Data is the most important asset for modern organisations as it helps us achieve various objectives in the most efficient way possible.
Today, data helps decision-makers understand the relationship between business processes and results. In this way, the right data is paramount to making better business decisions.
Understanding raw data, however, is just as hard as building a house without a blueprint. Fortunately, certain methods and techniques help us leverage the full potential of data-backed decision-making.
One way organisations can understand the relationship between data and their business processes is through data modelling. In this post, we explore data science modelling techniques and how they help you achieve organisational goals that make you more competitive.
Data modelling is the process of presenting the relationships between data objects in a visual, logical and conceptual manner to help decision-makers organise and define their business processes.
This process considers the data requirements of an organisation and presents them in an easily understandable way. While it doesn’t specify the operations performed on any data object, it can improve your operations by organising your data.
The three primary types of data models are:
Beyond these, there are five different data science modelling techniques, which encompass:
With data modelling techniques, it’s easier to gain complete control over your definitions and metadata. This comes with certain benefits.
Data modelling techniques help businesses build fast and powerful databases, which are critical for powerful data analytics. A good database will accelerate data processing time, making business processes quicker and more efficient.
Errors can cause catastrophic damage to the internal and external operations of a business. Given that data modelling requires you to define your processes and relationships between data objects, this reduces ambiguity and, in turn, the likelihood of errors.
Over time, this can also reduce the cost of your operations.
Different parties in an organisation have varying levels of technical literacy. Regardless of this, all parties need to collaborate frequently and effectively to deliver products or services to the market.
Data modelling techniques make collaboration easier because it is a form of business documentation. It establishes a common and easily understandable vocabulary to facilitate and improve collaboration among teams.
The success of a business depends on how well decision-makers and other stakeholders understand your operations and its processes. Data modelling is critical to this effort because it requires all parties involved to understand the business before creating data models.
Software development, for example, requires developers to understand the functions of the software, the needs of their customers, and the requirements of the project.
Many organisations use multiple business information systems that may or may not support effective communication.
Data modelling allows you to integrate information systems by identifying redundancies, understanding the relationships between these systems, and resolving discrepancies so that communication is not only much easier, but more effective too.
Every organisation strives to optimise their processes to improve performance, efficiency and profitability over time.
Data modelling supports these objectives by helping organisations define and organise business processes to understand the relationship between data objects. While not a novel concept, it has increased in popularity in recent years as we’ve become more dependent on data.
When you understand how data science modelling techniques can improve how you do what you do, it’s much easier to make a mark and get a foot in the door in today’s hyper-competitive business environment.
We always talk about how data analytics platforms can generate the necessary insights organisations need to optimise business operations. But, we seldom dive into the modeling techniques data analysts use to breakdown data and generate useful insights.There are several modeling techniques at an analyst’s disposal, but in the interest of time, we are only going to cover the most essential data science modeling techniques, along with some crucial tips to optimise data analysis.
There are several data science modeling techniques data analysts use, some of which include:
Linear regression is a data science modeling technique that predicts a target variable. It completes this function by finding the “best” relationship between the independent and dependent variable. The resultant graph should ideally ensure that the sum of all the distances between the shape and the actual observation is small. The smaller the distance between the mentioned points, the smaller the chances of an error occuring.
Linear regression is further divided into the subtypes: simple linear regression and multiple linear regression. The former predicts the dependent variable using a single independent variable. Meanwhile, the latter uses the best linear relationship by using several independent variables to predict the dependent variable.
Non-linear models are a form of regression analysis using observational data modeled by a function. It is a nonlinear combination of model parameters and depends on one or more independent variables. Data analysts often use different options when handling non-linear models. Techniques like step function, piecewise function, spline, and generalised additive model are all crucial techniques in data analysis.
Supported vector machines (SVM) are data science modeling techniques that classify data. It is a constrained optimisation problem with a maximum margin found. However, this variable depends on the restrictions that classify data.
Supported vector machines find a hyperplane in an N-dimensional space that classifies data points. Any number of planes could separate data points, however, the key is to find the hyperplane that has the maximum distance between the points.
You may have heard of this term in the context of machine learning and AI, but what does pattern recognition mean? Pattern recognition is a process where technology matches incoming data with the information stored in the database.
The objective of this data science modeling technique is the discovery of patterns within the data. Pattern recognition is different from machine learning because the former is a subcategory of the latter.
