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People generate massive amounts of data daily, and each year the amount generated grows faster. It’s estimated that, by 2025, the total amount of data created, captured, copied, and consumed annually will be more than 180 zettabytes.

Big Data Analytics

Big Data innovations are profoundly influencing both technology and business. Already, its applications span a wide range of sectors, from healthcare and pharmaceuticals to finance, manufacturing, and the supply chain.

Big Data in Real Estate

We are innovators with industry-leading expertise in data & AI technology.

Unlock the Value in Your Data

Our State-of-the-Art data solutions are revolutionising the way companies do business.

Success Stories 

Popular sentiment-based systems are ineffective in trading scenarios. The client needed an innovative way to increase its trading profitability by utilising news data.

Creating a Bespoke News Analysis System for a Quantitative Hedge Fund

One of the world's biggest pharma companies reached out for help to improve its drug manufacturing process.

Manufacturing Process Optimisation in Pharmaceutical Industry

Our client was looking for data experts who would be able to bring medical device data to Google Cloud.

Bringing Medical Device Data to Google Cloud

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Sentium has revolutionised how we collect and evaluate data. They have the perfect balance of expertise, professionalism, and price point. 

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I have been working with Sentium for a while now, and their approach is great. They offer seamless data analytics and visualisation without all the fuss. Their software is easy to use, making it painless to find what you need in minutes. 

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Data-driven businesses are crushing the competition. AI is revolutionising entire industries. We provide the tools your business needs to stay ahead of the curve. Do not get left behind!

Why Sentium?

We are data technology experts. We build state-of-the-art data solutions that combine Business Intelligence, Artificial Intelligence, and Data Integration into a bespoke package that perfectly fits your business. 

We prioritise delivering actionable insights that generate immediate value, enabling companies to maximise their data's potential without requiring a dedicated data team. 

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We work with the world’s largest and most advanced pharmaceutical companies.

Pharma


The rapid pace of technological advancement is reshaping the global labour market. With the **World Economic Forum**'s latest [“Future of Jobs Report 2023”](https://www.weforum.org/reports/the-future-of-jobs-report-2023/in-full "The Future of Jobs Report 2023") offering a deep dive into the topic, there are several pivotal findings to consider.

In the era of accelerated high-tech evolution, few things have garnered as much attention and speculation as the future of jobs. The ever-expanding realm of digital innovation has set the stage for monumental shifts in the international employment landscape. Companies are increasingly intertwining their operations with cutting-edge technology, whether it is through [AI transformation](https://sentium-consulting.com/ "AI Transformation"), big data, cloud computing, or business automation. 

But with this comes a pressing question: What does it mean for the worldwide workforce and, more importantly, for you?

### **Embracing the Digital Era**

The World Economic Forum, always at the forefront of universal trends, has rolled out its “Future of Jobs Report 2023”, providing a comprehensive look into the next chapter of employment over the next five years. From the revolutionary impact of AI features to the pivotal role of [automation](https://sentium-consulting.com/services/data-integration "Data Integration") in shaping the employment market, this release is an invaluable resource for anyone aiming to stay ahead in the modern professional world.

So, if you have ever wondered how emerging technologies might shape your professional future, or if you are a business leader pondering over the next strategic move, dive into our distilled insights from the WEF's research. It promises not just a glimpse into the coming years but a roadmap to navigate the intertwined realms of technology and employment.

### **The catalysts of change: AI and green transition**

The report reveals several divergent global job market conclusions. High-income countries experience tight labour fields, while unemployment is on the rise in low to lower-middle-income countries, especially post-COVID-19. Women and those with basic education are particularly affected.

The most important **factors driving business innovation** are **technology adoption, green investments, and the incorporation of Environmental, Social, and Governance (ESG) standards**.

Over 85% of the surveyed organisations recognise technology integration, especially digital transformation, as the primary force behind business evolution. New tech trends such as [big data](https://sentium-consulting.com/blog/introduction-to-big-data-analytics-how-it-works-and-why-it-matters "Data Analytics"), cloud computing, and AI feature prominently in organisational strategies for the next [5 years](https://www.weforum.org/reports/the-future-of-jobs-report-2023/in-full/2-drivers-of-labour-market-transformation "Drivers of Labour Market Transformation"). In fact, these tech solutions are at the forefront of anticipated job creation.

Investments driving the green transition stand out as potent job creators. Localising supply chains also promotes job growth, suggesting a move towards more sustainable and community-focused corporate models.

### **The dual impact of tech advancement on the labour market**

While modern tech is heralded as a beacon of job growth, there are concerns.

Over half of the organisations foresee job creation due to the digital revolution, but a fifth predicts job displacement, highlighting the need for adaptability. This underscores the pressing necessity for flexibility and resilience in our ever-evolving computerised age. 

This dual phenomenon calls for a proactive approach to continuous learning and skill acquisition, ensuring that the workforce remains relevant and robust in the face of technological shifts.

**[Big analytics](https://sentium-consulting.com/services/data-analytics "Data Analytics") and AI technology** are not mere buzzwords; over 75% of businesses plan on adopting these in the coming years. **Digital platforms and apps** are set to be the most wanted technologies, with a whopping 86% of companies planning to integrate them by 2027.

Despite the hype around automation, enterprises are embracing it at a slower pace than anticipated. By 2027, only 42% of business tasks are expected to be automated. Interestingly, while tasks requiring reasoning are becoming more automatable, humans still have the edge in communication and coordination.

### **What is next for the global [job market](https://www.weforum.org/reports/the-future-of-jobs-report-2023/in-full/3-jobs-outlook "Jobs Outlook")?**

There is a significant labour force evolution projected in the coming five years based on WEF’s report. Approximately 23% of roles are anticipated to transform by the year 2027. This entails the creation of 69 million new positions while, concurrently, 83 million old ones will be phased out.

Roles like **AI and Machine Learning [Specialists](https://sentium-consulting.com/careers#current-openings "Current Openings") are rapidly growing**. In terms of specialties, the domains witnessing the fastest expansion include Artificial Intelligence and Machine Learning Specialists, Sustainability Experts, Business Intelligence Analysts, and Information Security Professionals. On a broader scale, sectors such as education, agriculture, and digital commerce are predicted to experience the most substantial absolute growth.

On the other hand, digitalisation spells a decline in jobs in clerical domains like Bank Tellers, Postal Service Clerks, and Data Entry Clerks.

It is necessary to note and observe potential challenges: a sluggish economic trajectory, persistent supply shortages, and looming inflation are among the most significant threats to career stability.

