GTP-4 AI Language Model: Deep Dive Into Advanced Technology

GTP-4 AI Language Model: Deep Dive Into Advanced Technology


All Information Regarding GPT-4

We live in unusual times, where each new model launch dramatically transforms the field of artificial intelligence. OpenAI unveiled DALLE2, a cutting-edge text-to-image model, in July 2022. Stability followed a few weeks later. Stable Diffusion, an open-source DALLE-2 variant, was introduced by AI. Both of these well-liked models have demonstrated promising outcomes in terms of both quality and capability to comprehend the prompt.

Whisper is an Automatic Speech Recognition (ASR) model that was just released by OpenAI. In terms of accuracy and robustness, it has fared better than any other model.

We can infer from the trend that OpenAI will release GPT-4 in the next months. Large language models are in high demand, and the success of GPT-3 has already shown that people anticipate GPT-4 to deliver better accuracy, compute optimization, lower biases, and increased safety.

Despite OpenAI’s silence regarding the release or features, we will make some assumptions and predictions about GPT-4 in this post based on AI trends and the data supplied by OpenAI. Additionally, we will study extensive language models and their uses.

What is GPT?

A text generation deep learning model called Generative Pre-trained Transformer (GPT) was learned using internet data. It is employed in conversation AI, machine translation, text summarization and classification.

By studying the Deep Learning in Python skill track, you can learn how to create your own deep learning model. You will learn about the principles of deep learning, the Tensorflow and Keras frameworks, and how to use Keras to create numerous input and output models.

GPT models have countless uses, and you can even fine-tune them using particular data to produce even better outcomes. You can cut expenditures on computing, time, and other resources by employing transformers.

Prior to GPT

Most Natural Language Processing (NLP) models have been trained for specific tasks like categorization, translation, etc. before GPT-1. Each of them was utilizing supervised learning. Two problems emerge with this form of learning: the absence of labeled data and the inability to generalize tasks.


Improving Language Understanding by Generative Pre-Training, GPT-1 (117M parameters) paper, was released in 2018. It has suggested a generative language model that was honed for particular downstream tasks like classification and sentiment analysis and trained on unlabeled data.


Language Models are Unsupervised Multitask Learners, GPT-2 (1.5B parameters) paper, was released in 2019. To create an even more potent language model, it was trained on a larger dataset with more model parameters. To enhance model performance, GPT-2 employs task conditioning, zero-shot learning, and zero short task transfer.

GPT model


Language Models are Few-Shot Learners, GPT-3 (175B parameters) study, was released in 2020. Compared to GPT-2, the model contains 100 times more parameters. In order to perform well on jobs down the road, it was trained on an even larger dataset. With its human-like tale authoring, SQL queries, Python scripts, language translation, and summarizing, it has astounded the world. Utilizing in-context learning, few-shot, one-shot, and zero-shot settings, it has produced a state-of-the-art outcome.

What’s New in GPT-4?

Sam Altman, the CEO of OpenAI, confirmed the reports regarding the introduction of the GPT-4 model during the question-and-answer portion of the AC10 online meetup. This section will make predictions about the model size, optimal parameter and computation, multimodality, sparsity, and performance utilizing that data in conjunction with current trends.

Model Size

Altman predicts that GPT-4 won’t be significantly larger than GPT-3. Therefore, we can assume that it will have parameters between 175B and 280B, similar to Deepmind’s Gopher language model.

With 530B parameters, the large model Megatron NLG is three times bigger than GPT-3 yet performs comparably. Higher performance levels were attained by the subsequent smaller model. Simply put, more size does not equate to better performance.

According to Altman, they are concentrating on improving the performance of smaller models. It was necessary to use a massive dataset, a lot of processing power, and a complicated implementation for the vast language models. For many businesses, even installing huge models is no longer cost-effective.

Ideal parameterization

Large models are typically not optimized enough. Companies must choose between accuracy and cost because training the model is costly. For instance, GPT-3 was only trained once, despite mistakes. Researchers were unable to do hyperparameter tuning due to prohibitive prices.

It has been demonstrated by Microsoft and OpenAI that GPT-3 could be enhanced with the use of appropriate hyperparameter training. According to the results, a 6.7B GPT-3 model with tuned hyperparameters improved performance by the same amount as a 13B GPT-3 model.

The best hyperparameters for the larger models with the same architecture are the same as the best for the smaller ones, according to a novel parameterization theory (P). It has made it much more affordable for academics to optimize big models.

Ideal computation

Microsoft and OpenAI demonstrated last month that GPT-3 might be enhanced further if the model was trained with the ideal hyperparameters. They discovered that a 6.7B version of GPT-3 significantly improved its performance to the point where it was on par with the model’s initial 13B performance. For smaller models, hyperparameter optimization produced a performance boost equivalent to doubling the number of parameters. The best hyperparameters for a small model were also the best for a larger one in the same family, according to a new parameterization they discovered (called P). They were able to optimize models of any size using P for a tiny portion of the cost of training. The larger model can then almost instantly get the hyperparameters.

