Data Analytics is the Future

Published by mpume on

No More Business As Usual: Data Analytics Is The Future

I had the privilege of chatting to Adelaide Matsika who is in the Data and Analytics space in order to understand why data is so important today. The IT industry has always been male dominated, so it is great to see how the industry is changing and more women are getting into the industry and focusing on business intelligence.  I’m sure we can agree that we are living in an era where data science has become an integral part of our lives. With the outbreak of the Covid-19 pandemic, the world has become a fast-changing environment. As a result, we have had to adapt and change how we do things, it is no longer business as usual,  for instance, data and analytics has become more high profile and more visible across the globe since the outbreak of the pandemic.

In response to the ever-changing environment, as well as to support health systems with COVID-19 strategic decision making, and health policy formulation to help fight against the virus, a number of predictive models and forecasting exercises have been developed by several organisations. These organisations include research groups, academic institutions, and hospitals. The models are developed to collate data that offers key insights to policy makers, such as calculating demand for medical services, planning for required health support technologies such as ventilators and personal protective equipment. As bad as the pandemic is, it has brought the need for data analytics to the fore and forced people to look at the way they do business differently.

But first things first, let’s try  and define what data science is, for someone who may be wondering what it is we are talking about.  Data science is the study of data. It involves developing methods of recording, storing, and analysing data to effectively extract useful information. The goal of data science is to gain insights and knowledge from any type of data — both structured and unstructured.

Who is using data?

As I have mentioned above, we are all using data in one way or another, businesses, and individuals alike. As individuals one may not be cognisant of this reality because one just doesn’t see it that way, but we are all using data. However, most businesses are intentionally making use of data science, think of businesses that rely on quick, agile decisions in order for them to remain competitive.

Data analytics assists businesses in utilising their data to identify new opportunities. Once new opportunities have been discovered,  management can make informed  and intelligent business decisions, more systematic operations, which in turn lead to the bottom line – higher profits.  In other words, business analytics help both small and big companies to maximise the value of their data, bring out insights, build plans and respond in real-time to customer demand.

Some of the industries that have adopted the use of data analytics include the travel and hospitality industry, where turnarounds can be quick.  They can collect their customer data and figure out where there may be a problem and how to address it, if any. The retail industry uses quite a large amount of data to meet the demands of shoppers that are constantly changing. The data collected can assist them to identify trends, recommend services and products and ultimately increase their profits.

These are just a couple of examples, industries such as healthcare also make use of data to make quick decisions, manufacturing companies often record the runtime, downtime, and work queue for a number of machines and then analyse the data to better plan the workloads, to allow the machines to operate at their optimal level.

Gaming companies use data analytics to set reward schedules for players that keep the majority of players active in the game. And Content companies use many of the same data analytics to keep consumers clicking and liking or watching etc.

In our Change Conversation with Adelaide, she stressed the fact that there’s never been a better time to learn data analytics and enter the workforce as a data scientist than now. Companies are becoming more tech advanced, there are more opportunities in the data space. Adelaide encouraged girls, especially those who still have the opportunity to choose subjects related to the tech industry, to explore data science as a field of study. The job landscape in the data space looks promising and opportunities extend across multiple industries.  Even better, the nature of the job often allows for remote work flexibility and sometimes, self-employment. As the industry is changing, the number of women going into tech is slowly increasing, women no longer see this field as one meant for men alone. More and more people are realising that it’s about ability and not about gender.

Types of jobs that require knowledge in data analytics:

  • Quantitative Analysts

They are highly sought after especially in financial fields, to seek out potential financial investment opportunities or risk management problems.

  • Data Scientists

They able to understand data from a more informed perspective to help make predictions. Their positions require a strong knowledge software tools, programming languages like Python or R, and data visualisation skills to better communicate findings.

  • Data Engineers

Data engineers often focus on larger datasets and are tasked with utilising the infrastructure surrounding different data analytics processes.

  • Business Intelligence Analysts

Their most fundamental job is to find patterns  and value in their company and industry data.

  • Data Analyst

Just like the job title implies, analyse company and industry data to find value and opportunities.

  • T Systems Analyst

They use and design systems to solve problems in information technology.

  • Marketing Analysts

Digital marketing also requires a strong knowledge of data analytics.

What we took away from this Change Conversation:

  • As a woman in tech, own the work you are given, don’t be isolated, when you are given the opportunity, run with it. Try to do even the work where the general perception is that only men can do it and learn while doing it, to refine your skills.
  • Speak up, don’t allow yourself to be doubted. Be able to convert technical language to simple language and show people that you can articulate your work in a way that a lay man can understand.
  • Evolve, and adapt. You can’t use obsolete skills in this industry, data science is beyond just reporting and mining it. You need to know what algorithms and other tools to use in order to inform businesses.
  • Data science is progressing, keep yourself relevant, always be on the learning path; learn cloud concepts such as python, and all coding languages; keep up with the trends by attending data and analytics conferences and enroll yourself in free online courses.
  • There is a lot of impact that data and analytics are having now. If you are going to start a business, you better be using data to inform yourself.

For more on Data Analytics, please visit our Youtube channel at https://www.youtube.com/watch?v=lmJk5mzuliA and watch our Change Conversation video with Adelaide Matsika.


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