What is data classification?

Data classification is nothing else than the process of classifying data, meaning organising it in categories according to certain criteria, for example data’s level of sensitivity.

Such categorisation helps organisations manage and secure data more effectively.

Why is the data classification process important for your security posture?

Data classification process is of paramount importance for every organisation’s security posture as it allows organisations to identify and prioritise their most sensitive data and critical information, and implement effective access controls.

It also ensures compliance with security regulations, as many regulatory governing bodies require data to be labelled. In case of a data breach, data classification facilitates a more targeted and efficient response, needed to mitigate the impact of such an incident.

What are the types of data classification?

Data classification involves categorising information based on its attributes, sensitivity, and importance. There are many different types of classification that can be used by an organisation, depending on its specific needs and goals.

There are however three main types of data classification that can be used by businesses of all sizes and industries:

• content-based classification,
• context-based classification, and
• user-based classification.

Content-based classification allows to organise data depending on the content of the document, contact-based classification looks at the ways data is being used and at who is accessing it, while user-based classification relies on user-knowledge selection of the document.

Other common types of data classification include confidentially-based classification (which looks at whether data is public, confidential or restricted), regulatory-based classification (looking at whether it is PII – personally identifiable information, PHI – protected health information, or financial data), data type-based classification and lifecycle-based classification.

To respond to their specific needs and environment, organisations often use a combination of those classification types, creating a system that works for them and aligns with their business goals.

What are the levels of data classification?

The process of data classification involves assigning a different data classification level or tier to data, based on its sensitivity, importance, and confidentiality. Those levels include:

• public data, available locally or on the Internet, often shared, updated and passed around;
• internal data, intended to be used within a certain organisation and not by the public or external parties;
• confidential data, meaning sensitive information that requires protection;
• restricted data, meaning highly sensitive information, with access limited to a specific group of people.

What are examples of data classification?

The trickiest part of data classification is the moment of assigning data category. Here are some data classification examples that may help you in the process:

• documents considered public data include press releases, marketing materials, content of a website, addresses, phone numbers;
• documents considered internal include departmental reports, company newsletters, employee directories;
• documents considered confidential include financial reports, business plans, researches, internal policies, social security numbers, medical reports, customer data;
• documents considered restricted data include trade secrets, intellectual property, highly sensitive financial information, reports prepared by the government.

The intersection of classification of data and privacy regulations

Data classification plays a crucial role in meeting data protection requirements. By integrating data classification practices into their overall data management strategy organisations can align with privacy regulations, enhance their data protection practices, and demonstrate a commitment to safeguarding sensitive and personal information.

What’s more, data classification helps in achieving and maintaining compliance with evolving privacy requirements in various jurisdictions.

Read more about data security:

• Data security and privacy
• Big Data security: challenges

Best practices in data classification for enhanced security

To help you kick start with your data classification processes or improve your existing ones, here is a quick checklist of best practices in data classification for enhanced security:

1. Understand data protection regulations relevant to your industry and geographic location;
2. Establish a clear and understandable data classification policy;
3. Involve stakeholders from different departments to ensure a comprehensive and well-rounded approach;
4. Automate your data classification processes to reduce manual efforts and mistakes;
5. Educate and train your employees about the importance of data classification and how to stick to it in everyday life;
6. Be consistent in your labelling system;
7. Regularly review and update your data classification methods to ensure they remain aligned with your evolving business needs.

Transform your data strategy with Future Processing

Data classification is a complex, long-term process that lies at the very heart of an effective data strategy of each company. But you don’t need to face it on your own. It is much better to use expertise and experience of professionals who are doing it on daily basis.

At Future Processing we have all it takes to allow you to make the most of your information assets and apply innovative data solutions to your business. Get in touch with us today to discuss your options and find the best solutions that will take your organisation to the next level.

Data Science and Engineering

Process data, base business decisions on knowledge and improve your day-to-day operations.

Let’s work together

AI-augmented software development refers to the integration of artificial intelligence (AI) technologies and techniques into the software development process to enhance productivity, efficiency, and the quality of software applications.

This approach leverages AI and machine learning to automate tasks, make data-driven decisions, and provide intelligent insights throughout the software development lifecycle.

The game changer: how AI is redefining software development?

Because of the many aspects it encompasses, AI is a proper game changer when it comes to software development. It is transforming the development processes by increasing efficiency, improving code quality, enhancing collaboration, and enabling developers to focus on higher-level tasks.

This evolution is leading to faster development cycles, reduced costs, and the creation of more reliable and innovative software solutions, ultimately redefining how software is conceived, built, and maintained.

AI for programmers: will AI replace software developers?

AI has the potential to automate many aspects of software development, making the process more efficient and assisting software developers in various tasks.

However, it is unlikely AI will completely replace software developers in the foreseeable future – it is more likely it will augment and assist developers in their work.