**Analytical and creative thinking, articulate communication, and intelligent problem-solving** - attributes where humans significantly outshine AI - top the chart of [essential skills](https://www.weforum.org/reports/the-future-of-jobs-report-2023/in-full/4-skills-outlook "Skills Outlook") in 2023. There's an increasing emphasis on cognitive skills, technology literacy, and self-efficacy skills. Recognising the power of AI, businesses are placing strategic importance on skills such as robotics, machine learning, and data analysis and processing.

Companies anticipate that 44% of worker skills will face disruption in the next half-decade. **Training initiatives, especially in analytical thinking, AI, and big data, are deemed essential**.

Organisations are confident in training their existing workforce but are sceptical about the availability of external talent.\*\* Improving expertise progression, offering competitive wages, and underlining in-house training\*\* are top strategies to bridge this gap.

**Companies are prioritising Diversity, Equity, and Inclusion (DEI) programs** focusing on women (79%), youth under 25 (68%), and those with disabilities (51%), pointing out a move towards a more inclusive corporate culture.

**There is a strong demand for government intervention**. 45% of enterprises think that skill training funded by the government is crucial for linking talent to job opportunities, highlighting the connection between government policies and corporate efforts.

As the world stands at the cusp of significant business automation and technology adoption, it is imperative to recognise and harness the positive potential of this digital transformation. We remain hopeful and enthusiastic about the possibilities that lie ahead. Whether it's the power of big data, the promise of AI technology, or the broader [applications](https://sentium-consulting.com "Our Services") of cloud computing, the onus is on businesses and individuals to adapt, upskill, and evolve.



Artificial Intelligence has started to transform the way things are done in the real estate industry. It began slowly, but the need to use AI-powered platforms became inevitable in the last two years.

Many developers, [real estate](https://builtin.com/artificial-intelligence/ai-real-estate "Real Estate") companies, property investors and consultants, and house builders across the world adapted to the data-focused trends and embraced the latest [technologies](https://sentium-consulting.com/ "Sentium Consulting"), such as Artificial Intelligence (AI) and Machine Learning (ML), to improve their position on the market. 

### Turning missed opportunities to profitability

Developers and investors are always focused on understanding where to buy property and when to generate development, so they need to be able to use data to get fast and accurate insights to inform their decisions. Real estate data can be diverse and chaotic, which makes it even harder for analysts to evaluate dozens of millions of records or statistics to identify patterns and have a clear picture. Keeping track of so much data is not only challenging, but it translates through missed opportunities and profits. 

Advanced technologies can change the way data is interpreted and managed. Machine learning algorithms make it possible not just to gather and analyse data but connect various sources of information and create patterns and forecasts. These results are extremely valuable in conceiving investment strategies and maintaining a competitive edge.

Many industry players and decision-makers are already discussing the benefits of AI-powered applications on their organisations and the new world of possibilities they have brought along. From analytics that supports decision-making and investments to [automation](https://sentium-consulting.com/services/data-analytics "Data Analytics"), AI has the potential to significantly improve the [real estate](https://www.forbes.com/sites/nataliakarayaneva/2023/02/23/top-5-tech-trends-transforming-real-estate-in-2023/?sh=5913665c66ea "Tech Trends") market, and the Sentium team is ready to offer full support to its clients.

*“Our Artificial Intelligence systems are able to forecast real estate price movements with 80% higher accuracy than the leading products from Amazon and Google”*, stated Henry Smith, the CEO & Founder of [Sentium](https://sentium-consulting.com/about "Sentium Consulting") Consulting.

### 8 ways AI can impact the Real Estate market

We have identified eight ways in which AI can be applied to improve different aspects of the real estate industry:

* [Property Search and Recommendation:](https://www.inman.com/2023/07/01/how-ai-is-transforming-the-luxury-real-estate-business-right-now/ "AI in Real Estate") AI can analyse vast amounts of data, such as estate listings, demographics, and market trends, to provide more accurate and personalised property recommendations to investors and developers. AI-powered algorithms can consider individual preferences, budget, location, and other factors to match potential buyers with properties that meet their specific criteria.
* **Pricing and Valuation:** AI algorithms can interpret historical and current property data, such as recent sales prices, estate features, and market trends, to provide accurate valuations and pricing suggestions. They can help sellers determine the optimal listing price and assist buyers in assessing the fair value of a property.
* **[Predictive Analytics:](https://sentium-consulting.com/services/ai-forecasting "AI Forecasting")** by evaluating historical data, AI can identify patterns and trends in the real estate market, enabling more accurate predictions of future market conditions. This information is extremely valuable because it can help investors, developers, and real estate professionals make informed decisions regarding investments, property development, and market timing.
* **Virtual Reality (VR) and Augmented Reality (AR):** AI-powered VR and AR technologies can create immersive experiences for buyers and renters. Prospective clients can enjoy virtual tours of properties, view 3D models, and visualise potential renovations or interior designs. These technologies take property viewing to the next level, enhancing experiences, saving considerable amounts of time, and allowing clients to explore properties remotely from the comfort of their office or home.
* **Risk Assessment and Fraud Detection:** AI algorithms can process and analyse large volumes of data to assess risk factors associated with real estate investments, such as property location, economic indicators, and market stability. AI can also help detect fraudulent activities, such as identity theft, money laundering, and real estate scams, by flagging suspicious transactions and patterns.
* **Property Management:** AI can streamline [property management](https://www.inman.com/2023/07/01/how-ai-is-transforming-the-luxury-real-estate-business-right-now/ "AI in Real Estate") tasks by automating processes like rent collection, maintenance scheduling, and tenant communication. Smart home technologies powered by AI can also monitor energy consumption, security, and maintenance needs, optimising efficiency and reducing costs for property owners.
* **Market Research and Lead Generation:** AI algorithms can gather and evaluate market data, including social media sentiments, online search trends, and economic indicators, to generate insights on market demand, identify potential leads, and target marketing campaigns more effectively.
* **Legal and Contract Analysis:** AI-powered tools can assist in reviewing and interpreting legal documents and contracts, a deeply valued activity. Thus, it helps real estate professionals and lawyers to save time, reduce errors, and identify potential risks or discrepancies.

It is important to mention that operating models vary for each [company](https://sentium-consulting.com/#industries "Our Industries"), and every project comes with specific requirements that call for a careful evaluation. However, the impact of the new technologies on business operations is undeniable, and implementing these solutions sooner rather than later can save dozens to millions of pounds per year, depending on the organisation size.

If you have questions and want to know what advanced solutions we can provide to support your business operations with immediate results, [get in touch](https://sentium-consulting.com/contact "Contact Us") and we will answer them.



