The unmistakable need for datafication

datafication

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Since the term big data started this big wave of everyone turning to data as a resource, many companies jumped on board. But it’s easy to utilize data when you can precisely point it out or quantify it. What happens with all the events that you previously couldn’t pinpoint? This is where datafication comes in. 

Every second of our life is an event. Our behavior, even the behavior and actions of machines and similar, can be described, but it’s often abstract enough that you can’t use numbers or letters to do that. However, just by identifying these events, technological advances (IoT for example) have tried to make these events into useful data points.

What is datafication?

Datafication is the process of turning events and information into useful, accessible, and actionable data. What does that mean? It means that behavior, actions, and events, that weren’t expressed as data, can now be turned into something utilizable in data-driven projects and analytics. Information that wasn’t measurable before, is now. 

Even though datafication was a term coined in 2013, it is relevant today more than ever. For example, looking at an average person’s day, we can see how many events and actions occur. And each of them can be described and measured due to technology that tracks our activity.

Sensors, smart devices, production processes, business processes, and others, collect and generate large amounts of data that move at a fast pace. Datafication brings those assets to the forefront and can be used to stimulate or generate revenue, minimize risks, and maximize ROI.

Datafiction collects that data, stores it, processes it, and analyzes it through various tools, algorithms, and methods to deliver valuable insights and metrics. But it should not be equalized with digitization. Datafication is a broader aspect of converting events into purposeful data that brings value. 

Datafication defines the quality and potential of any business

Quality data creates value. Without good data, you can’t have successful and accurate data-driven projects, especially in machine learning and AI. A larger data set or more relevant data means greater accuracy and better performance of data models. Not everything needs or should be turned into actionable data, but identifying that what could bring value, will strengthen the business and its performance. Ultimately, datafication can be defined as a new business model

By turning focus to data and everything that can be successfully turned into data points, a new strategy emerges. There is no spontaneous decision-making based solely on personal preferences or intuition (not to say this isn’t valuable), but rather on specific data-backed information and correct metrics and insights. Now, certain decisions have a good foundation and can be explained on the “why, how, and what for” front. But, datafication is not only about decision-making. It’s also about creating business processes that can be optimized and run smoothly. It’s about creating a customer experience that will maximize consumption and loyalty. 

Imagine increasing revenue, ROI, lowering costs, raising efficiency, and more, solely because your business decided to utilize its greatest asset – data that previously wasn’t available, but now is. 

Although, it should be mentioned that datafication is not easy or quick to implement. It will bring loads of benefits and open up new opportunities if properly executed. However, it should be done carefully and with the help of experts. Sometimes, datafying something is not optimal or even correct. 

Data without context is useless

Datafication doesn’t mean only transforming events into data. It is also about implementing data catalogs, metadata, new forms of data storage, processes, and procedures in data collection and processing. It’s about giving meaning to data. 

For example, data lineage and data catalogs are important in ensuring that data comes from a trustworthy source, thus trying to avoid data poisoning and similar threats or data manipulation. With data catalogs, you can trace data back to the source. Each step from transforming daily occurrences to data points is vital in ensuring datafication is expertly done. As said previously, not everything should be turned into quantifiable data points. 

It might seem like every event in your business is of immense value, but if you can’t explain it in a way that benefits your processes or your strategy, they might not be worth the trouble. Each piece of information should bring something to the table. Its context is what makes it valuable. That’s why it’s important to define data sources and important information before starting any datafication process. 

AI and ML drive the need for better data

With the way AI and ML are evolving, data needs to keep up. Each model gets better depending on the data it uses and how much data can it ingest. It could probably be fine with the traditional sorts of data somebody collects or generates, but the point is to find potential and unused data to discover new frontiers and untapped opportunities. Some behaviors or events weren’t described before or they couldn’t be, but now with new technologies it all changes. That’s why AI and ML models can work on data that opens up new doors and on new events that enhance business operations.

By datafying almost every event, interaction, or behavior inside and outside of a business, one can predict the behavior of various processes, operations, or customer behavior and purchases. Pushing that information to the forefront unleashes AI and ML to its fullest potential.

Towards the necessity of data

It is not always easy to balance the need for data and not turning everything into data. Those two, let’s call them forces, will pull businesses in two directions. But, you cannot disregard the fact that if your organization isn’t data-driven yet, it should be.

Even though datafication is nothing new, it has gained new traction with the appearance and rising magnitude of AI and machine learning. Companies all around will speed up the process of getting more data-driven just to stay on top of the market. It’s inevitable. But data doesn’t just happen. We turn happening around us into data. And it’s a delicate process if accuracy is the target. 

One common mistake companies make is to datafy events and occurrences that are either valued wrongly or expressed the wrong way. Often, a lot of the time, data points are not enough. It’s either bad data or not enough relevant data to actually do something with it. Some companies can collect or generate a lot of data, but it’s not always utilizable. Sometimes, the datafication process isn’t done correctly, leading to a mess of databases, ETL processes, or not properly stored data. 

Jump in before it’s too late

Datafication is going to become inevitable in the upcoming years. We have witnessed traditional businesses that dealt with physical stores, products, and services transform to digital. But we also witnessed companies taking data and turning it into service enhancers and huge parts of their products, business processes, and customer service. Netflix, Amazon, and Spotify are only some of the companies that used datafication as a competitive advantage and that’s why they are staying on top. 

With the market getting oversaturated with products and services that are so similar in offer, finding that segment that will differentiate them will be integral. This is where proper events identifications and turning them into data points will come in. But be careful, datafication is not a one-and-done. It is a continuous process of handling existing and identifying new data but in a structured and carefully planned manner. 

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