As part of Arden University’s online webinar series, programme leader Dr. Ben Silverstone recently spoke about the importance of data analytics, and if there’s truth behind the rumour that data is now the world’s most valuable commodity.
To watch a full recording of the webinar, click on the Register Now button above, where you can also sign up to attend future Arden University webinars on a wide range of topics.
Arden offers a wide range of specialised online master’s degrees in data analysis, so if you’re looking to get ahead of the curve in your computing & IT career, read Dr. Silverstone’s full interview below.
Thanks for joining us, Ben. To kick off, can you tell us why is data now considered by many to be the world’s most important and valuable commodity?
Well, whether this data should be considered the world’s most important commodity or not is an interesting question, What is clear that business and other organisations are viewing it as a ‘golden bullet’ for decision making. However, this is not necessarily the case; the misnomer that data will solve all your problems is one that is getting a little out of control. There is no doubt that data, collected and used in a strategic way, can aid decision making and automate processes, but I would argue that it is the people who know how to use data that are the most important commodity.
What exactly is data analytics?
For me, data analytics is an applied process of using data to solve specified problems. The applied nature of the definition is what I think is key; individuals who understand the needs of the business and how data is applied are wedded to analytics.
How does data analytics differ from big data or data science?
I always view data science as being much more hard edged. Specialists who understand the deeper applications of machine learning and statistical analysis techniques, and who can develop more complex analytical models, tend to work in the data science sphere. Data analytics focuses on the application of established tools and processes to identify problems, collect data, analyse, and then present it to address specific needs. The two work hand in hand and complement one another closely.
How does data analytics relate to XXI century business intelligence and decision making?
Data driven decision making is becoming more common, but can be hugely dangerous if applied incorrectly. The quality of the decisions made are hostage to the quality of the data used and the processes applied to analyse it. It is very easy to produce an analysis that misinterprets what is shown, and therefore does more harm than good. However, we are inevitably going to see more of this type of decision making, so creating a better educated and informed workforce will only serve to improve the process.
Can you give us an example case study where data analytics has proven to be crucial to an enterprise’s performance?
One area that we are all familiar with is the use of data analytics in customer loyalty schemes. The opportunity to monitor spending patterns, product choice, and behaviour has given companies an edge in marketing, creating tailored promotional opportunities.
How important is data visualisation to the field of data analytics and to the modern workplace?
It’s absolutely critical. The outcomes of data driven decision making needs to be communicated effectively, often to people who are not experts in the same field. Creating visualisations that convey the right information in a way that is understood and has the intended impact is a critical skill.
How does Arden University incorporate data analytics training into the programmes it offers?
Arden has a wide range of data analytics courses built around a common data core. This enables the development of skills and knowledge in the design of data collection tools, interpreting needs, and designing questions that can be tested by quantitative means, analysing data, and making decisions and then visualising and presenting that data to non-specialists. These modules sit alongside other specialist pathway modules which provide context to the data driven content and help to develop additional skills.
Why is it beneficial to study data analytics modules next to core subject modules?
The parallel specialist modules provide two key things. They are there to give context to the data content covered, giving students the opportunity to apply these new techniques in their specialist field, bringing life to their workplace activities. They also provide additional value, data analytics belongs in all functions and the more people that are data literature, the better. Studying data alongside specialist modules gives a wider and deeper skillset that can benefit business.
What skills, like mathematics or statistics, are necessary to study a data analytics oriented course at Arden University?
The courses assume that students have a level of comfort with numbers, but there is no need to have studied statistics in the past. The focus here is on understanding what different tools do and the situations in which to apply them, rather than the underpinning mathematics behind them.
What is the one big advantage of choosing a data analytics oriented programme here with Arden?
Business focus, highly relevant skills and an approach that ties together data with other specialist routes to embed the application of data analytics. The need for data skills will soon be an essential part of almost every job, not just those involved in data functions. Obtaining those skills and understanding how to apply them to relevant business areas will set young professionals up to become leaders in their chosen area of business.
If you’ve enjoyed reading Ben’s insight thoughts on the importance of data analytics, why not take a further look at the Arden University computing and IT programmes available through Edology? Alternatively, you can find out more about our regular webinars hosted by Joe O’Brian by clicking the Register Now button below.
MSc Data Analytics & Finance
This master’s degree provides you with the opportunity to acquire an invaluable command of the key concepts in data handling as applied to the financial sector.