Big Data is undoubtedly one of the latest innovations making a significant impact on technology and business.  Large-scale Data Analytics found potential applications in many areas, including healthcare, pharmaceuticals, finance, supply chain, etc. Although [Big Data](https://sentium-consulting.com/services/data-integration "Data Integration") is very relevant in many subject areas, it has a significant impact on the business field of finance. 

Business firms need to hire professionals with finance backgrounds, especially accountancy, to make [innovations](https://sentium-consulting.com "Our Solutions") in this area. The professionals also should possess skills in computers and information technology (IT) to cater to modern business dynamics. Those finance professionals and accountants who have the relevant domain knowledge and relevant IT skills can take full advantage of that, integrating Big Data into decision-making processes.

Billions of financial transactions occur every day globally. The finance field cannot ignore the relevance of big data. Thus, data streams and analytics are essential tools for the financial services sector. The new data-driven services can leverage increased revenues with reduced cost and improved efficiencies. It can also help improve security and customer service. Globally, Big Data [Analytics](https://sentium-consulting.com/services/data-analytics "Data Analytics") is big business. It is currently predicted that its market will exceed two hundred billion US dollars, making it one of the biggest drivers for the growth of the banking sector.

Modern Big Data empowered FinTech firms are real challenges for the traditional banking industry. It can offer customers fast and low-cost financial services, make and receive online payments, and offer more advanced benefits such as a peer-to-peer lending facility. There is specific evidence that the traditional banking sectors are catching up rapidly, adding Big Data analytics in their business.  

### **Why does the Finance Industry need Big Data?**

Large-scale Data Analytics can analyse the financial industry's petabytes of structured and unstructured data to anticipate customer behaviours and create strategies for banks and financial institutions. The financial data sets are too large for conventional database applications to handle in real-time.

In short, by collecting and analysing vast quantities of data, financial organisations can anticipate the behaviour of their customers and develop strategies to provide better, safer, and more relevant, and better services. The value of the data lies almost entirely in the way it is gathered, analysed, and interpreted.

### **Opportunities of Big Data in Finance**

High-volume data plays a pivotal role in strengthening various aspects of the business. People’s lives are improved by smoothening the business process by utilising large-scale Data Analytics. Integrating the Internet-of-Things or IoT with the business processes is the most modern direction of data streams for real-world use cases. Sensors linked to objects (things) generating meaningful data and information helping in decision-making are known as Internet-of-Things or IoT.

Decentralisation of the decision-making process is a salient opportunity available in vast datasets studies. The systems are competent, efficient decision-making based on the information available to them and set standards, all with the help of [Big Data](https://sentium-consulting.com/services/ai-forecasting "AI Forecasting"). The minor details of the financial data are not needed to be checked by the accountants.

Predictive analytics and data visualisation are other major utilities of Big Data in the field of finance. The decision-making process is achieved from objective facts and figures when finance practices are dependent on predictive analysis of the financial data. The data analysis processes are simplified in these techniques. The decision-making process has been simplified for accountants and financial analysts by using various data visualisation approaches. 

Financial analyses need a lot of refinement of data into information before it is used, so Big Data techniques are adequate for the purpose. Since vast data is to be managed by the accounts in every facet, the big data analytic solutions are primarily based on data-driven accounts audits. Extensive datasets can control and eliminate the involved risk.

The **top 5 applications** of Big Data in the finance industry are:

1. **Customer Analytics** – **employed by roughly 55% of the sector**: Customer-centred analytics has become a priority, a radical departure from the past when the financial sector was mainly product-centred. The main focus of data insights, systems, and operations is now directed towards the customer base. Knowing the changing markets and customer preferences allows banks and other financial-services companies to quickly develop new customer-centred products and services, seize new market opportunities, and retain customer loyalty. 
2. **Risk and Financial Management** – **adopted by about 23% of the sector**: A large segment of the finance sector utilises big data to enhance risk and financial management by optimising return on equity, fighting fraud, reducing operational risk, and meeting regulatory and compliance requirements.
3. D**evelopment of New Business Models** – **used by almost 15% of the sector**: Fraud prevention is a critical security responsibility. Fraudsters employ experts and massive resources to develop new tools/ways to commit cybercrime. Big Data Analytics can incorporate artificial intelligence and machine learning as software weapons in combating these cyber threats, identifying cyberattacks and fraudulent financial transactions.
4. **Operational [Optimisation](https://sentium-consulting.com/services/data-integration "Data Integration")** –** leveraged by about 4% of the sector**: The operational cost of the financial sector depends on the large unsupervised data needed to be collected and processed on a regular basis. Massive data can serve for overall operational optimisation.
5. **Employee Collaboration** – **engaged by approximately 2% of the sector**: The employees form a large part of the stakeholders of the financial sector. High-volume data can improve the participation of the employees by enriching employee collaboration.

Some examples of the use of Big Data in the financial sector:

* Offering customers credit cards with customised interest rates and incentives which is based on their spending history.
* Suggesting consumer products to the customers that offer value and that match their spending patterns.
* Analysis of customers ATM usage and interactions with call centres to increase customer engagement.
* Use of speech analytics to determine main reasons for repeat calls to call centres from customers.
* Predicting when a customer might close their account and taking steps to dissuade them from doing so.

### Challenges of Big Data in Finance

In the past few years, a large amount of data is being created at a swift pace. The data that has been generated/synthesised over the past few decades has not been generated/synthesised for thousands of years of evolution of human civilisation. 

Data utilisation is necessary and essential to make critical decisions. Out of all of the data created and collected, some data are analysed and utilised. This data can be passed to the company’s tables and increase its revenues, but there is a set of obstacles correlated with the utility of big data in finance.

Investments in data analysis can result from financial companies' struggle and fight for getting [positive returns](https://sentium-consulting.com "Industries"). There could be a couple of grounds that became the basis for negative returns, as mentioned below.

1. Accountants often face some difficulties while inspecting and analysing the correct finance data. Since garbage data can produce garbage outcomes only, thus collecting, observing, and processing debris data has zero utility. Data analytics have evolved from descriptive to diagnostic, predictive, and now gradually to perspective analysis, which has usages in identifying ideal solutions. These analytics produce superior outcomes for data processed and collected from a reliable source. The financial analyst must make judgments on the data types being used. Understanding the correct type of data may overcome this problem.
2. A faulty data strategy may be one of the major issues. If the company expects to have high returns from investments in big data, a sound and rational data strategy and big data implementation plan are needed. The data strategies are required to be analysed regularly. A clear and comprehensive data identification is necessary to minimise the wastage of resources.
3. A company's journey from data analytics to the optimisation and improvement of business operations is long. The approach of the pilot project is one way to improvise, gain insight, and resultantly create quick wins. Taking one smart and small step makes the overall position of a company solid and stable. It is a great challenge to determine the direction of movement that the organisation follows in terms of data analysis implementation.
4. The combination and integration of data are obstacles for financial companies. The creation of informed analysis for people in the field of finance has not been achieved. Without considering the format and location, the integration of various data sets needs to be made clear. Data analytics is a merger of both IT and business operations. Accountants do not usually understand that, which is a threat to data analytics. Usually, this problem takes place when there is an insufficient understanding of knowledge between finance and IT. Some serious issues arise because there is a dissimilarity between the languages of IT and finance, which results in problems in the interpretation of the analysis. This often occurs because the departments are decentralised. A centralised system where the finance teams work with the departments of operations, the supply chain, and sales and marketing is required for better cooperation and a combination of data analytics.

