Strategic Data Acquisition – the first step to use the potential of AI

Many organisations fail to understand the concept of AI-based businesses. It is often mistaken that the simple introduction of neural networks or deep learning mechanisms will be the factor that transforms a particular traditional company into an AI-driven business. We have to take into account many more factors. In the article we want to introduce strategic data acquisition concept and convince of its significance in the process of implementing machine learning and AI solutions.

It would be incorrect to state that there is one factor that makes an AI organisation. However, Andrew Y. Ng, a computer scientist and professor of computer science at Stanford University, enumerated a few aspects that he believes are necessary for a company to truly transform into an AI organisation. He believes that such a transformation is based on data and conscious decisions associated with it. In other words, you need: strategic data acquisition and unified data warehouses.

There are over 2.5 quintillion bytes of data produced EACH DAY

According to IEA statistics, the internet traffic has been rising at an exponential rate and will continue to do so. It is predicted that in 2021 it will be twice as big as it was in 2018.

Data centre workloads present a positive linear trnd over the years and continue to increase. It shows a constant increase in the amount of data produced each year.

Strategic Data Acquisition

Strategic data acquisition is necessary to remain in a competitive position on a market. Big data means the amount of data that cannot be processed through traditional methods. They has to be analysed by automated means.

Gathering data about customers is vital to learn their behavioural patterns. It will allow you to adjust your strategy to gain more customers, or convince them to make the switch to use your services. This text will shed some light on a few aspects affecting the process of introducing strategic data acquisition into an organisation.

Structured Data vs. Unstructured Data

Structured data is data that is generated within the relational database management system (RDBMS). Sales transactions, ATM activity, flight’s reservation, storing names, and phone numbers all classify as structured data.

Unstructured data is basically all the other data and, just as structured data, can be generated by humans and machines. It includes email message field, social media, image files, video files, sensor data, and all other compound data.

There is also common ground between the two, being semi-structured data. Emails are a perfect example of such. They possess some structured information, like the name of the recipient and sender, but the text message within the email is a text file and thus an unstructured data.

Analysis of structured data is much simpler and requires only traditional analytics tools. Therefore it is much cheaper than analysing the more complex, unstructured data. To analyse unstructured data, the current most effective tools are based on machine learning.

Search for processes that can be done autonomously

Start looking for the places where it’s possible for you to implement AI systems. Andrew Ng mentioned a concept: “Anything a person can do with less than a second of thought can now probably be done autonomously.”

Though it is not an absolute rule, as it’s simply untrue in many cases, it can give you an idea of where to begin. Short processes are easier to become automated than the longer ones, as they are usually less complex and require lesser volumes of data.

Machine Learning process in 5 steps

Step 1 – Collecting Data

As you can see above, the ML process starts with data collection, which is the most crucial step. As it’s a first step in constructing a model, strategic data acquisition is vital in collecting relevant data that corresponds to getting the desired outcome. To correctly analyse data, businesses hire specialists in this field. Referred to as ‘The Hottest Job of the XXI century,’ data scientists are persons in charge of making decisions about collecting and interpreting vast volumes of data.

Summary

To conclude this information, to implement strategic data acquisition, it’s vital to correctly classify data within the company. This way we are able to adjust adequate analytical methods to each type. Before launching a product, every leading AI organisation should consider whether it’s possible to plan a path in data acquisition that results in a defensible business.

Strategic data acquisition is the first step in the process of creating AI-based company.

If you don’t want your business to fall behind in the context of this rising technology, then you should quickly introduce necessary changes to make the transformation. AI-based innovation might be the most vital and the biggest game-changer in the last few years. It is necessary to make such changes to stay on the market on not fall behind the competition.

Would you like to know more about the next steps to becoming an AI-based company? Read the second part of this article, where we describe data management and analysis! To make sure you don't miss it, you can subscribe to our newsletter below.