructure and authorisation process is in place, so relevant people are authorised to access relevant data from anywhere. This combined with an effective database/data warehouse (see above) should eliminate any accessibility issues. Complete, real-time information can then be analysed and acted upon, to allow rapid decision-making.

  1. Inaccurate and poor-quality data

‘Garbage in, garbage out’ is especially true when it comes to analysing data. If the data is inaccurate, then the insights and decisions based on it will be flawed. Poor data can have many different causes, including inaccurate manual transfer/data entry and asymmetric data. This is where the information in one system doesn’t update when changes are made in another system.

A centralised data system can help here, where data is input automatically with mandatory fields. Proper system integrations can also ensure changes in one area are updated everywhere in real-time. And data cleansing routines can also help – provided they are kept up to date as source systems change. A modern tool that makes it easy to maintain these routines (such as Power Query) is also very useful.

  1. Confusion and anxiety

The work that data professionals do is increasingly in the spotlight and undergoing massive change – so it’s only natural that people feel anxious. Management can have unrealistic expectations around data, there can be friction between data science and production, and users can be resistant to automation. Nobody likes change and it can be extremely confusing and stressful

To help alleviate the stress, management and HR should promote the benefits of the new systems, and provide technical training and HR support to those affected. They should acknowledge that data analysts and data scientist need to have deep expertise in a variety of tools and techniques – and be committed to fill any gaps in peoples’ skillsets.

Here at JBI Training, we can help with that. We provide a range of cutting-edge training courses designed for data professionals.

To find out more, contact us here


About the author: Craig Hartzel
Craig is a self-confessed geek who loves to play with and write about technology. Craig's especially interested in systems relating to e-commerce, automation, AI and Analytics.

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