Here are some reasons why:

1. Creativity and problem solving – software development often involves creative problem-solving, designing innovative solutions, and making decisions that require human intuition and expertise. While AI can assist in automating routine tasks, it lacks the creativity and critical thinking abilities of humans.
2. Complex decision-making – many software development decisions are complex and require a deep understanding of business requirements, user needs, and technical constraints. AI can provide data-driven insights, but human judgment is crucial in making decisions that consider the broader context.
3. Adaptability and learning – AI systems are limited to the knowledge and training data they have received. Software development is a dynamic field, and developers need to continually learn and adapt to new technologies, tools, and methodologies. Humans excel in their ability to learn and stay up-to-date.
4. Understanding user needs – understanding and empathising with user needs, preferences, and experiences is a human skill that is difficult for AI to replicate. Developers play a crucial role in creating user-centric software.
5. Communication and collaboration – effective communication and collaboration are essential in software development, especially when working in teams or with clients. Humans excel in interpersonal skills and teamwork, which are challenging for AI to replicate.
6. Ethical and moral considerations – making ethical and moral decisions in software development, such as addressing bias in algorithms or considering the impact of technology on society, requires human judgment and values.
7. Innovation and research – software development often involves pushing the boundaries of technology and conducting research. AI can assist in research tasks, but breakthrough innovations typically come from human creativity and curiosity.
8. User Experience and design – while AI can analyse data and provide insights, the design of user interfaces and user experiences requires human creativity and an understanding of aesthetics and usability.
9. Debugging and problem resolution – debugging complex software issues and resolving unexpected problems often require detective work and problem-solving skills that AI currently lacks.
10. Regulatory and legal compliance – ensuring that software complies with legal and regulatory requirements, such as data privacy laws, requires human expertise in understanding complex legal frameworks.

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AI in software development: opportunities

AI presents numerous opportunities in software development, revolutionising the way software is designed, built, and maintained.

Here are some key opportunities and areas where AI is making a significant impact in software development:

Automated code generation

AI-powered code generators can automate the creation of code snippets, modules, and even entire applications, significantly speeding up development.

Predictive analysis

AI can analyse historical project data to predict project timelines, resource requirements, and potential risks, aiding in project management and planning.

Enhanced User Experience

AI-driven chatbots can provide developer support, answer questions, and offer guidance on coding issues and best practices, making the User Experience much better.

Software testing automation

AI can automate various types of testing, including unit testing, integration testing, and user acceptance testing, leading to more comprehensive testing coverage.

Identifying security threats and vulnerabilities

AI algorithms can automatically detect and categorise bugs and issues in code, making it easier for developers to identify and fix problems.

AI-based security solutions proactively identify and mitigate security vulnerabilities in software applications, improving overall security.

Language translation and localisation

AI-powered translation and localisation tools help make software accessible to global audiences by automating language translation and cultural adaptation.

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AI in software development: challenges

While AI offers numerous benefits in software development, it also presents several challenges that need to be addressed for successful implementation.

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Here are some key challenges associated with AI in software development:

Training custom AI models is expensive

Training custom AI models can be expensive, and this cost is one of the significant challenges in AI-driven software development.

Several factors contribute to the expense of training custom AI models, such as data collection and preparation, computing resources, expertise, training time, hyper parameter tuning and infrastructure maintenance.

The potential benefits however, such as improved performance, competitive advantages, and innovation, may justify the investment. Careful planning, resource allocation, and cost management are essential to navigate the challenges associated with custom AI model training effectively.

Data privacy concerns

Handling sensitive data raises privacy and security concerns.

Developers must implement robust security measures to protect data used for AI training and inference, while organisations should carefully plan AI projects, invest in education and training, and establish clear ethical and governance frameworks to ensure responsible and successful AI integration into software development processes.

Job displacement fears

The fear of job displacement due to AI in software development is a valid concern and a significant challenge.

Addressing those fears requires a combination of individual and organisational efforts: individuals should proactively seek opportunities for upskilling and reskilling, companies should invest in employee raising and development programmes, while government and education institutions can play a role in providing resources and programs to help individuals acquire AI-related skills.

Mastering the AI-driven software development: turn challenges into opportunities

Mastering AI-driven software development involves recognising the challenges and transforming them into opportunities for innovation and growth.

By addressing all of those challenges proactively and viewing them as opportunities for improvement and innovation, organisations and developers can master AI-driven software development and leverage AI to create more efficient, ethical, and innovative software solutions.

If you are looking for reliable partners to discuss those issues, do get in touch. We will be happy to share our experience and expertise.

Data Science and Engineering

Process data, base business decisions on knowledge and improve your day-to-day operations.