Models with optimal computing

Recently, DeepMind went back to Kaplan’s research and discovered that, in contrast to popular belief, the amount of training tokens had just as much of an impact on performance as model size. They came to the conclusion that more computing budget should be distributed equally between scaling parameters and data. By training Chinchilla, a 70B model (four times smaller than Gopher, the previous SOTA), with four times as much data as all major language models since GPT-3 (1.4T tokens, as opposed to the average 300B), they were able to demonstrate their hypothesis. The outcomes were unmistakable. Across a variety of language benchmarks, Chinchilla outperformed Gopher, GPT-3, MT-NLG, and all other language models “uniformly and significantly”: Models today are big and undertrained.

Given that GPT-4 will be slightly bigger than GPT-3, it would require about 5 trillion training tokens to be compute-optimal, according to DeepMind’s findings. This is an order of magnitude more training tokens than are now available. Using Gopher’s compute budget as a proxy, they would need between 10 and 20 times more FLOPs to train the model than they used for GPT-3 in order to achieve the lowest training loss. When he indicated in the Q&A that GPT-4 will need a lot more computing than GPT-3, Altman might have been alluding to this.

Although the extent to which OpenAI will incorporate optimality-related findings into GPT-4 is unknown due to their funding, it is certain that they will do so. They’ll undoubtedly concentrate on optimizing factors other than model size, that much is certain. Finding the ideal compute model size, a number of parameters and a collection of hyperparameters could lead to astounding improvements in all benchmarks. If these methods are merged into a single model, then all forecasts for language models will be wrong.

Altman added that if models weren’t made larger, people wouldn’t believe how wonderful they could be. He might be implying that scaling initiatives have ended for the time being.

GPT-4 will only be available in text

Multimodal models are the deep learning of the future. Because we inhabit a multimodal world, human brains are multisensory. AI’s ability to explore or comprehend the world is severely constrained by only perceiving it in one mode at a time.

However, creating effective multimodal models is much more difficult than creating an effective language- or vision-only models. It is difficult to combine visual and verbal information into a single representation. We have very little understanding of how the brain functions, thus we are unsure of how to implement it in neural networks (not that the deep learning community is taking into account insights from the cognitive sciences on brain anatomy and operation). In the Q&A, Altman stated that GPT-4 will be a text-only model rather than multimodal (like DALLE or MUM). Before moving on to the next iteration of multimodal AI, I’m going to venture a guess that they’re trying to push language models to their breaking point.


Recent research has achieved considerable success with sparse models that use conditional computing to process various input types utilizing various model components. A seemingly orthogonal relationship between model size and compute budget, is produced by these models, which scale beyond the 1T-parameter threshold without incurring significant computational expenses. On very large models however the advantages of MoE techniques are reduced.

It makes sense to assume that GPT-4 will also be a dense model given the history of OpenAI’s emphasis on dense language models. Altman also stated that GPT-4 won’t be significantly bigger than GPT-3, therefore we may infer that sparsity is not an option for OpenAI at this time.

Given that our brain, which served as the inspiration for AI, significantly relies on sparse processing, sparsity, similar to multimodality, will most certainly predominate the future generations of neural networks.

AI positioning

The AI alignment problem, which is how to get language models to follow our intents and uphold our ideals, has been the focus of a lot of work at OpenAI. It’s a challenging subject both theoretically (how can we make AI grasp what we want precisely?) and philosophically (there isn’t a single way to make AI align with humans because human values vary greatly between groups and are sometimes at odds with one another).


InstructGPT, a redeveloped GPT-3 taught with human feedback to learn to follow instructions (whether those instructions are well-intended or not is not yet integrated into the models), is how they made their initial effort.

The primary innovation of InstructGPT is that, despite its performance on language benchmarks, it is rated as a better model by human judges (who are made up of of English speakers and OpenAI staff, so we should be cautious when drawing generalizations). This emphasizes the need to move past using benchmarks as the sole criteria to judge AI’s aptitude. Perhaps even more crucial than the models themselves is how humans interpret them.

Given Altman and OpenAI’s dedication to creating a useful AGI, I do not doubt that GPT-4 will put the information they gathered from InstructGPT to use and expand upon.

Given that it was only available to OpenAI personnel and English-speaking labelers, they will improve the alignment process. True alignment should involve individuals and groups with various backgrounds and characteristics in terms of gender, ethnicity, nationality, religion, etc. Any progress made in that direction is appreciated, though we should be careful not to refer to it as alignment since most people don’t experience it that way.


Model size: GPT-4 will be slightly larger than GPT-3 but not by much when compared to the largest models currently available (MT-NLG 530B and PaLM 540B). Model size won’t be a defining characteristic.

Ideally, GPT-4 will consume greater processing power than GPT-3. It will apply fresh optimality ideas to scaling rules and parameterization (optimal hyperparameters) (the number of training tokens is as important as model size).

Multimodality: The GPT-4 model will solely use text (not multimodal). Before switching entirely to multimodal models like DALLE, which they believe will eventually outperform unimodal systems, OpenAI wants to fully utilize language models.

Sparsity: GPT-4 will be a dense model, continuing the trend from GPT-2 and GPT-3 (all parameters will be in use to process any given input). In the future, sparsity will predominate more.

GPT-4 will be more in line with us than GPT-3 in terms of alignment. It will apply the lessons learned from InstructGPT, which was developed with human input. However, there is still a long way to go before AI alignment, so efforts should be carefully considered and not overstated.