### Data Protection Challenges of Big Data

There are still some regulatory challenges that have to be tackled to enable the technologies proficient in order to provide competitive and effective solutions.

*Data Privacy Protection*

Since May 2018, data privacy protection has become a prime requirement in every business about and from the EU countries. Data privacy protection matters have become mandatory, and every company must comply with the General Data Protection Regulation (GDPR). Data privacy issues have not yet been fully implemented in every big data solution. Financial businesses, such as banking and insurance, collect, store, and use a large amount of personal data. Regulatory requirements of GDPR dictate that personal data must be collected, stored, processed for specified and lawful purposes only. The impact of GDPR for the banking and insurance sectors is significant because individual clients have the right to ask financial services organisations to remove or refrain from using/processing their personal data in particular situations. The financial services sector may incur an additional cost since they are supposed to deal with the individual request of the clients. The removal of some data may lead to the large dataset being grossly modified/shuffled.

*Requirements of Confidentiality*

All third-party information that is processed in Big Data Analytics is supposed to be confidential in nature. Thus, the financial services sector, including the banks and insurance companies, will have to comply with a mandatory requirement of maintaining data confidentiality so that no data breach occurs.

### Conclusions

It is fascinating to notice financial companies have initiated considering big data analysis as a developmental project. Some more prominent companies have already begun to adopt big data analytic techniques. However, the literature review shows that big data is not used in finance, mainly for smaller companies.

Research studies showed that the Big Data method is only advantageous when used with the right set of minds. How the company will enhance its performance is primarily dependent on how the company and its analysts use this critical opportunity. With the usage of automated methods and the right reforms being taken at the right time, along with the integration of big data analysis, the company's revenue can be increased significantly.

Big Data has revolutionised the finance sector in various applications, including real-time stock market analysis, fraud prevention, and machine learning-powered risk analyses. Services can improve customer satisfaction, increase revenues, analyse financial performance, and improve, control, and develop products. Despite several advantages, there remain severe concerns about big data in the field of finance.

The use of [Big Data](https://sentium-consulting.com/blog/introduction-to-big-data-analytics-how-it-works-and-why-it-matters "Big Data Analytics") and techniques to analyse it create substantial competitive advantages for the companies that can do that. There is a set of major challenges in the world of finance that are generated by data streams. All financial products concerning services are dependent on data and, in turn, also produce data. There are still many areas that need particular research developments in big data.

Ongoing data regulatory compliance is the top priority of the financial sector. Large-scale Data Analytics in finance must be customisable to incorporate the requirements of data privacy and confidential protection criteria of GDPR in EU countries.

Further research studies in financial data management systems must be undertaken to determine how the large data sets can be collected/handled and analysed to devise more efficient financial data solutions.



It is common knowledge that getting a suitable job is a difficult task. On the contrary, recruiting the right candidate for a specific job opening is equally challenging for any striving company. Often recruiting tasks are complex, costly, and demanding. There is also competition among companies to recruit and retain highly qualified and experienced employees.

[Big Data](https://sentium-consulting.com/services/data-analytics "Data Analytics") studies can rescue a company from the complex recruitment hurdles. The impact of large-scale data analysis in the recruiting process is undeniable irrespective of industry and business. In this article, we concentrate on the role of big data analytics in the ever-evolving recruitment processes.

### Big Data Improves the Quality of Recruits

A bad hire is not only a misuse of the company resources, but a bad employee can also spoil a company’s work culture. It is necessary not to mention the loss of resources in useless recruitment processes. Nevertheless, how to reduce errors in recruitments? Big data analytics can help reduce errors in intelligently selecting new hires. Big data studies can help a company go for evidence-based recruitment decisions.

HR professionals and recruiters can select the ideal candidates easily using vast datasets analysis of online databases and publicly available candidates’ information. Identifying the suitable candidates with matching requirements becomes a job of sorting based on key performance metrics of the complete set of candidates. [Big data](https://sentium-consulting.com/services/data-integration "Data Integration") also makes it possible to find more applicants per opening and more effective lead sources.

### A Larger Pool of Candidates

In the rarest situation, the recruiting manager instantly finds the right candidate to hire. However, in a normal scenario, the recruitment manager requires interviewing more candidates. The most unpleasant problem is not getting enough pool of candidates to interview.

Big data can address this problem by using analytics instruments. Vast datasets can reach a larger pool of candidates for interviews and selection. A bigger pool of talents can help better interpret and optimise the hiring process by addressing skills gaps and future hiring requirements. Data streams can help have a line-up of candidates ready to interview, thereby making the hiring process faster.

### Enhancing Development and Retention Initiatives

Recruitment of the right candidate does not end the process to a full stop. The recruitment process goes deep inside the employment and up to training and retention. These follow-up programs should be successful. Otherwise, the whole selection process may end up a waste of time and money.

Large-scale data can contribute to employee training and retention. The HR department can evaluate the effectiveness of a training and development program. These studies can also search for ways to improve and simplify the training methods. Studying these data analytics can help craft better employee retention strategies, resulting in increased job satisfaction among the employees.

### Optimisation of Hiring

Over-hiring and under-hiring can result in losses in a business. The former creates additional costs incurring direct monetary losses, while the latter can affect/lower productivity causing indirect losses. The recruiters and HR managers can avoid such unfavourable situations by adopting data-driven hiring plans. These plans can continually be updated based on the needs of the company.

The data processing may also consider the [forecasts](https://sentium-consulting.com/services/ai-forecasting "AI Forecasting") based on the analysis of various recruitment factors like attrition, lateral movement, promotions, and quality of hire. Big data may also help remove bias in recruitments.

### Lowering the Cost of Recruitment

Large-scale Data Analytics provide the hiring departments with a complete view of overall performance versus budget. Data examination can immensely help streamline recruitment spending by utilising the complete picture of the recruitment processes. These studies help the hiring managers determine which lead sources have produced the best quality hires, which job ad campaigns have resulted in more applications, and which platforms have attracted which types of candidates.

