How to keep your staff motivated
May 15th, 2019
An early encounter
At my graduation ceremony from the University of Salford, John Amaechi, a best-selling author, organisational consultant and retired basketball player, made a fantastic speech that caught my attention. During this he described the significant rate at which data is being produced and in turn how data storage has needed to adapt and increase to handle this.
In 2013 the term “Terabyte” was something that I had little knowledge of and I was shocked to realise just how much data we, as individuals, constantly produce. Every purchase made in a shop or online or even just a click of a mouse online creates data that can be stored and analysed to produce usable information. I didn’t realise that in just over a year, I would encounter this topic again and be learning about the Big Data industry.
Big data introductions
My official introduction into the world of Big Data started 8 weeks ago when I began my new role as KDR’s Marketing and Recruitment Administrator. It has been important for me to develop an understanding of our market – recruitment in the Information Management and Big Data industry. Luckily, our MD – Mark Dexter is happy to guide me through the learning process and has given me insight into an industry that at first seemed a little perplexing.
My first thought, when trying to understand what “Big Data” is, was that it must be data being discussed purely due to its significant size. During my discussions with Mark, I was quickly steered away from this notion and informed that the term “Big Data” itself is actually quite misleading and the concept that it attempts to define, must include additional factors, not just that of size.
Follow the leader
Mark also recommended that I follow industry thought-leader Tom Davenport. His article “Stop using the term ‘Big Data’” certainly coincides with the impression I now have of Big Data – that the term is not fully descriptive of the concept. His book “Big Data at work” has provided me with more insight as to the effects of Big Data and how it can truly impact upon businesses if utilised properly. There is a lot more literature to take in around this topic so this research is certainly going to be an ongoing project for me.
The textbook answer
The most straightforward “textbook” definition for Big Data that I have found is the 3 v’s, originally created by Doug Laney in 2001. The model outlines Big Data using the following factors:
1. Volume (The amount of data)
2. Velocity (the speed of data production)
3. Variety (The number of different types of data)
There is some disagreement as to which terms should be included in this model but knowing what each of these mean has been helpful in understanding what is necessary in order to class data as “Big Data”. Many discussions around this topic suggest that this model does not include enough elements to define Big Data and a range of other factors have been added.
In searching for a more up to date version of the model, I found it helpful to study the article: “Understanding Big Data: The Seven V’s” by Eileen McNulty. The original three elements are still the same and then the model is expanded to include:
4. Variability (Data that is constantly changing)
5. Veracity (The potential value of data dependent upon its accuracy (referring to clean data))
6. Visualisation (ability to present data in a readable, interpretable manner)
7. Value (the potential value of data once it is exploited)
This definition has given me a fantastic overview of the main factors that underline the concept of Big Data however I felt that understanding how Big Data is actually applied would give me a deeper understanding of the industry.
The real world of Big Data
One of the most useful ways to get to grips with the Big Data industry has been to search online for related news articles. By reading these articles I have been able to understand the application of Big Data in a more tangible sense. This has shown me how analysing data has allowed companies to better understand their customers and therefore market their products to them more accurately. One of the examples that now seems glaringly obvious to me is Amazons “You may also like” section. To create this they must be analysing the data produced as individuals navigate around the website looking at different products, purchasing some but not others. The scale of this data must be vast as they can retain this information from all Amazon shoppers and thus push products they feel are relevant to each specific person.
Some of the articles have also had less commercial angles to them for example, the use of Big Data in tracking the spread of disease has been posted on social media frequently, showing how Big Data can be utilised to try and deal with outbreaks such as Ebola as well as the use of data analysis in sport as statistics are analysed and influence decisions for sports teams.
Having access to social media platforms such as Twitter and LinkedIn has been essential in following Big Data news and discovering further insights into this industry. I have been following thought leaders, industry experts and Big Data companies that post interesting Big Data perceptions and information that make Big Data seem more of a tangible, applicable concept. I am also hoping to attend some of the industry events and conferences to help broaden my knowledge (For our list of Big Data events you can’t afford to miss click here)
Hindsight is a wonderful thing
Looking back to my graduation day and the speech about data production, I can appreciate the relevance of what was being said and the application of it to the industry I now work in. More data is generated and captured than ever before and the rate of this is increasing. This data in turn can then be harnessed to provide answers and understandings and become very valuable to businesses across the world. This must be fuelling the employment market and creating more demand for professionals that can understand and analyse the information. My understanding of “Big Data” is still developing and hopefully this will show through my future articles as I gain more insight and am able to articulate this further.
If you have any recommendations for information sources or Big Data insights you would like to share then please comment below!
Written by Claire Samuel