Enterprise Data Strategy Roadmap: Which Model to Choose and Follow

Enterprise Data Strategy Roadmap: Which Model to Choose and Follow

Enterprise sata strategy roadmap

Data Strategy Roadmap

A Data Strategy roadmap is a step-by-step plan for transforming a company from its existing condition to the desired business organization. It is a more extensive, researched version of the Data Strategy that specifies when and how certain improvements to develop and upgrade a business’s data processes should be implemented. A Data Strategy roadmap can help an organization’s processes align with its targeted business goals.

A good roadmap will align the many solutions utilized to update an organization and assist in the establishment of a solid corporate foundation.

The removal of chaos and confusion is a big benefit of having a Data Strategy plan. During the change process, time, money, and resources will be saved. The roadmap can also be utilized as a tool for communicating plans to stakeholders, personnel, and management. A decent road map should include the following items:


  • Specific objectives: A list of what should be completed by the end of this project.
  • The people: A breakdown of who will be in charge of each step of the process.
  • Timeline: A plan for completing each phase or project. There should be an understanding of what comes first.
  • The funding required for each phase of the Data Strategy.
  • The software: A description of the software required to meet the precise goals outlined in the Data Strategy roadmap.

Why Do You Need a Data Strategy Roadmap?

It’s nearly hard to arrive at your end goal if you don’t know where you’re going. Creating a data strategy can assist in breaking down the huge picture into manageable parts. With a roadmap in hand, you’ll always know how far you’ve progressed and whether you’re on schedule.

When developing a data strategy, it is critical to consider all areas of implementation. If you try the monolithic method, you will quickly run out of steam. It’s a good idea to prepare a detailed plan including everything from the project’s scope to its cost before you start any data-related projects.

A data strategy roadmap will allow you to explain to stakeholders and management what you anticipate to achieve. It will also serve as a convenient reference point for any future data initiatives.

Roadmaps can help you envision your approach and keep your entire organization focused on your objectives. Implementing a modern data and analytics platform will improve your organization’s data-driven decision-making process and can assist in transforming big data from a buzzword to a valuable business asset. Just getting started requires a solid understanding of your organization’s goals and the resources needed to achieve them.

Other components of the Data Strategy Roadmap include:


  • The Business Case – Create a business case for implementing a data strategy.
  • Data Governance Strategy – Create an efficient governance system for managing data assets.
  • Data Management Strategy – Determine the resources required to manage the data.
  • Data Quality Assurance Plan – Define best practices for maintaining high data standards for accurate reporting.
  • Plan for Data Analytics – Create a procedure for analyzing data to support decision-making.

Case Study In Business

One of the most important aspects of a data strategy roadmap is defining the business case for a contemporary data platform. The following elements should be included in your business case:


  • What issue does a contemporary data platform address?
  • Are there any extra charges related with the data platform?
  • How much does the total cost of ownership (TCO) cost?
  • What is the expected return on investment (ROI)?
  • What is the deployment timeline?
  • Do we have enough money to finish the project?

Plan For Data Goverance

A solid data governance strategy serves as the foundation for a strong data strategy. You should create a structure for managing your organization’s data assets. You’ll also need to define roles and responsibilities, determine who owns what data, locate the data, and develop policies and procedures for accessing and using the data.

The following aspects should be included in a strong data governance plan:


  • Roles and responsibilities – Explain who will have access to the data, how they will access it, and who will supervise their activities.
  • Data asset ownership – Determine who owns each piece of data and how it will be utilized.
  • Data location – Specify where the data will remain and how it will be accessed.

Plan For Data Management

After you’ve chosen the breadth of your data strategy and the sorts of data you’ll employ, you must select how you’ll manage the data. The data management plan specifies the tools and processes to be used in data management. The following are some important concerns:


  • Data management resources – Make a list of the resources needed to manage the data. Hardware, software, people, and training may all be included.
  • Data classification – Determine the various sorts of data that will be stored. Structured data, such as financial records, and unstructured data, such as emails and text documents, are two examples.
  • Storage options – Select the storage option that best meets your requirements.

Plan For Data Quality Assurance

The data quality assurance strategy specifies the methods and mechanisms that will be utilized to guarantee that the data fulfills your requirements. The following elements are included in a data quality assurance plan:


  • Requirements identification – Describe the standards that must be met before the data can be shared.
  • Metrics definition – Define the metrics that will be used to assess the performance of the data quality program.
  • Data testing methodology – Outline the data testing process.
  • Reporting of results – Report the test results.
  • Process monitoring entails tracking the progress of the data quality program and reporting back to stakeholders.

Plan For Data Analysis

The analytical approaches used to analyze the data are detailed in the data analytics plan. The aspects of an analytics plan are as follows:


  • Process and approach — Your analytical process, from prioritizing to guided navigation to self-service analytics.
  • Data preparation – Describe the actions taken prior to evaluating the data.
  • Define the scenarios that will drive the analytical methodologies using use cases.
  • Describe the business rules and data models that have been applied to the data.
  • Presentation – Explain how the analytics presentation will be used internally and externally.

In conclusion

A data strategy is a plan that describes how businesses will use data to achieve specified business goals. It establishes expectations and provides a clear sense of direction. Creating a data strategy roadmap is a useful tool for assisting with strategy implementation.

