By the time Thomas Edison had invented the lightbulb, he had racked up over 10,000 failed By 2025, IDC predicts there will be 163 zettabytes of digital data created worldwide. That’s roughly 163 billion Terabytes.
There’s a reason why big companies such as Facebook risk data protection lawsuits on a daily basis. Data is among the most valuable assets in the world right now. Forget oil, property or huge technological advancements – the true business advantage is given to the person or company holding the most data. Customer data, market data, transactional data; it doesn’t matter, providing you have the appropriate tools and personnel on hand to process the data correctly. It’s all well and good having the secret to true happiness written in a book, but if it’s hidden in 100 unlinked sentences sprawled across an ancient 12,000 page novel then you can’t locate or utilise it. A skilled and well-equipped data analyst can find the secret to true happiness in that artefact, and reveal to you the hidden answer: “Reading more Quanta Training blogs.”
Thankfully, there’s no need to go hunting for the value of data analysis. We’ve brought you our thoughts on the subject and why the role of the data analyst is highly valuable both personally and in a business scenario (regardless of organisation size). A word of warning, you’re about to read the word “data” far too many times.
Shiny, sparkly data
Data analysis as a science is relatively new in the context of IT history, due to the processing power needed to analyse massive sets of data either through traditional BI applications or by building deep learning systems using technology such as neural networks. Like a right hook to the face, data analysis has taken a few senior IT leaders by surprise in organisations on both colossal and local scales. Collecting, analysing and reporting data has become synonymous with big data, and therefore big companies. However, a company of any size can generate stores of data sufficient to meet the same criteria, but are assuming that they cannot compete on a strategic level of data analysis with much larger companies. This combined with an unwillingness to alter well-established business structures has meant some IT heads, CIO’s and other managers have been apprehensive of advanced data analysis. The likes of Facebook, Google and Microsoft have pioneered using big data to drive business strategy, due to their ability to gather masses of user data cheaply and easily. The exponential data-driven growth exhibited by the data giants has encouraged organisations of all sizes, no matter the kind of business you’re running, to take data science and capture more seriously. Reports made by data analysts enable the best possible business decisions to be made – you wouldn’t buy a used car without knowing the age, mileage and any conditions impacting its performance - unless you have a particular soft spot for one of the AA roadside rescue drivers.
When building a data model, the first step is data cleansing (or cleaning) to remove and update any information that may be incomplete, incorrect, duplicated or has simply become irrelevant. If you’re collecting data from customers and employees properly, you’ll have a mountain of (potentially) filthy data resource that needs scrubbing. Having crystal clear, top quality data is the foundation for the skyscraper of information you’ll build; a skyscraper that will maintain its reputation thanks to minimal inaccuracies when reporting data.
Digital transformation initiatives are reliant on data and analytics, according to 90% of analytics and data professionals. However, it’s been revealed that 83% of organisations worldwide potentially do not have the data skills requirements that they need. This is shocking, given that DTI’s often comprise the focal point of many corporate strategies.
This is, of course, just a single example of data analysis informing high level decisions in an organisation. The point is, profit is driven by decision making in all aspects of an organisation, making tweaks across the board will make substantial, sweeping improvements across the board in an organisation. These high-level decisions can be influenced by data such as risk identification, market shifts or the general performance of departments within the business. Going with your gut has always seemed like the right thing to do, but if you’re digesting enough information, a systematic approach will be far more efficient and profitable (up to nineteen times more profitable, according to recent research.) Leveraging specialised skillsets is the definitive route to strategic action, by extracting and evaluating key data.
Data scientists are the special breed of professional that hold these skillsets. When faced with voluminous sources day in day out, these Datamancers (patent pending) are able to guide the organisation with a deft hand. In case you couldn’t tell, this entire blog has been a love letter to data scientists, they have a hand to play in the exceptional performance of most market leaders.
We’ve made our thoughts clear on data analysis and the people that make it happen. There’s nothing more powerful than an organisation that is well-armed with data and has the means to use it effectively. If you want to find out more about data, analysis or developing the skills you need to become more advanced in the field, then find out more here.
Like what you've read? Download the pdf here!