Let’s work together

That’s an IBM definition. Machine Learning is a complex concept, so before deciding to leverage any ML solutions, it’s wise to take a step back and ask yourself a few questions. Here they are – for your ease we divided them into the basics of ML strategy and the questions that help address the challenge.

The basics of ML strategy for businesses and organisations

• Do you have any data to work with? If so, is your data structured or unstructured?
  You need to be fully aware of the kind of data that you have in order to know how it should be applied. Structured data is clearly defined, stored in tabular forms and easily searchable (like customer phone numbers or transactional information). Unstructured data is not organised, and is stored in its native format (like audio files or images).

• Do you understand your data and the processes you want to optimise?
  You should be able to separate right from wrong and know which pieces of data are actually relevant to the processes that require optimisation – processes that should also be known inside-out.

• Does your data storage architecture provide seamless data usage?
  You should have convenient yet secure access to data, permitting easy collaboration between data engineers.

• Have you already reviewed your current reporting system?
  Maybe all that you need to begin with is simple analytics instead of complex machine learning applications.

Addressing problems before developing a Machine Learning strategy

• Can you identify a business issue that could be solved with the use of enterprise machine learning?
  It’s essential to know exactly what you need ML for and the effects that you expect. This will be helpful later on, when the time comes to measure the results of your investment and make any adjustments to your strategy.

• Do you have sufficient data, or maybe you need some external data sources?
  Very often, companies need to reach for public government data or use social media analytics tools to gather more of it. This isn’t exactly rocket science, but it adds another layer to your business and technological process.

• Are you at a sufficient level of expertise to solve this task? Do you have enough collected data?
  Maybe you will need to collaborate with an external Data Solutions partner or outsource data analysis to evoke the full potential of Machine Learning. Plus, a tandem of data and machine learning engineers are often needed in order to implement ML solutions correctly, so you need to take this into consideration as well.

• Should this be a one-off like a discovery experiment, or a solution that will be repeated?
  If it’s the latter case, you will need to think about how to maintain the solution, which can sometimes be trickier and more time and resource-consuming than the Machine Learning algorithm itself.

• Are you able to build infrastructure for the solution?
  This is also an HR-related question. It’s very likely that your IT team is not capable of creating an efficient infrastructure on their own, especially if they have little to no experience with ML.

• Can you take the risk of failure?
  The initial phase of the ML solution is always an experiment. It requires multiple attempts at parameter tuning or making several changes to the original model. Due to the very specific nature of the ML solution development process, one would need to be conscious of the fact that there are situations in which we may not be able to receive a complete answer to our original question, though this doesn’t mean that we can’t still benefit from the insights that we’ll have gathered along the way.

What if you don’t have all the answers?

You might not be able to answer all of the above-mentioned questions. For example, you may not fully comprehend the nature of your processes, lack sufficient data, or not have all of the resources needed to implement a desired ML solution.

But should this hold you back from investing in this kind of technology? Not necessarily.

Nowadays, there are many ways to bridge certain gaps:

• If you need to enrich your data, because the pieces that are at your disposal are insufficient, you can turn to external sources. Companies like Google or Facebook offer access to the data they are constantly gathering, and you can either use this as a complementary source or build your own solution on top of their data with the help of an Software Development Company. Of course, this would only apply to a certain group of specific problems.

• If your objective for using ML is not well-defined, think about workshop with technical and business experts to understand your data better and identify possible ways to utilise ML in your organisation.

• If you don’t have the in-house resources needed to build a solution, there are outsourcing companies with experience, providing either small parts to a solution or entire solutions on their own.

Of course, you may also come to the conclusion that machine learning is not something that you need to implement at this very moment.

A thorough evaluation of your situation is a must, in order to avoid putting all your resources into something that is not going to bring any additional benefits to your business.

The business challenges that ML and deep learning models can address

A key when it comes to the final decision of whether using machine learning strategy lays in understanding the business challenges it can address. Let us give you just some of the very many examples.

Machine learning techniques facilitates predictive analytics. It can analyse historical sales data and external factors to predict future sales, helping businesses optimise inventory and staffing. It can also predict which customers are likely to leave, allowing companies to take proactive retention measures.

An interesting use case of machine leaning is also recommendation systems, and we all know personalised product recommendations can improve cross-selling and upselling in e-commerce.

Natural Language Processing tools are used in chatbots and virtual assistants, as well as in sentiment analysis and language translations, allowing your clients to use your online solutions no matter their location or the language they use.

It is also a great help when it comes to fraud detection – it can be used to identify fraudulent transactions in real-time, reducing financial losses.

In finance, machine learning used in algorithmic trading and credit scoring allows for making data-driven investment decisions and assessing creditworthiness of loan applicants.