The top-level managers can utilise all of this information and make data-backed decisions on where to invest money, be it on mentorships or advertising. Finally, big data studies result in cost savings.

### Conclusions

These five points discussed are just the very few among the many other aspects of a complex recruitment process that big data studies can improve.

The hiring managers can improve the candidate experience, recruiting capacity, and speed of hire by utilising analytics-driven facts and figures, thereby intensifying the employer’s brand image. 

The essential factors that remain are finding the data, classifying it with data scrutiny, and deriving relevant [insights](https://sentium-consulting.com/services/ai-forecasting "AI Forecasting") from the examined data.

More data is not always better if not analysed and inferred correctly. Companies should not go for vanity metrics which are redundant in recruitment processes. The recruiters have to invest in learning data science and extensive datasets processing. The knowledge of data science and analytics would help adopt the appropriate analytics tools.

There is much opportunity to go for data-driven recruiting, and it would help optimise the whole recruitment process and save considerable time and cost.



The transformative power of Artificial Intelligence (AI) is an undeniable reality. From its integration into daily tasks to spurring monumental growth in the global economy, AI's influence is evident. 

The recent [survey](https://cepr.org/voxeu/columns/impact-artificial-intelligence-growth-and-employment#:~\:text=The%20McKinsey%20Global%20Institute%20predicts,in%202030%20$PwC%202017$) released in the spring by The Centre for Macroeconomics (CFM) and The Centre for Economic Policy Research (CEPR) indicates that experts forecast AI will drive global economic growth to an impressive 4-6% annually over the next decade, surpassing the past years' average of 4%. As Mihir Shukla, the co-founder of Automation Anywhere, a global leader in the automation industry, aptly remarked at the 2023 World Economic Forum: "People keep saying AI is coming, but it is already here".

**ChatGPT's Success and Future Projections**

A testament to its rapid adoption is the meteoric rise of generative AI systems like ChatGPT by OpenAI. To contextualise, ChatGPT achieved a user base of 100 million in a mere [60 days](https://www.theguardian.com/technology/2023/feb/02/chatgpt-100-million-users-open-ai-fastest-growing-app#:~\:text=ChatGPT%2C%20the%20popular%20artificial%20intelligence,analysis%20by%20data%20firm%20Similarweb.), eclipsing platforms like Instagram, which took two years to achieve the same feat. These developments signal AI's potency as a catalyst for productivity. Institutions like the McKinsey Global Institute project that nearly [70% of companies](https://cepr.org/voxeu/columns/impact-artificial-intelligence-growth-and-employment#:~\:text=The%20McKinsey%20Global%20Institute%20predicts,in%202030%20$PwC%202017$) will employ some form of AI by 2030. Furthermore, Price Waterhouse Coopers (PWC) suggests that by 2030, AI could bolster [global GDP](https://www.pwc.com/gx/en/issues/data-and-analytics/publications/artificial-intelligence-study.html) by an impressive 14%. 

In today's fast-paced digital world, artificial intelligence and machine learning stand out as transformative tools. They're reshaping industries by automating tasks, personalising customer interactions, and offering [novel insights](https://sentium-consulting.com/services/ai-forecasting). Dive in as we explore how AI can be the linchpin for your business's future growth.

**The Power of AI: Beyond the Buzzwords**

* **Deep learning & its magic**: AI isn't just a buzzword; its subfield, deep learning, is changing the game. Deep learning, using neural networks resembling our brain's structure, has spearheaded advances in computer vision, speech recognition, and [natural language processing](https://sentium-consulting.com/services/nlp). When harnessed right, this could be your business's game-changer.
* **Advantages to unlock with AI**:
  * *Smarter decisions*: With AI-driven analytics, dive deep into data to unearth patterns, guiding strategic moves.
  * *Elevated customer journeys*: Use AI chatbots for instant customer support and [data analysis](https://sentium-consulting.com/services/data-analytics) for tailored marketing, enhancing customer loyalty.
  * *Boosted efficiency*: Say goodbye to tedious tasks. AI, especially through Robotic Process Automation ([RPA](https://sentium-consulting.com/services/rpa)), can automate them, increasing overall productivity.
  * *Staying ahead*: Be the market innovator with AI-driven insights, offering fresh products that resonate with changing consumer desires.
  * *Fortified security*: AI's proactive threat detection ensures robust cybersecurity, shielding from potential breaches.

**Making AI Work for You**

Venturing into AI requires strategy. Here's how to make your journey effective:

* **Vision is key**: Pinpoint what AI should achieve for you. Your goals guide your path.
* **Seek the maestros**: AI is intricate. Collaborate with experts, like us at [Sentium](https://sentium-consulting.com/about), to sail smoothly.
* **Solidify data frameworks**: Robust [data infrastructures](https://sentium-consulting.com/blog/blog-optimising-saas-architecture-key-principles) are non-negotiable. They ensure AI learns from quality data, offering you valuable insights.
* **Empower your troops**: Upskill your team members to navigate the AI landscape efficiently. Knowledgeable teams amplify AI's benefits.
* **Stay agile**: AI adoption isn't a one-time gig. Regularly review and adapt to ensure it's aligned with your vision.

AI isn't just a technological upgrade; it's the catalyst for business metamorphosis. With an informed strategy, you can leverage AI's prowess to escalate decision-making, amplify customer satisfaction, and elevate your business stature.

At **Sentium**, [our commitment](https://sentium-consulting.com/about) is to steer businesses into tomorrow with AI, data analytics, and bespoke software solutions. Together, let's craft your AI-powered success story!



Our world is more connected than ever. Hyper-connected! Our ability to communicate with anyone, anywhere, at any time has redefined boundaries. There is no doubt that the digital realm has become as integral to our daily lives as the air we breathe. 

Yet, similar to our concerns about the quality of the air, the security of our online spaces must be paramount. Cybersecurity, once the domain of IT specialists, is a pressing issue for everyone, especially for businesses harnessing the power of [AI forecasting](https://sentium-consulting.com/services/ai-forecasting "AI Forecasting") and natural language processing ([NLP](https://sentium-consulting.com/services/nlp "NLP")). 

As cyber threats evolve, our defences, enhanced by artificial intelligence automation and NLP, must advance. Let's explore essential strategies to protect sensitive data and ensure our digital environment remains invincible.

