Unless you’ve been living under a log for the past few years, you would have no doubt heard the term ‘machine learning’ used in abundance. It’s everywhere – there is some level of machine learning implementation in all new age technologies, from chatbots to automated cars to predictive analytics. Machine learning itself fits under the banner of something known as ‘data science’, a broader term that focuses not only on the intelligent analysis of algorithms and statistics but also the entire data processing methodology. Data science involves analysing data to effectively extract useful information, in order to gain insights and knowledge from any type of data — either structured and unstructured. Why does this matter in 2020? The answer is simple. Businesses have at last recognised that there is enormous value in data processing and analysis, and now more and more organisations are taking concrete steps to ensure they are unlocking the power of data insights. By taking actionable insights from chunks of ‘Big Data’, businesses and organisations are able to mitigate risk and fraud, personalise the customer experience, deliver relevant products and as a result turn a higher profit.
The career potential that could result from acquiring data science skills, or the potential value that could be unlocked for your organisation by embracing data science. For those seeking to upskill or reskill, gaining a Data Science Certification is the first step. Generally, this course helps students acquire the key skills that are required to work on the projects available in the data science industry, with subjects including data visualization, analysis, libraries, and open source tools.
As articulated by John W. Foreman, the chief data scientist for MailChimp,“Data science is the transformation of data using mathematics and statistics into valuable insights, decisions, and products.” Take global ecommerce platform Amazon’s approach to data analytics as the perfect example of a company using data well. Its anticipatory shipping model utilises big data and predictive analytics to predict which products you are likely to purchase, when you might buy them and also where you might choose to ship the products, thus increasing its sales and profit margins while decreasing its delivery time and overall expenses. And it does all this through using intelligent machine learning techniques to gather and analyse readily available information. Microsoft is similarly invested in big data. To date, 1,800 data workers have been employed by the global tech company to help it better leverage consumer data to improve its products, and it isn’t stopping anytime soon. It does this by first looking at products, services, devices, and the Microsoft infrastructure, before looking for areas of potential improvement, all through using product and service telemetry like crash data and client device information. It also collects user feedback and satisfaction metrics from employee surveys and Microsoft Helpdesk support data to better inform its decisions. Coca Cola is another example of a company that uses big data analytics to drive customer retention: in 2015, it strengthened its customer base by building a digital-led loyalty program, all based on big data.
The funny thing is, organisations are so awash with data, but few of them know how to truly leverage that information. Bad data is apparently costing the US roughly $3.1 trillion a year, and according to healthcare firm McKinsey big data initiatives in the US healthcare system “could account for $300 billion to $450 billion in reduced healthcare spending. Statista predicts that by 2023, the big data business world is going to be worth $77 billion, globally.
So why aren’t organisations investing their efforts and resources into capturing the powerful insights offered by big data and data science?
For a start, businesses don’t know what information they wish to glean from big data in the first place, and this is their biggest problem.
“The essence of analytics is for business units, marketing, emerging business offices, etc. to determine what they want to learn from the data and then use the records information management team, IT, data analysts and scientists to identify data sources, understand access controls, execute the analysis, and deliver the results in a user friendly, typically visual, mode,” said Sue Trombley, managing director of Thought Leadership at Iron Mountain, a data management firm. Without knowing exactly what their end goal is, how are organisations expected to collect relevant data? There is no use learning what customers’ favourite colours are if the organisation has no intention of changing a product’s colour, is there? Or in learning customers’ marketing preferences if there are no resources to invest in new marketing?
First, businesses must establish their end goal, or what it is they wish to learn from big data. Secondly, they must develop the right approach to capturing that data. Next, they should use that data to inform and improve decision-making, revamp and refine operations, and create new streams of revenue. After all this, they must sit back and take stock of their big data operations. What worked, and what didn’t work?
Was the process as efficient as it could be?
Data science must be understood and appreciated by those at the top too, however.
Data technicians must work alongside executives and corporate leaders in order to properly deploy machine intelligence within organisations. If executives cannot understand – and perhaps more importantly critique – the results of machine-intelligence modelling, and then use that to deliver results, it isn’t worth the investment and effort now, is it?
We are in the midst of the Fourth Industrial Revolution, an era awash with new and wonderful technologies such as artificial intelligence and machine learning. These new technologies present both challenges but also incredible opportunities to organisations. If organisations want to adapt – or better yet, not only survive but thrive – in the coming decade, they need to get onboard with big data. There is no alternative. Those that do not will be overtaken by competitor organisations, that’s the truth of it. Learning how to transform data into valuable tools is key to organisations having a profitable, sustainable, and enduring future.