Having these expectations outlined in a roadmap engages the entire organization in the journey. This is important for a variety of reasons, the most important of which is that data consumers fully understand the cultural transformation required to become a data-informed organization.

Code Refactoring: Let’s Find Out If This Is Really Necessary | KoderShop

Code Refactoring: Let’s Find Out If This Is Really Necessary | KoderShop

Code refactoring

Code Refactoring: Meaning, Benefits and Practices

The technique of rearranging code without affecting its original functionality is known as refactoring. Refactoring’s purpose is to improve internal code by making many modest changes that do not affect the code’s exterior behavior.

Refactoring code is done by computer programmers and software developers to improve the design, structure, and implementation of software. Refactoring increases code readability while decreasing complications. Refactoring can also assist software engineers in locating faults or vulnerabilities in their code.

The refactoring process involves numerous minor changes to a program’s source code. For example, one technique to refactoring is to enhance the structure of source code at one point and then progressively extend the same modifications to all appropriate references throughout the program. The thought process is that all of the modest, behavior-preserving modifications to a body of code add up. These adjustments keep the software’s original behavior and do not change it.

In his book Refactoring: Improving the Design of Existing Code, Martin Fowler, considered the father of refactoring, consolidated many best practices from across the software development industry into a specific list of refactorings and described methods to implement them.

A few remarks on code

The most popular definition of clean code is that it is simple to understand and modify. Code is never written once and then forgotten. It is critical for everybody who uses the code to be able to work on it efficiently.

The term “dirty code” refers to code that is difficult to maintain and update. It usually refers to code that was added or altered late in the development process owing to time constraints.

Legacy code is code that was passed down from a previous owner or an earlier version of the software. It could possibly be a code that you don’t understand and that is difficult to update.

Remember that. We’ll get back to this later. And now for the main course: refactoring.

Why is refactoring code important?

All programmers must follow the same rule: the code must be short, well-structured, and clear to the developers who will be working with it. Even after a successful software development project, the system must be improved in order to give new features and solutions. It frequently leads to code complexity since the upgrades are applied in a way that makes updates more difficult. Source code reworking can help to improve the code’s maintainability and readability. It can also aid in the avoidance of standardization issues created by the large number of developers providing their own code. Furthermore, reworking reduces the amount of technical debt that developers build as a result of failing to capitalize on opportunities to improve the code. Technical debt is the cost a company will incur in the future as a result of opting for a simpler, faster, but less reliable option today. Any compromise you make in the present to release products or features faster will result in a greater volume of work to do in the future.

What does refactoring accomplish?

Refactoring makes code better by:

  • By resolving dependencies and complications, we can be more efficient.
  • Increase efficiency and readability to make it more manageable or reusable.
  • Cleaner, which makes it easier to read and understand.
  • It is easier for software developers to identify and remedy problems or vulnerabilities in code.

The code is modified without affecting the program’s functionality. Simple refactorings, such as renaming a function or variable across an entire code base, are supported by many basic editing environments.

Code refactoring process

When is it appropriate to refactor code?

Refactoring can be done after a product has been delivered, before adding updates and new features to existing code, or as part of the day-to-day development process.

When the process is carried out after deployment, it is usually carried out before developers move on to the next project. An organization may be able to rework more code at this point in the software delivery lifecycle because engineers are more available and have more time to work on the necessary source code changes.

However, reworking should be done before adding updates or new features to old code.Refactoring at this point makes it easier for developers to build on top of existing code since they are going back and simplifying it, making it easier to read and comprehend.

When a company understands the refactoring process well, it may make it a regular practice. When a developer needs to add something to a code base, they can examine the existing code to determine if it is structured in a way that makes adding new code simple. If not, the developer may refactor the existing code. Once the new code is added, the developer can refactor the existing code to make it more clear.

When is it not necessary to refactor?

It is often preferable to forego restructuring and instead launch a new product. If you intend to rebuild the app from the ground up, starting from scratch is the best alternative. It avoids the need for refactoring, which can be time-consuming while maintaining the same state.

Another scenario is that if you don’t have tests to verify that restructuring has altered the code, you shouldn’t refactor it.

What are the advantages of refactoring?

Refactoring has the following advantages:

  • Because the purpose is to simplify code and minimize complications, it makes it easier to understand and read.
  • Improves maintainability and makes it easier to identify bugs and make additional modifications.
  • Encourages a deeper grasp of coding. Developers must consider how their code will interact with existing code in the code base.
  • The emphasis remains solely on functionality. The original project does not lose scope by not changing the code’s original functionality.

What are the difficulties of refactoring?

However, difficulties do arise as a result of the process. Some examples are:

  • If a development team is in a hurry and refactoring is not prepared for, the process will take longer.
  • Refactoring can cause delays and extra work if there are no clear objectives.
  • Refactoring, which is designed to tidy up code and make it less complex, cannot address software issues on its own.