**1. Stay Updated on Security Patches**

By utilising AI forecasting, businesses can stay ahead of potential vulnerabilities. The [Orion](https://www.techtarget.com/whatis/feature/SolarWinds-hack-explained-Everything-you-need-to-know), [Ryuk](https://gatefy.com/blog/real-and-famous-cases-ransomware-attacks/), SamSam, and WannaCry ransomware attacks, spanning from 2015 to 2020, targeted outdated software. 

Ryuk ransomware, in particular, spreads via malicious phishing emails. Ransoms to release systems can exceed USD 300,000, placing Ryuk among history’s priciest ransomware. Since its rise in 2018, the FBI attributes over USD 60 million in global damages to Ryuk, impacting more than 100 companies. 

A profound cyberattack in December 2020 targeted Orion by the U.S.-based SolarWinds Corporation. With around 300,000 clients, including almost all Fortune 500 companies and several U.S. federal agencies, SolarWinds' breach had [widespread implications](https://www.techtarget.com/whatis/feature/SolarWinds-hack-explained-Everything-you-need-to-know). This attack, suspected to be from a Russian-backed group, injected the malicious Sunburst code into Orion, compromising countless systems. This episode stands as one of the U.S.'s gravest cyber-espionage incidents due to its duration and the high-profile entities affected.

Software applications today, built from various components, carry heightened risks of unpatched [vulnerabilities](https://www.techtarget.com/whatis/feature/SolarWinds-hack-explained-Everything-you-need-to-know). The introduction of the "software bill of materials" (SBOM) acts as a transparency tool for software. In 2021, the Biden Administration endorsed the use of SBOMs, ensuring U.S. government agencies align with software vendors adhering to these standards.

Regular software updates provide users with the latest security enhancements. Additionally, organisations are advised to adopt advanced threat intelligence tools and instil a strong cybersecurity awareness culture among employees.

**2. Multi-Factor Authentication (MFA) with AI Integration**

Leveraging AI automation, MFA can be more intuitive and user-friendly. As [reported by Symantec](https://www.symantec.com/blogs/feature-stories/2019-internet-security-threat-report), 80% of breaches could have been prevented by MFA. Requiring users to provide two or more verification factors, MFA offers a significant upgrade from the traditional password-only approach to gain access.

**3. Educate and Train Staff**

Drawing insights from analytics, training can be tailor-made to suit the unique needs of a business. According to a [study by Kaspersky](https://www.kaspersky.com/about/press-releases/2018_more-than-half-of-industrial-companies-do-not-report-cyberattacks-to-regulators), over 90% of cyber incidents are caused by human error or uninformed behaviour. Regular training sessions, enhanced with data-driven insights, can keep staff updated on the latest cyber threats and safe online practices.

**4. Backup Data Regularly with Integrated Solutions**

[Data integration](https://sentium-consulting.com/services/data-integration "Data Integration") tools ensure that backups are seamless and comprehensive. The [NotPetya ransomware attack](https://www.wired.com/story/notpetya-cyberattack-ukraine-russia-code-crashed-the-world/) in 2017 affected businesses globally, causing billions in damages. Companies with regular data backups, enhanced by integrated systems, had a safety net. Storing backups in disconnected environments can be a lifeline in recovering from cyberattacks.

**Key Takeaways**

The digital landscape is continually shifting, with new challenges emerging almost daily. Leveraging AI automation, analytics, and NLP can provide even sturdier safeguards against these challenges. 

The human factor, backed by regular education and proactive updates, remains equally crucial. Educating oneself, being proactive about updates, and incorporating tools like MFA can make the difference between a secured digital realm and one vulnerable to risks.

Adopting a holistic cybersecurity strategy is not just about fending off attacks. With the support of our IT [consulting services](https://sentium-consulting.com "Our Services"), like AI automation and transformation strategies, it's about ensuring that our digital world remains a protected and trustworthy space for all its inhabitants.



The world of business is at the cusp of a transformative change, thanks to Generative AI (GenAI). Forget the fleeting allure of tech fads of the past. GenAI is here to stay, promising unmatched operational efficiency and improved communication across enterprises.

### GenAI's Inevitable Rise in the Corporate Landscape

While many technologies promise the moon but underdeliver, GenAI is different. 

In a recent [study](https://go.grammarly.com/forrester-report-23?_gl=1*1bpy9ht*_gcl_aw*R0NMLjE2OTE1OTM1MDYuQ2p3S0NBanc4c3ltQmhBcUVpd0FhVEFfX0V5LTRMOWJsVkx0WFpFUnk1X3lOUzczb1BZODc3bjRSbklFTEJkUm9BVG1lalotRGJtMExSb0NQZDBRQXZEX0J3RQ..*_gcl_dc*R0NMLjE2OTE1OTM1MDYuQ2p3S0NBanc4c3ltQmhBcUVpd0FhVEFfX0V5LTRMOWJsVkx0WFpFUnk1X3lOUzczb1BZODc3bjRSbklFTEJkUm9BVG1lalotRGJtMExSb0NQZDBRQXZEX0J3RQ..*_gcl_au*Mzc1NDg2MTA5LjE2ODY3NTU3Njg.*_ga*MTMwNzIwNjYzOC4xNjg2NzU1NzY4*_ga_CBK9K2ZWWE*MTY5NDE4MDIxMS42LjAuMTY5NDE4MDIxMS42MC4wLjA.), Grammarly engaged Forrester Consulting to delve into the current state of Generative AI. Conducted in May 2023, Forrester's online survey encapsulated views from 301 respondents holding directorial positions or above, spanning diverse departments like customer support, IT, HR, marketing, and sales. 

According to the report, **89% of business decision-makers identify the adoption of GenAI as a [top priority](https://www.grammarly.com/business/learn/forrester-webinar-gen-ai/)**. Its application isn't confined to a few experimental departments. By 2025, nearly all enterprises are expected to employ GenAI for communication support, including writing and editing tasks.

The numbers tell a compelling story: only 26% of surveyed companies have currently implemented GenAI, but an impressive 71% are in the planning or implementation stages.

What is driving this unprecedented speed of adoption? The answer lies in GenAI's unrivalled capability to solve specific business problems, from streamlining workflows to enhancing customer experience.

### Beyond the Hype: Real Business Benefits

GenAI proves to be a transformative force reshaping the very fabric of businesses. About 74% of companies that have implemented [GenAI](https://sentium-consulting.com) report significant benefits, particularly in terms of revenue growth. This isn't surprising considering that improved communication leads to more effective collaboration, quicker decision-making, and ultimately, a more robust bottom line.

