Companies and brands are set to invest millions in big data analytics while attempting to secure future growth, but successful implementation relies on three key aspects working in perfect harmony.
As big data makes its way into organisations around the world, the synchronisation of processes, people and skills is proving tricky. Here are some of the major challenges that data analytics is facing today.
- Understanding how data management fits the business
Big data is still in its infancy, but lots of businesses are forging ahead, creating processes for dealing with the analysis of vast amounts of information. While new models are being developed to help companies reach specific goals, these have different purposes. Some companies prefer flexibility while others want greater functionality. There’s no ‘correct’ way to do big data, but businesses need to be careful not to over-commit to an approach that doesn’t fit their needs, resulting in wasted time and effort.
- The talent gap
It’s also becoming clear that knowledgeable technical staff are being trained at a much slower pace than technologies are developed, leading to a skills gap. By 2020 data science will account for 28% of all digital jobs according to IBM, but the same report highlighted that a lack of people skilled to do these jobs means that these positions remain unfilled for up to 45 days. Many of the current generation of big data experts don’t have expertise in data modelling or architecture: their experience tends to lie in tools, platforms and programming.
- Getting organisations on board
Research has shown that, while one in four businesses rely on big data for making day-to-day decisions, a lack of board-level support for data initiatives has contributed to failed projects in 25% of companies. It takes a significant time investment to change a business into a data-driven entity, with the first steps being to decide where analytics should be applied. There should also be enough time to explain the benefits of business analytics at every level of the company: when staff understand the purpose of capturing information, it’s more likely that they’ll keep to the processes that support good quality data.
- Join up data sources
Once data is integrated into a big platform, it must be in sync. Otherwise it can result in analyses that are wrong and could have completely disastrous results. Data invisibility, bias and selective use are prevalent in many areas of business. Confidently making decisions based on bad insights can be more damaging than not having the data at all.
- Extracting the relevant insights
Data that has no ability to inform or give insight has no worth. It’s essential to process raw data to make it more widely understandable. This problem only becomes greater when the data is gathered over an extended timeframe. As time increases and the number of data cycles rises, the more difficult it is to be able to display relevant information and draw usable insight.
From gaining an edge over competitors and anticipating future business demands, the possibilities with business analytics are endless. In the last decade, big data has come a very long way. Overcoming the challenges surrounding it is going to be one of the major goals of the data analytics industry in the coming years. If you’d like to explore this area further, the University of York’s 100% online Computer Science Masters with Data Analytics programme might be right for you. Designed for working professionals and graduates who may not currently have a computer science background and want to launch their career in this field, it equips graduates for a range of positions working with big data, algorithms, data structures and data mining.
Delivered 100% online, this is your computer science Masters degree, on your own terms, in your own time. Choose from six start dates per year, enabling you to study around work and home commitments. There’s a pay-per-module option available, and some students may be eligible for a government-backed postgraduate loan, covering the cost of the programme.