Techniques for refactoring code

Different refactoring strategies can be used by organizations in different situations. Here are a few examples:

  • Red and green. This popular refactoring method in Agile development consists of three parts. First, the developers assess what needs to be built; second, they ensure that their project passes testing; and finally, they rework the code to improve it.
  • This technique focuses on reducing code complexity by removing unneeded parts.
  • Changing the appearance of items. This method generates new classes while relocating functionality between old and new data classes.
  • This method divides code into smaller chunks and then moves those chunks to a different method. A call to the new method replaces fragmented code.
  • Refactoring through abstraction. This method decreases the amount of redundant code. When there is a big quantity of code to be refactored, this is done.
  • This methodology uses numerous refactoring methods, including extraction and inline, to streamline code and minimize duplications.

Best techniques for code refactoring

The following are some best practices for refactoring:


  • Prepare for refactoring. Otherwise, it may be tough to find time for the time-consuming practice.
  • First, refactor. To reduce technical debt, developers should do this before adding changes or new features to existing code.
  • Refactor in modest increments. This provides input to developers early in the process, allowing them to identify potential flaws as well as add business needs.
  • Set specific goals. Early in the code reworking process, developers should define the project scope and goals. As refactoring is intended to be a sort of housekeeping rather than an opportunity to change functionality or features, this helps to avoid delays and needless labor.
  • Test frequently. This assists in ensuring that refactored changes do not introduce new bugs.
  • Whenever feasible, automate. Automation tools make refactoring easier and faster, resulting in increased efficiency.
  • Separately address software flaws. Refactoring is not intended to fix software problems. Debugging and troubleshooting should be done independently.
  • Recognize the code. Examine the code to learn about its processes, methods, objects, variables, and other components.
  • Refactor, patch, and update on a regular basis. When refactoring may address a substantial issue without requiring too much time and effort, it generates the highest return on investment.
  • Concentrate on code deduplication. Duplication complicates code, increasing its footprint and squandering system resources.

Concentrate on the process rather than on perfection

The truth is that you will never be completely satisfied with the results of code refactoring. Even so, it’s critical to begin thinking about the process as an ongoing maintenance project. It will necessitate that you clean and organize the code on a regular basis.


Refactoring is a procedure that involves revising the source code of the code. It makes no new features or changes to the underlying system. It’s a practice that helps maintain the code running smoothly and without errors. Another advantage of refactoring is that it allows developers to focus on the details that will drive the solution’s implementation rather than just the code itself.

You can get rid of outdated software applications and improve their overall functionality using proper refactoring techniques without compromising their current state.

OLTP vs OLTP: Their Differences and Comparative Review

OLTP vs OLTP: Their Differences and Comparative Review


OLAP vs. OLTP: The differences?

These terms are frequently used interchangeably, so what are the fundamental distinctions between them and how can you choose the best one for your situation?

We live in a data-driven society, and firms that use data to make better decisions and respond to changing demands are more likely to succeed. This data can be found in innovative service offerings (such as ride-sharing apps) as well as the behemoth systems that run retail (both e-commerce and in-store transactions).

There are two types of data processing systems in the data science field: online analytical processing (OLAP) and online transaction processing (OLTP) (OLTP). The primary distinction is that one employs data to gain meaningful insights, whilst the other is just operational. However, both methods can be used to tackle data problems in meaningful ways.

The challenge isn’t which processing type to utilize, but how to make the best use of both for your situation.

But, what is OLAP?

Online analytical processing (OLAP) is a system that does multidimensional analysis on massive amounts of data at rapid rates. This data is typically derived from a data warehouse, data mart, or other centralized data source. OLAP is great for data mining, business intelligence, and complex analytical calculations. As well as financial analysis, budgeting, and sales forecasting roles in corporate reporting.

The OLAP cube, which allows you to swiftly query, report on, and analyze multidimensional data, is at the heart of most OLAP databases. What exactly is a data dimension? It is simply one component of a larger dataset. For example, sales numbers may contain multiple variables such as geography, time of year, product models, and so on.

The OLAP cube expands the typical relational database schema’s row-by-column arrangement by adding levels for extra data dimensions. While the cube’s top layer may categorize sales by area, data analysts can “drill-down” into layers for sales by state/province, city, and/or specific stores. This historical, aggregated data is typically kept in a star or snowflake format for OLAP.

OLAP cube

OLAP Types

Although any online analytical processing system uses a multidimensional structure, OLAP cubes come in a variety of shapes and sizes. Only the most well-known are mentioned here:


MOLAP is considered a typical form of OLAP and is commonly referred to as OLAP. The data in this OLAP cube example is kept in a multidimensional array rather than a relational database. Before running the system, pre-computation is required.


Unlike traditional OLAP, ROLAP works directly with relational databases and does not require pre-computation. However, in order to be used in ROLAP, the OLAP cube database must be well designed.


HOLAP, as the name implies, is a synthesis of MOLAP and ROLAP. This type allows users to determine how much data will be saved in MOLAP and ROLAP.

OLAP cube pros and cons

OLAP cubes, like any other BI tool or technique, have pros and limitations. Of course, before deploying this technology, it is necessary to ensure that the advantages of OLAP cubes outnumber the disadvantages.


  • High cost: implementing such technology is not cheap or quick, but it is an investment in the future that can pay for itself;
  • The OLAP cube’s main restriction is its computational capabilities. Some systems lack computing power, which severely limits system adaptability.
  • Potential risks: it is not always possible to give large amounts of data, and it is difficult to provide important relationships to decision makers.


