Data modelling in the digital age of AI

The importance of data continues to grow in today's data-centric digital economy, organisations are increasingly seeking competitive advantages by transforming data into meaningful insights that deliver operating efficiencies using reports and analysis. This is precisely why data modelling has become so widespread and is slated to become self-serving in the coming years.  It provides a uniform method to define and categorise databases across all systems, which enables various applications to store data in an identical format.

Technologies such as Machine Learning (ML) can further optimise data modelling and simplify complex tasks. Despite the deluge of data, extracting insights is a delicate affair and requires considerable data structure designs and architecture efforts. This crucial step begins with data modelling: establishing the requirements and formats to transform collected data into useful, and structured information. Data modelling hinges on visual representations of information systems for establishing different data types, relationships between data structures, and other data model attributes. Additionally, these models address co-related business needs/use cases, allowing for greater contextual relevance and deeper domain-specific insights.

International Data Corporation reports that 60% of organisations want to improve products, services, and customer experiences digitally and intend to increase spending on digital activities. In this context, organisations will benefit by allocating their funds to data modelling as it focuses on drawing out more benefits with fewer costs.  There are several trends in the data modelling world, that are set to take the world by storm in the coming years.

Leveraging AI engineering & predictive intelligence

Artificial intelligence (AI) will play a major role in data modelling and can redefine large-scale operations by sifting key insights for data flow and mapping. This, in turn, can free up manual data modellers, and allow them to utilise their time for more strategic and business-related modelling.

Empowered by AI, data modellers can quickly create systematic definitions, relationships, and improvements in the process design to revise outdated data models.

The right model makes all the difference. When it comes to model development, establishing the value of invested effort is critical in your AI implementation journey. Your business goals may require simpler prediction models using established technologies, or emerging technology implementation and complex model development and it is critical to the success of your business to figure this out correctly at the very start.

Predictive data modelling

Prediction modelling is a mathematical and statistical technique for predicting future behaviour or trends through the analysis of input data. Using existing and historical data to anticipate or forecast future events is a part of predictive analytics. Although predictive models are primarily concerned with forecasting future trends, the process also helps to anticipate specific outcomes based on patterns detected from input data. For example, if an email is spam or a transaction is fraudulent, it can indicate the likelihood of such an event.

ML or Deep Learning algorithms are commonly used for predictive models. It is an amalgamation of classification, grouping, forecasting, and outlier models. They are subsets of AI and each application has its own set of applications. ML enables machines to make inferences from raw data. These sets of data provide the computer with knowledge, which can be used for superior functions. After that, Deep Learning facilitates the creation of neural networks which are very similar to a  brain network. The intelligence of such Deep Learning techniques can be observed in autonomous vehicles and medical robots.

Big data modelling

Two key software programs comprise ‘Big Data modelling’ - data modelling and Big Data. Big Data is a term used for huge quantities of data generated by companies in the present-day world. There is no constant pattern in that kind of data, and they are complicated by nature. The ability of companies to analyse such data using conventional methods is nearly impossible.

Big data models are used by large companies that have more resources and are better equipped to connect disparate systems. Businesses must consider their entire big data infrastructure, be it as a whole or in silos, before creating such a model.
The focus of big data modelling is to move from database programming to the creation of a data ecosystem. With the huge amount of work required to model large amounts of data, small to medium-scale companies with lesser resources will be taking advantage of data service products provided by bigger and more well-known firms.

Model-Driven Development (MDD)

Data modelling activities will be crucial for the creation and enhancement of products. This will lead to more companies using MDD, a process that allows them to model and update data in combination with the underlying code and information deliverables. MDD works with a company’s iterative processes to facilitate rapid development. As software platforms become more mature and take on data views that are alive and growing, the modelling of data will evolve.

We often use MDD for varied projects, big or small, to make repetitive tasks a thing of the past. A focus on the basics and the automation of the rest is a key strategy for the use of MDD. It is expected that 80% of applications can be automatically executed without the use of a single line of code, once such a model has been defined. This allows companies to focus their energy on this critical 20% of activities, providing them with a competitive upper hand.

Small and wide data models

The ability to model large amounts of data will not be a luxury for all companies. Small and wide data models are emerging as a powerful alternative that requires less data while providing more insights. The massive influx of data makes it possible for an analyst to analyse and aggregate a multitude of different types of small, large, unidentified, or structurally derived data. Small data modelling focuses on using analysis techniques that are designed to find meaningful information in the smaller and individual data sets.

To understand a customer’s perspective regarding a specific product or service within a short period of time organisations can use small data analysis tools and create a hyper-personalised experience for each customer.

According to Gartner, by 2025, 70% of businesses are expected to shift their focus from big data to small and wide data. It is not a surprise that the trend has gone up in favour of smaller and more extensive data. From 2022, big data ecosystem technologies were implemented by only 15.54% of the participants as per a Data Management Survey by Dataversity.

Data models are essential to bring order and meaning to the countless volumes of chaotic data that many companies are dealing with today. Going forward, the traditional approaches for presenting data assets will be reinvented with the modern applications of data modelling.

With data modelling, organisations can develop a robust and efficient database that enables them to fulfil data requirements and is supportive of their business objectives. It is easier than ever to create custom, industry-specific data models that can be scaled with any new digital disruptions, ushering in a new era of innovation.

This article was first published on The Evolving Enterprises

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