Datafication

In business, datafication can be defined as a process that “aims to transform most aspects of a business into quantifiable data that can be tracked, monitored, and analyzed. It refers to the use of tools and processes to turn an organization into a data-driven enterprise.” 

There are three areas of business where datafication can really make an impact: 

• Analytics In today’s data-driven world, analytics is king. By collecting and analyzing data, businesses can gain valuable insights into consumer behavior, trends, and preferences, allowing them to make informed decisions that drive growth and success.
• Marketing Campaigns Marketing campaigns can be supercharged with datafication, allowing companies to personalize ads and offers for specific customers based on their interests and behaviors. 
• Forecasting Predictive analytics can help businesses forecast future trends and stay ahead of the competition by anticipating changes in consumer demand.

Importance of datafication in a business organisation

Datafication helps businesses improve their products and services by using real-time data. Plus, it is an important component in collecting customer feedback about the quality of the products and services offered by any company.

Take data-driven marketing strategies for instance. As one of the most important aspects of digital marketing, this process involves collecting customer insight through various channels such as social media, email and other digital platforms. The information can be used to create personalised campaigns for each client and targeting the right audience persona

The future of business is data fluency

Data-driven decision-making and the ability to make sense of data being presented at our fingertips are becoming more important than ever. The rise of artificial intelligence, machine learning, big data analytics, and other technologies have made this a reality.

Blockchain

Blockchain technology has been around for more than 10 years now. It’s time to take advantage of its potential to transform how businesses interact with their customers.

The blockchain is a distributed ledger that records transactions between two parties without needing a third party. This means that no one needs to rely on anyone else. The system is secure because all participants have access to the same information at the same

AIOps

AI-as-a-service (AIOps) is a term used to describe the use of AI tools within organisations. AIOps are often cloud-based, meaning they are accessible through a web browser or mobile app. They also provide real-time insights into processes and operations. As a result, AIOps can be used for predictive maintenance, process optimisation, and other operational improvements.)

The most common form of AI is machine learning. Machine learning involves training an algorithm on data that has been labeled by humans as either positive or negative. The algorithm then uses this information to make predictions about new data.

For example, if you have a dataset of people who have purchased a product and those who haven’t, you could train an algorithm to predict whether someone will purchase something in the future. This type of AI is called supervised learning because it requires human input during the training phase.

Unsupervised learning doesn’t require any human intervention. It works best when there is no clear distinction between positive and negative examples.

FinOps

Financial Operations Management (FinOps) is the practice of managing financial activities across an organisation. FinOps includes everything from budgeting to forecasting to risk management.

It is not just about financial reporting anymore. Financial reporting is only part of what FinOps encompasses. And here, datafication plays a huge role, as it allows for the integration and analysis of data that was previously siloed in different systems.

The term fintech has been used to describe this new wave of technology. It's a combination of finance and technology. In fact, there are many examples of companies that have successfully implemented FinOps such as Google Finance and Intuit QuickBooks Online.

Cognitive Computing

The term cognitive computing is a catch-all phrase for the study of artificial intelligence, machine learning and human–computer interaction. Here data mining is used to extract knowledge from large amounts of information. The goal is to make computers think like humans in order to solve problems that we cannot do ourselves.

One good example is the rise of solutions like natural language processing (NLP) or pattern recognition techniques used now to analyse text, images and even speech.

Edge Computing

Edge computing refers to the use of cloud-based services and technologies at the edge of a network, such as on mobile devices or in wireless sensors. Edge computing is an emerging technology that has been gaining attention for its potential to improve data processing speed and reduce energy consumption.

The main advantage of edge computing is that it can be used to process data locally without having to send all the information back to the cloud. This reduces latency and improves user experience by reducing bandwidth usage.

Microweather

The term microclimate (or microclimate) is used in meteorology to describe the local weather conditions at a small scale, such as within an individual building or on a street. Microclimates are often characterised by differences in temperature and humidity from those of the surrounding area.

The predictions obtained from the data collection can help consumers, companies and, especially, farmers. In addition to providing detailed climate forecasts, the system uses sensors to measure air quality, wind speed and direction, rainfall intensity and duration, soil moisture content, and other factors.

Warehouse Management Tech

Autonomous robots and analysis prediction are some of the buzzwords surrounding this emergent niche that aids warehouse management more efficiently.

The idea is to use a robot to perform tasks in an automated fashion, which can be done by using sensors to detect objects or other entities within its environment. The robot then uses these inputs to decide what it should do next. This process is repeated until the task has been completed.

A common example would be a picker robot picking items from shelves and placing them into boxes for shipping. Datafying the robot’s movements allow us to predict where it will go next based on previous actions. This data can then be used to plan routes through the warehouse so as not to waste time moving around empty space.

Online Reputation Management

Online reputation management (ORM) has become an integral part of HR professionals' toolkit. ORM is not just about monitoring online reviews; it is more than that. It is about managing the online presence of your organisation.

The goal of ORM is to ensure that your brand or company name does not get tarnished by negative comments posted on review sites such as Google, Yelp, TripAdvisor, Facebook and others.

We have entered a new era for the human resources industry, and it is a digital one. Many of the strategies around HR are also being datafied, and hiring is no different.