Business leaders are optimistic about the fast-paced transformation that GenAI brings. A whopping 62% of decision-makers expect to see their companies transformed within a year and 87% within two years. But this isn't a race; it's a calculated strategic move. Companies are not just rushing to appear innovative; they're leveraging GenAI [to solve real-world issues that impact their operations and revenue.]()

### The Rise of Enterprise-Wide GenAI Strategy

For organisations looking to stay ahead of the curve, a piecemeal approach to adopting GenAI simply would not do. An enterprise-wide strategy is critical to reaping the full benefits of this transformative technology. 

According to Forrester, ignoring the impact of GenAI on existing business processes could be a costly mistake. Importantly, the benefits of GenAI are not limited to a single department or function. Its applications are vast and varied, addressing specific needs of different teams while enabling greater inter-departmental collaboration. Whether it is IT, customer service, or product development, **GenAI is setting a new standard for operational excellence**.

### Unlocking Potential: Tailored GenAI Solutions for Your Business 

While the overarching benefits of GenAI are evident, it's essential to appreciate the [customised solutions](https://sentium-consulting.com "Our Solutions") it can offer to different business sectors. 

For instance, in the **retail **industry, GenAI can optimise inventory management by predicting trends and customer preferences, leading to reduced overhead costs and improved customer satisfaction. In **healthcare**, [GenAI](https://sentium-consulting.com/services/nlp "NLP") can be employed to generate personalised patient care plans, ensuring a more nuanced approach to treatment, thereby enhancing patient outcomes. For **manufacturing**, predictive maintenance, automated quality checks, and optimised production schedules are just a few of the possibilities. 

And in the **finance** sector? Think of GenAI-driven risk assessment models, automating financial [forecasting](https://sentium-consulting.com/services/ai-forecasting "AI Forecasting"), and enhancing fraud detection mechanisms.

### Your Next Step in the GenAI Evolution

Regardless of your industry, the potential applications of GenAI are limitless, waiting to be harnessed. But knowing where to start can be challenging.

That's where we come in. **Our team of experts is ready to collaborate with you, identifying your unique business challenges and designing GenAI solutions tailored to your needs**. Whether you aim to revolutionise customer interactions, optimise back-end processes, or unveil new revenue streams, integrating GenAI can be your game-changer. 

Let us guide your enterprise to newfound heights with the power of Generative AI. Don't be among the 6% of companies slowing down on this revolutionary technology. Be a part of the 94% who are changing the game!

Ready to tap into the power of GenAI? [Our experts](https://sentium-consulting.com/contact) are here to guide you. Dive deep into accurate insights and watch your business evolve like never before. Start your AI transformation journey today!



The COVID-19 pandemic has propelled the Software as a Service (SaaS) market into a period of swift expansion and transformation. [Statista](https://www.statista.com/statistics/505243/worldwide-software-as-a-service-revenue/) reveals that in 2023, the SaaS market is valued at an impressive $197 billion, with projections suggesting an increase to $232 billion by 2024. Furthermore, [Allied Market Research](https://www.alliedmarketresearch.com/press-release/software-as-a-service-saas-market.html) forecasts the market to skyrocket to an astonishing $702.19 billion by 2030.

### SaaS in the Post-COVID Era

This surge is attributed to the global shift to remote working, which has mandated businesses to integrate advanced technologies like AI, machine learning, and IoT for seamless operations. The result? A global spike in demand for [SaaS-based solutions](https://sentium-consulting.com). For instance, acknowledging these evolving demands, Cisco Systems, Inc. launched a next-generation [networking suite](https://www.computerweekly.com/news/252498687/Cisco-launches-next-gen-networking-suite-to-address-post-pandemic-new-normal#:~\:text=Cisco%20has%20launched%20a%20new,by%20the%20Covid%2D19%20pandemic.) in 2021. Designed for the post-pandemic environment, this suite emphasises network application visibility, network-as-a-service (NaaS) solutions, and an expanded secure access service edge architecture.

As we delve deeper into the digital age, having a robust [SaaS](https://www.techtarget.com/searchcloudcomputing/definition/Software-as-a-Service) architecture is no longer just advantageous — it's indispensable. Perfecting this architecture is crucial for harnessing the full potential of SaaS and ensuring its sustained growth in the upcoming decade.

### Key Principles for a Robust Saas Architecture 

SaaS applications, accessible via various platforms, have certain vital components for an ideal architecture.

**Scalability**

* Horizontal scaling, which involves adding new servers, ensures system reliability. Platforms like [Amazon](https://aws.amazon.com/blogs/database/scaling-your-amazon-rds-instance-vertically-and-horizontally/) AWS are proponents of this methodology.
* Load balancing, exemplified by [Netflix](https://netflixtechblog.com/netflix-edge-load-balancing-695308b5548c), maintains system performance and reliability by distributing network traffic.
* Auto-scaling adjusts resources according to demand. Microsoft's Azure [Autoscale](https://learn.microsoft.com/en-us/azure/architecture/best-practices/auto-scaling) stands out here, dynamically responding to workload changes.

**Performance**

* Caching techniques, using tools like [Redis](https://redis.com/solutions/use-cases/caching/#:~\:text=Cache%2Daside,both%20the%20cache%20and%20database), speed up data access and improve system operations.
* Database optimisation, including indexing, enhances system performance. Oracle's solutions spotlight these [optimisation benefits](https://www.oracle.com/a/ocom/docs/database/autonomous-database-value-realization-report.pdf).
* Content Delivery Networks ([CDN](https://www.cloudflare.com/learning/cdn/what-is-a-cdn/)), championed by [Akamai](https://www.akamai.com/glossary/what-is-a-cdn#:~\:text=CDNs%20cache%20content%20like%20web,a%20CDN%20like%20an%20ATM) and Cloudflare, ensure fast content delivery, enhancing the overall digital experience.

**Reliability**

* [Redundancy](https://www.synopsys.com/cloud/insights/benefits-of-redundancy-in-cloud-computing.html#:~\:text=Data%20Redundancy,don't%20lose%20your%20data.), exemplified by [Google Cloud](https://cloud.google.com/network-connectivity/docs/interconnect/how-to/dedicated/creating-redundant-interconnect#:~\:text=Google%20recommends%20redundancy%20), duplicates resources to reduce system failure risks.
* Failover mechanisms redirect traffic during server issues, maintaining service continuity.
* Backup strategies, like those from [Dropbox](https://experience.dropbox.com/en-gb/resources/3-2-1-backup-strategy), protect essential data, anchoring operations in security.