  • Multidimensional data representation: this data structure allows users to examine information from several perspectives.
  • High data processing speed: An OLAP cube typically executes a typical user query in 5 seconds, saving users time on computations and building sophisticated heavyweight reports.
  • Data that is detailed and aggregated: a cube is organized with multiple dimensions, making it simple and quick to navigate through large amounts of information.
  • Instead of manipulating database table fields, the end user interacts with common business categories such as products, customers, employees, territory, date, and so on.

As you can see, the advantages of OLAP cubes are not only in their increased number, but also in their increased capability. Every tool has risks, but in the case of OLAP cubes, the risks are worth it.

OLAP and data cube applications

To begin working with OLAP cubes, you must first select the appropriate tool. We recommend that you pay attention to the following items from the market’s wide variety:

  • IBM Cognos
  • Micro Strategy
  • Apache Kylin
  • Essbase OLAP cubes

It is also possible to create an OLAP cube with Hadoop, particularly with the Ranet OLAP analytical tool. You can get OLAP cube software for free and use it for a 30-day trial period. However, implementing an OLAP data cube is not the only problem. When working with OLAP cube data, it is necessary to assemble MDX queries and generate current reports. Given the correlation of relations, MDX queries are indeed extremely difficult to create and test on their own. Furthermore, for successful report preparation, a user must be able to navigate the data in a meaningful way and understand how to compile all relevant information. For this purpose, there is a Cubes Viewer, a browser-based visual tool for analyzing and dealing with data in an OLAP system. Ranet OLAP includes a CubesViewer function that allows users to examine data, design, generate, and embed charts. As an HTML version of Ranet OLAP may be used in any browser, the charts and dynamic analytics can be presented on all websites and applications as a result of the viewer. Because the browser is used, selected views can be saved and shared. CubesViewer’s Ranet OLAP integration allows even non-professional users to view data from numerous dimensions and aggregations, create complex queries, and generate sophisticated reports.

 The viewer makes it easy to exploit raw information, data series, and visualizations. The embedded viewer in our system will not require any additional installation or storage space.

What is OLTP?

OLAP OLTP shceme

Online transactional processing (OLTP) allows huge numbers of individuals to execute enormous numbers of database transactions in real time, generally over the Internet. Many of our everyday transactions, from ATMs to in-store sales to hotel reservations, are powered by OLTP systems. Non-financial transactions, such as password changes and text messages, can also be driven by OLTP.

OLTP systems employ a relational database that can perform the following functions:

  • Process a huge number of relatively basic operations, which are typically data insertions, updates, and removals.
  • Allow several users to access the same data while maintaining data integrity.
  • Allow for extremely fast processing, with reaction times measured in milliseconds.
  • Make indexed data sets available for quick searching, retrieval, and querying.
  • Be available 24 hours a day, seven days a week, with continuous incremental backups.

OLTP system requirements

A three-tier design is the most popular for an OLTP system that uses transactional data. It typically consists of a presentation layer, a business logic tier, and a data store tier. The presentation layer is the front end, when the transaction is initiated by a human or is generated by the system. The logic tier is made up of rules that check the transaction and guarantee that all of the data needed to complete it is available. The transaction and all associated data are stored in the data storage tier.

The following are the primary characteristics of an online transaction processing system:

  • ACID compliance requires OLTP systems to ensure that the complete transaction is appropriately logged. A transaction is often the execution of a program, which may necessitate the execution of numerous steps or actions. It may be considered complete when all parties involved recognize the transaction, when the product/service is delivered, or when a certain amount of updates to specific tables in the database are completed. A transaction is only properly recorded if all of the processes required are completed and recorded. If any of the steps contains an error, the entire transaction must be aborted and all steps must be deleted from the system. To ensure the accuracy of the data in the system, OLTP systems must adhere to atomic, consistent, isolated, and durable (ACID) qualities.


  • Atomic controls ensure that all steps in a transaction are executed successfully as a group. That is, if any of the stages between the transactions fails, all subsequent steps must also fail or be reverted. Commit refers to the successful completion of a transaction. The failure of a transaction is referred to as abort.


  • Consistent: The transaction preserves the database’s internal consistency. If you run the transaction on a previously consistent database, the database will be consistent again when the transaction is finished.


  • Isolated: The transaction runs as if it were the only transaction running. That is, running a series of transactions has the same effect as doing them one at a time. This is known as serializability, and it is often accomplished by locking certain rows in the table.


  • Concurrency: OLTP systems can support massive user populations, with multiple users attempting to access the same data at the same time. The system must ensure that all users attempting to read or write into the system can do so at the same time. Concurrency controls ensure that two users accessing the same data in the database system at the same time cannot change that data, or that one user must wait until the other user has completed processing before changing that data.


  • Scalability: OLTP systems must be able to immediately scale up and down to manage transaction traffic in real time and execute transactions concurrently, regardless of the number of users attempting to access the system.


  • Availability: An OLTP system must be available and ready to take transactions at all times. A transaction loss might result in income loss or have legal ramifications. Because transactions can be conducted from anywhere in the world and at any time, the system must be operational 24 hours a day, seven days a week.


  • High throughput and low response time: OLTP systems demand millisecond or even faster response times to keep enterprise users busy and match customers’ escalating expectations.