Pattern recognition often takes part in two stages. The first is the explorative part, where the algorithms look for patterns without a specific criteria. Meanwhile, the descriptive part is where the algorithms categorise the discovered patterns. Pattern recognition can analyse any type of data, including texts, sound, and sentiment.
Resampling methods refer to data science modeling techniques that consist of taking a data sample and drawing repeated samples from it. Resampling generates unique sampling distribution results, which could be valuable in analysis. The process uses experiential methods to generate a unique sampling distribution. As a result of this technique, it generates unbiased samples of all the possible results of the data studied.
Bootstrapping is a data science modeling technique that helps in different scenarios, like validating a predictive model performance. The method works by sampling a replacement from the original data with certain data points that are not used as test cases. By contrast, there is another method called cross validation, which is a technique used to validate model performance. It works by splitting the training data into different parts.
Most of the data science modeling techniques are crucial for data analysis. However, along with these data analysis models, there can be several viable techniques used to optimise the data science modeling process.
For example, data visualisation technology can go a long way in optimising the process. Staring at rows and columns of alphanumeric entries makes it difficult to conduct any meaningful analysis. Data visualisation can make the process much easier by converting all alphanumeric characters into graphs and charts.
The right data analytics platform can also play a huge role in optimal data analysis. With optimised data analytics platforms, it can increase the rate of data analysis, delivering insights at an even faster rate.
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Everyday, 2.5 quintillion bytes of data are generated. With so much information at our disposal, it is becoming increasingly important for organisations and enterprises to access and analyse relevant data to predict outcomes and improve services.
However, arbitrarily organising data into random structures and relationships is not enough. In order to access the data properly and extract the most out of it, it is essential to model your data correctly.
The Big Data revolution has arguably provided a more powerful information foundation than any previous digital advancement. We can now measure and manage large volumes of information with remarkable precision. This evolutionary step allows organisations to target and provide more finely-tuned solutions and use data in areas historically reserved for the “gut and intuition” decision-making process.
Data science modelling techniques play a crucial role in the growth of any organisation that understands the importance of data-driven decisions for their success. Having your data in the right format ensures that you can get answers to your business questions easily and quickly.
In simple terms, data modelling is nothing but a process through which data is stored structurally in a specific format. Data modelling is important because it enables organisations to make data-driven decisions and meet varied business goals.
Typically, a data model can be thought of as a flowchart that illustrates the relationship between data. It enables stakeholders to identify errors and make changes before any programming code has been written. Alternatively, they can be introduced as part of reverse engineering efforts to extract other data models from existing systems.
Data modelling represents the data properly in a model. It rules out any chances of data redundancy and omission, helping analysis and processing. Furthermore, data modelling improves data quality and enables concerned stakeholders to make data-driven decisions. This clear representation makes it easier to analyse data properly. It provides a quick overview of the data, which can then be used by the developers in different applications.
Since a lot of business processes depend on successful data modelling, it is necessary to adopt the right modelling techniques to get the best results.
There are three types of data modelling techniques for business intelligence: Conceptual, logical, and physical.
Conceptual data modelling examines business operations to create a model with the most important parts (such as describing a store’s order system). Essentially, this data model defines what data the system will contain.
Logical data modelling examines business functions (like manufacturing and shipping) intending to create a model describing how each operation works within the whole company. It also defines how a system should be implemented: By mapping out technical rules and data structures.
Physical data modelling examines how the database will actually be implemented, intending to model how the databases, applications, and features will interact with each other. Here, the actual database is created while the schema structure is developed, refined, and tested. Data models generated should support key business operations.
Clearness: How easy it is to understand the data model just by looking at it.
Flexibility/scalability: The ability of the model to evolve without making a significant impact on code.
Performance: You can attribute performance benefits based on how you model the data.
Productivity: An organisation’s model needs to be easy to work with.
Traceability: The ability to manoeuvre through historical data.
In the end, it is all about data: Data comes flooding in from everywhere, data is processed following business rules, and finally, data is presented to the user (or external applications) in a convenient way.
With new possibilities to easily access and analyse their data to improve performance, data modelling is morphing too. More than arbitrarily organising data structures and relationships, data modelling must connect with end-user requirements and questions, as well as offer guidance to help ensure the right data is being used in the right way for the right results.
Business performance, in terms of profitability, productivity, efficiency and customer satisfaction can benefit from data modelling that helps users quickly get answers to their business questions.
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