Data-driven digital economy
Data is regarded as a lifeblood of many industries and in turn as a fuel for digital economies. Data-driven digital economy uses information and digital technologies (Cloud, Big data analytics, Artificial Intelligence, Internet of Things) coupled with talent to create economic value for everyone. However those at the forefront of these business and technology innovations will benefit the most. Organizations are beginning to see data as a valuable asset. According to the dictionary, data is facts and statistics collected together for reference or analysis. Data can be qualitative (Text) or quantitative (Numbers). It can also be pictures and videos.
Data, for instance, must be accessible, reliable and up-to-date, for it to be effective. It can be structured, unstructured and semi-structured. There's human generated data, for example, emails and text messages; webpages; online purchases etc. Data is also generated by machines, for example, smartphones, computers, RFID readings, sensors etc. In fact more devices means more data generation which may lead to opportunities for many industries and society. For example, according to Statista (2017), the number of smartphone users in South Africa is estimated to reach over 25 million by 2022. Statista (2016), Internet of Things (IoT) connected devices installed base worldwide is estimated to reach 75 billion by 2025.
Data lead to valuable insights which can make a huge difference in society and business. For example it can improve customer experience and help strengthen customer relationships. Social media contributes greatly to this data surge, for example, every minute of the day, an estimated 300 hours of new videos are uploaded on Youtube, 347 222 tweets, 4 166 667 Facebook posts like, 1 736 111 Instagram photos post like, to name a few (Wersm, 2017). Technologies that are contributing to this data surge is of course social media platforms, e-commerce (Amazon), IoT and soon Internet of Everything (IoE). All this leads to huge amount of data collected every second, minute, hour, days etc. Some examples of smart devices and applications include "Smart Everything". This means any device, asset, or software that can communicate, sense, transmit and interact using digital technologies. Anything that is digital these days is considered "smart". Such devices or appliances may include, smart application, smart homes, smart stoves, TVs, phones, watches, fridges, air conditioners, meters, to name a few. These devices, assets or applications are entrenched in everything we do from when we wake up, in our homes, in the office and until bedtime. Very soon we may coexist with smart robots as well. Imagine living a fully digital lifestyle. Smarter business models are already disrupting traditional business models.
The General Data Protection Regulation (GDPR) is expected to come to law from 25 May 2018. This regulation intend to strengthen and unify data protection for all individuals within the European Union (EU). It also addresses the export of personal data outside the EU. The GDPR aims primarily to give control back to citizens and residents over their personal data (Wikipedia, 2018). This may present some challenges for big corporate and government.
Big data
Big data refers to data because of its size, velocity, volume, variety cannot be easily stored, manipulated and analysed with traditional methods. Based on these attributes, big data requires powerful computing power. These requirements can be addressed using cloud computing and hadoop clusters. The rise of big data promises great opportunities for a data-driven digital economy, including accelerated innovation and growth, and increased productivity and competitiveness. Artificial Intelligence (AI), for example require big data to be effective. To name a few opportunities for example using big data, analytics, AI and machine learning (ML), Google now, makes recommendations before you ask for them; and based on content you consume, Netflix makes recommendations about other movies you might like; and Amazon, makes recommendations for books etc.
Hyacinth (2017) AI is a branch of computer science that emphasizes the creation of intelligent machines or programs that think, learn, and react like human beings. Today AI technology has made its way into a host of products, from search engines like Google, voice assistants like Amazon Alexa, autonomous vehicles, to a range of "smart" consumer devices and home applications.
Six key steps to achieve full benefits of data-driven digital economy:
Understand business or social goals and drivers.
Identify relevant datasets.
Collect data.
Store data.
Mine and transform the data.
Thorough analysis.
These steps will lead to valuable insights, better strategies and decision making. The data-driven economy enhances strategy analysis and formulation, for example using algorithmic future prediction and providing alternative choices or options.
Five data strategies and benefits:
1. Optimization strategy
Optimization of value chains by identifying patterns in data to improve performance and efficiency. This will help drive economies of scale, reducing operational expenditure and in turn improve customer experience.
2. Healthcare strategy
Improve diagnosis practice to minimize risk of infection in the healthcare sector. Through data-driven algorithms, machines will be able to respond to health concerns and even make diagnoses. This will enable health institutions to predict and prevent decease spread.
3. Retail strategy
In retail, customer data can be analyzed to identify purchase patterns, behaviors, customer lifestyle and preferences. This will lead to tailor made products and services to meet the customer needs.
4. Farming strategy
In farming, data can be collected using IoT through sensors and drones coupled with AI for analysis to provide better insights for the farmers to improve productivity.
5. Organizational effectiveness strategy
Organizations can use data to improve customer experience and employee productivity. To assess risk, cultivate culture, improve decision making and talent management.
These are few examples where a combination of innovation, data analytics, AI, ML and IoT can make a huge difference in business and society. The difficult part of working with data is collecting enough, cleaning it and selecting relevant datasets for analysis. The downside may be that statistics rely on information from the past, for example trend analysis. Thus forecasting is based on historical trends and cannot predict something that is unexpected or unknown. For example, very few people predicted Brexit and the outcome of the 2016 US elections.
Data-driven digital economy contribute to the abundance of information on the internet. Having quality data and information is no longer a competitive advantage, because many people or organizations may have access to the same data. Creativity, innovation, how you understand and analyse the data, apply the information practically to address business and social problems is the new competitive advantage. This will inevitably lead to value creation and economic growth. However, to unlock these opportunities, data-driven digital economy require us to learn new skills, in order to stay relevant and keep up with the rapid pace of technological innovation. Business innovation, for example, structures, processes, ways of working etc. will also need to keep up with technology innovation. Maintaining the balance between the two could lead to new forms of value creation, for instance, jobs, products, services and markets.
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