Read more about other applications of Machine Learning algorithms:

1. Machine Learning in logistics
2. How is Natural Language Processing (NLP) used in business?
3. NLP techniques: key methods that will improve your analysis
4. How to implement predictive maintenance

It’s just the tips of the iceberg – there are so many uses of data driven models in modern business, we probably wouldn’t manage to list them all. But what we’ve listed has probably given you a good picture of business benefits of machine learning techiques.

Two ways of getting your ML solution

When you decide to start your ML journey, there are two ways you can go: you can either buy a ready tool or develop one. Both options carry some risk, both mean costs.  

The cheapest way to go about introducing ML to your business is to buy a ready solution.

Even better is to find one on GitHub – there is an abundance of them there! But when using something that already exists, you need to take into consideration that it may not always do exactly what you are after, and it may not be the best option for your organisation. 

The second way is to create the ML solution from scratch.

Such an approach means your solution will be tailored to your needs, it will respond to the problems you have, and it will be specific to your organisation. A definite downside here is cost.  

Good news is there is a way in the middle, a combination of the two already described. You can find and buy a half-finished product and you can develop it so that it responds to your needs and works exactly as you want it to work. It’s a solution that is being used more and more often – a very pragmatic and cost-effective one.  

What do you need to succeed with your ML strategy? 5 steps guide

Now that we’ve established the ways of getting your ML solution, let’s look at what you really need to succeed with your ML strategy. Here is our list:

Step 1. Define clear objectives and goals

It may sound obvious, but to start with, you need to know what problem you want to solve, and you need to be sure solving it will give you a proper value. What’s more, you need to check that the problem is solvable using ML. 

To do that, define clear, measurable objectives, such as reducing costs, increasing revenue or improving customer satisfaction. Remember to start with a few well-defined projects rather than trying to tackle everything at one.

Step 2. Data collection and preparation

Data lies at the heart of all organisations and at the centre of all IT projects they undertake. What counts is its quality and quantity, but also the relevance to the problem you want to solve using ML.

Having access to high volumes of data does not necessarily mean it will be valuable for you: weather related data alone is not good enough for problems related to industrial processes, even if the weather influences them directly.

Data collection should start even before the beginning of your ML project: given the always increasing data volumes, it’s worth thinking about your data strategy as soon as possible and assess your current data landscape before you set off on any ML journey. 

Step 3. Explore the options to work with the best talents

If you are outsourcing your ML engineers, they will definitely need support from within your organisation: they will need to speak to domain experts who understand the problem they are working on, who are able to explain it properly, and who will be able to evaluate the results provided. 

Remember that collaborating with skilled individuals is critical to the success of your ML initiatives. Building a strong ML team and fostering the culture of innovation is of paramount importance.

Step 4. Implementation and integration of Machine Learning model

Choose appropriate ML algorithms and deep learning architectures based on the nature of your data and the specific use case. Consider factors such as classification, regression, clustering, or recommendation. Train your models using a representative dataset. Employ techniques like cross-validation to assess model performance and fine-tune hyperparameters.

Step 5. Evaluation and continuous improvement

Evaluation and continuous improvement of your ML initiatives is absolutely crucial if you want your ML project to remain effective, efficient and aligned with your business goals.

To do that, define Key Performance Indicators, implement a regular evaluation process, get feedback and encourage collaboration and communication among team members, including data scientists, engineers, domain experts and stakeholders.

Also, remember to use iterative development approach – continuously iterate on your ML projects, incorporating feedback and making improvement in each iteration.

Practical use of Machine Learning algorithms

The opportunities offered by Machine Learning were first used by Internet giants processing data from social media and online retail. Soon they were followed by hardware companies, who used it to optimise their chips, memory, and storage. 

The recent pandemic forced many businesses around the world to set off on their digital transformation journey or to accelerate what they were already doing. The result is that today many services are delivered almost entirely online via automated or semi-automated processes.

What does it all mean for you? It means it’s time to embrace ML and use it to your advantage!

No matter whether you are keen to add some ML solutions or are ready for a full digital transformation, it is definitely worth investing your time in checking available solutions and their importance for your business processes.

Looking for the right partners for your ML project?

No matter what stage of your ML strategy you are at, Future Processing can help. Let’s look at what we can do for you: 

• If you are considering a problem to be solved, consult it with our ML specialists: check your options and whether the problem you are thinking about is solvable using Machine Learning model.

• If you already have some data, you can show it to us and check the proof of concept. If you don’t have your data as yet, we can help you organise the data acquisition process (data engineering, cloud, data pipelines – these are all our areas of expertise). A good solution is to speak to pragmatic digital transformation consultants such as embracent, ready to deliver the best results for you and provide solutions to the problems you have.  

• If you are ready to start – here we come! We have a great team of IT professionals with a vast experience in ML projects. The scope of our cooperation will depend on you: we can either develop the ML model, obtain MVP and / or further develop the solution, or we can provide you with a complete solution including programming, testing, delivery, and maintenance of the application.