The primary distinction between OLAP and OLTP is: Type of processing

OLTP OLAP distinction

The major difference between the two systems can be found in their names: analytical vs. transactional. Each system is designed specifically for that type of processing.

OLAP is designed to perform complicated data analysis for better decision-making. Data scientists, business analysts, and knowledge workers use OLAP systems to support business intelligence (BI), data mining, and other decision support applications.

OLTP, on the other hand, is designed to handle a large number of transactions. Frontline workers (e.g., cashiers, bank tellers, hotel desk clerks) or customer self-service applications use OLTP systems (e.g., online banking, e-commerce, travel reservations).

Other key differences between OLAP and OLTP

  • OLAP systems enable data extraction for complicated analysis. The queries used to make business decisions frequently entail a huge number of records. OLTP systems, on the other hand, are perfect for doing simple database updates, insertions, and deletions. Typically, the inquiries involve only one or a few records.
  • Data source: Because an OLAP database is multidimensional, it can support complex queries of multiple data facts from current and historical data. Different OLTP databases can be used to aggregate data for OLAP and can be organized as a data warehouse. OLTP, on the other hand, makes use of a traditional DBMS to handle a high volume of real-time transactions.
  • Processing time: Response times in OLAP are orders of magnitude slower than in OLTP. Workloads are read-intensive and involve massive data sets. Every millisecond counts in OLTP transactions and responses. Workloads consist of basic read and write operations via SQL (structured query language), which need less time and storage space.
  • Availability: Because OLAP systems do not modify current data, they can be backed up less frequently. However, because of the nature of transactional processing, OLTP systems modify data frequently. They necessitate frequent or concurrent backups to ensure data integrity.

OLAP vs. OLTP: Which is the best option for you?

The best system for your situation is determined by your goals. Do you require a centralized platform for business insights? OLAP can assist you in extracting value from massive amounts of data. Do you need to keep track of daily transactions? OLTP is meant to handle huge numbers of transactions per second quickly.

It should be noted that typical OLAP systems necessitate data-modeling knowledge and frequently necessitate collaboration across different business units. OLTP systems, on the other hand, are mission-critical, with any outage resulting in disrupted transactions, lost revenue, and brand reputation damage.

Organizations frequently employ both OLAP and OLTP systems. In reality, OLAP systems can be used to evaluate data that leads to improvements in business processes in OLTP systems.

Analytical Maturity Model as a Key to Business Growth

Analytical Maturity Model as a Key to Business Growth

Analytics Maturity Model
Analytics Maturity Model

Data Analytics Maturity Model

Analytics maturity is a model that describes how companies, groups, or individuals progressed through various stages of data analysis over time. This model progresses from simple to more difficult types of analysis, with the working assumption that the more complex types of analytics provide more value.

In this article, we will provide an overview of the widely used analytics maturity model’s purpose and discuss how it is frequently misinterpreted. By the end of this article, you will have a more nuanced understanding of how to apply the analytics maturity model within your organization.

What is Analytics Maturity?

On the surface, the analytics maturity curve appears to be simply the progression of types of analysis on which an organization focuses its resources. A single descriptive analysis use case, for example, is not as valuable as a single predictive analytics use case. Knowing what happened is useful, but not as useful as predicting the future. This is how analytics maturity progresses. Each level is directly related to the types of questions we’re attempting to answer.

In an organization, answering the question “What happened yesterday?” is much easier than answering the question “What will happen tomorrow?”

This is a straightforward example of analytics maturity. In general, the more effectively an organization invests in technology, processes, and people, the more complex questions it can answer. The underlying assumption is that the answers to these more complex questions are more valuable to an organization.

The types of questions are labeled with the types of analytics:

Descriptive = What happened?

Diagnostic = Why did it happen?

Predictive = What is likely to happen?

Prescriptive = How can we make something happen?

Analytics Maturity Levels

Analyzing Analytics Maturity Models

You’ve probably seen a version of the chart above if you work in data science, analytics, business intelligence, or even IT. It is so common that it has almost become cliche. Companies like Gartner have made a business out of creating cool little visuals like these, and you can find them all over the internet. It is an excellent way to differentiate between the various types of analysis. It is an excellent chart for visually demonstrating the various types of analysis.
People and businesses, however, misinterpret this visualization. They believe this is a road map. The transition from one type of analysis to another. This should not be the main point. In fact, having that as your final interpretation can be detrimental to your business.
Because of this misunderstanding, enterprises tend to overinvest in Predictive and Prescriptive analytics while underinvesting in Descriptive and Diagnostic analysis. That isn’t to say that Prescriptive analysis isn’t the “holy grail” of business intelligence; it is and will likely remain so; rather, focusing on Prescriptive analysis should not come at the expense of more foundational analysis.

Where Do People Go Wrong When It Comes to Analytics Maturity?

The main reason people misinterpret this chart is that they believe you are progressing from one type of analysis to another, from descriptive to diagnostic to predictive to prescriptive. Actually, this is not the case. You are not switching from one to the other; rather, you are expanding your organization’s analysis types.

As an example, consider a basic sales use case.

Descriptive: How much did we sell in July 2021?

Diagnostic: Why were our sales higher or lower in July 2021 compared to July 2020?

Predictive: What are our sales projections for July 2022?

Prescriptive: What should we do to ensure that sales in July 2022 exceed those in July 2021?

You should begin with descriptive questions and work your way up from there. Undoubtedly, the prescriptive question is a much more valuable one to answer, but the descriptive question is necessary to get there.

Analysis: Some businesses overinvest in Predictive/Prescriptive analytics (and the associated resources and tools) at the expense of Descriptive/Diagnostic analytics (and the corresponding resources and tools). Avoid falling into this trap. Descriptive use cases will always exist and serve as the foundation for more valuable analyses such as diagnostic, predictive, and prescriptive analytics.

What is It if Not Progression?

How can we characterize it if it isn’t a progression? It’s all about laying the groundwork.

Diagnostic analytics is built on descriptive analytics. Predictive analytics, for example, is built on diagnostic data. A firm foundation, just like a house, is required to support all of the other elements.
Three things should be kept in mind:


  • Just because a single descriptive use case is (usually) less valuable than a single prescriptive use case does not mean it isn’t beneficial overall.
  • There will always be more descriptive use cases than prescriptive use cases. As you proceed along the curve, the volume at each step decreases.
  • Prerequisite use cases at the other levels are also required for prescriptive use cases: predictive, diagnostic, and descriptive. Your company will never cease using these.

Ground Level of Analytics

It may be difficult to imagine, but there are still firms that do not use technology and conduct their operations with pen and paper. Even at this fundamental level, though, data is collected and controlled – at least for financial purposes.
There is no analytical strategy or organization at this point. Data is gathered to provide a better knowledge of reality, and in most situations, the only reports available are those that show financial performance. In many cases, no technology is used in data analysis. Reports are created in response to management’s ad hoc requests. However, most decisions are made based on intuition, experience, politics, market trends, or tradition.

The major issue here is a lack of vision and comprehension of the benefits of analytics. In many circumstances, there is also a lack of desire to invest time and resources in building analytical talents, owing to a lack of expertise. Changing management’s perspective and attitude would be an excellent place to start on the path to analytics maturity.

Descriptive Analytics

Most firms now employ software to collect historical and statistical data and display it in a more intelligible way; decision-makers then attempt to analyze this data on their own.

Keep Creator Personas in Mind

The architecture of your firm must accommodate multiple groups, each of which may have distinct personas.

The type of analytics that an individual (i.e. persona) focuses on is determined by their function and department within an organization. Invest in tools that will help you support both of these personas.

Line-of- Business Analysts will typically concentrate on Descriptive and Diagnostic use cases. They will typically work on a greater number of use cases (i.e. questions to answer). Despite the fact that each individual answer may not be as beneficial to the organization as a whole, the entire sum of the value supplied is quite significant owing to the volume.
Citizen Data Scientists and Analytics Engineers are typically supportive of prescriptive and predictive use cases. They often have a lower volume of use cases and answer a smaller set of questions, but the value of each of these responses can be higher.

Because each of these personas has a different focus, it is critical to choose the correct tool for that persona inside your organization.
A line-of-business analyst, for example, is probably best suited to Power BI and Tableau. Whereas Alteryx or Dataiku may be better suitable for a citizen data scientist or analytics engineers.

Let’s Take a Look at a Real-life application:

Data for Forecasting in a Variety of Areas

Machine learning and big data offer a wide range of analytical possibilities. ML algorithms are now used for marketing purposes, customer churn prediction for subscription-based businesses, product development and predictive maintenance in manufacturing, fraud detection in financial institutions, occupancy and demand prediction in travel and hospitality, forecasting disease spikes in healthcare, and many other applications. They’re even utilized in professional sports to forecast who will win the championship or who will be the next season’s superstar.

Automated Decisions Streamlining Operations

Apart from the obvious and well-known applications in marketing for targeted advertising, improved loyalty programs, highly personalized recommendations, and overall marketing strategy, the advantages of prescriptive analytics are widely applied in other industries. Automated decision support assists in the financial industry with credit risk management, the oil and gas industry with identifying best drilling locations and optimizing equipment usage, warehousing with inventory level management, logistics with route planning, travel with dynamic pricing, healthcare with hospital management, and so on.

Prescriptive analytics technologies can now address global social issues such as climate change, disease prevention, and wildlife conservation.

When you think of examples of prescriptive analytics, you might think of companies like Amazon and Netflix, which have customer-facing analytics and sophisticated recommendation engines. Other examples of how advanced technologies and decision automation may help firms include Ernsting’s family managing price, an Australian brewery organizing distribution, and Globus CR optimizing marketing strategy.

Important Challenges

The biggest obstacles a firm has at this level are not related to additional development, but rather to maintaining and optimizing their analytics infrastructure. According to studies, the primary issues with big data include data privacy, a lack of knowledge and professionals, data security, and so on. As a result, organizations should prioritize increasing their skills in data science and engineering, preserving customers private data, and maintaining the protection of their intellectual property at this time.


Don’t fall into the frequent pitfall of focusing solely on the gleaming predictive and prescriptive use cases. They are extremely valuable and should be invested in, but not at the expense of resources that support more fundamental analysis. Keep in mind that these “lower stages” of analysis are requirements for more complex projects.