**Security**

* Authentication, such as two-factor methods, verifies user identities, with [platforms](https://auth0.com/resources/whitepapers/build-vs-buy-evaluating-identity-management?utm_content=gbrmfa-mfa-buildvbuywpr\&utm_source=google\&utm_campaign=emea_uki_gbr_all_ciam-all_dg-ao_auth0_search_google_text_kw_utm2\&utm_medium=cpc\&utm_term=2%20factor%20authentication%20software-c\&utm_id=aNK4z0000004GMjGAM\&gad=1\&gclid=CjwKCAjwloynBhBbEiwAGY25dCBMFNnIXhXQXrPdY3LAqFvS6Tl-jQKTgJckGNTXxkMANPt5UcUVChoCC7cQAvD_BwE) like Auth0 highlighting its importance.
* Encryption, using standards like [TLS](https://www.comparitech.com/blog/information-security/tls-encryption/), guards data confidentiality.
* Access Control, demonstrated by Salesforce's [role-based access](https://help.salesforce.com/s/articleView?id=sf.cms_access_control_overview.htm\&type=5), manages user permissions, enhancing data security.

**Multi-tenancy**

* Isolation & Resource Utilisation: Salesforce, once again, excels here by ensuring that [shared resources](https://help.salesforce.com/s/articleView?id=sf.pfs_view_availability.htm\&type=5) are optimally and fairly distributed.
* Data Management: Maintaining data integrity and security is essential in a multi-tenant environment. Tools like Amazon RDS or Microsoft Azure SQL Database can automate backup processes and ensure data reliability.
* Databases: The decision between relational and NoSQL databases is pivotal. MongoDB, for instance, provides flexible [NoSQL solutions](https://www.mongodb.com/nosql-explained).
* Data Retrieval: Efficient querying and indexing methods can revolutionise data access speeds.

In the evolving SaaS environment, regular architectural reviews are essential. With the industry's upward momentum, perfecting foundational elements is paramount. Stay [ahead](https://sentium-consulting.com/), stay informed.



As the world pivots towards an eco-conscious future, the symbiotic relationship between technology and sustainability becomes increasingly evident. With rising concerns about climate change and environmental degradation, cutting-edge solutions — particularly those powered by [AI](https://sentium-consulting.com/) — emerge as the catalysts in shaping our green future.

### The Global Climate Commitment

Nations worldwide are setting ambitious benchmarks to counteract climate change. The Paris Agreement, along with data from [Climate Watch](https://www.climatewatchdata.org/2020-ndc-tracker), underscores this commitment:

* China eyes carbon neutrality by 2060, with a CO2 peak projected for 2030.
* The U.S. aims for a 50-52% GHG emission cut from 2005 figures by 2030.
* The EU seeks a 55% reduction by 2030, referencing 1990 statistics.
* The UK is on track for a minimum 68% reduction within the same period.

Tackling primary culprits of global greenhouse [gas emissions](https://ourworldindata.org/emissions-by-sector), including energy production, transportation, construction, agriculture, and heating, becomes crucial to realising these goals.

### Pioneering GreenTech Solutions

[Green hydrogen](https://www.iea.org/energy-system/low-emission-fuels/hydrogen), a carbon-neutral fuel derived from water molecule splitting, is central to global [net-zero emissions](https://www.weforum.org/reports/the-net-zero-industry-tracker/digest) ambitions set for 2050. Yet, transitioning to green hydrogen introduces challenges, particularly concerning demand predictions, certification, regulations, and infrastructure.

Several innovative companies are leading the green revolution. [Infarm](https://www.infarm.com/vision "vertical farming"), for instance, is reinventing vegetable cultivation with vertical farming, consuming significantly fewer resources. On the other hand, Relativity Space is showcasing the potential of [3D printing](https://www.relativityspace.com/terran-r) in rocket construction. These pioneers, alongside global brands like [Tetra Pak](https://www.tetrapak.com/about-tetra-pak/news-and-events/newsarchive/tetra-pak-makes-further-progress-on-sustainability-transformation) and [Unilever](https://www.unilever.com/planet-and-society/climate-action/), are championing the shift towards sustainable consumption.

### Technology's Role in an Eco-Friendly Future

The proliferation of solar and wind energy applied sciences and the advent of smart grids underline technology's vital role in spearheading a [green shift](https://www.csm.tech/blog-details/why-green-tech-will-fuel-the-sustainability-engine/).

**Smart Cities and Sustainable Infrastructure**

Smart cities employ data analytics and automation, offering innovative solutions that minimise environmental impact. Coupled with sustainable construction and artificial intelligence capabilities, this ensures our urban spaces evolve to be both tech-savvy and eco-conscious.

**AI's Environmental Construction Advances**

AI, through tools like ChatGPT, simplifies the management of environmental records. [Automated systems ](https://sentium-consulting.com/services/data-analytics)can instantaneously access, process, and evaluate environmental metrics, offering insights for better decision-making and eco-friendly outcomes.

**Data Analytics: Monitoring Urban Carbon Footprints**

Natural language processing platforms continuously gauge corporate carbon footprints by examining power use, waste generation, and transport demands. This data empowers businesses to adopt green practices and assists policymakers in setting eco-focused regulations.

**Machine-Driven Transportation Solutions**

[AI's route optimisation](https://fareye.com/resources/blogs/ai-route-optimization), leveraging machine learning and predictive analytics, reshapes transportation. Integrating with IoT and autonomous vehicles, AI ensures efficient and customer-centric transport solutions.

**The Agricultural Tech Evolution**

Emerging precision farming tools are reshaping agricultural resource management. Machine learning techniques promise even greater transformation:

* Enhanced yield predictions: AI's data analytics capabilities offer [accurate forecasts](https://www.techtarget.com/searchenterpriseai/feature/Agricultural-AI-yields-better-crops-through-data-analytics) on weather, soil, and pests.
* Efficient resource management: Neural networks regulate water use and [optimise fertiliser](https://www.sciencedirect.com/science/article/pii/S258972172030012X) and pesticide application.
* [Farming automation](https://www.sciencedirect.com/science/article/pii/S277323712200020X): Autonomous AI-driven machinery can perform planting, harvesting, and weeding, making farming more precise and efficient.

With AI at the helm, agriculture is primed to meet rising global food demands sustainably.

**Crafting Green Supply Chains in a Circular Economy**

The circular economy, emphasising recycling and reuse, gains traction as AI drives supply chain [sustainability](https://www.supplychainbrain.com/articles/35019-how-ai-is-driving-sustainability-in-the-supply-chain):

* Accurate demand [forecasting](https://sentium-consulting.com/services/ai-forecasting): AI facilitates demand prediction, minimising overproduction.
* Ethical sourcing with AI: knowledge engineering tools prioritise sustainable suppliers, ensuring products are responsibly made.
* End-to-end chain [optimisation](https://www.sciencedirect.com/science/article/pii/S0040162522000403): machine learning systems provide a holistic view of the supply chain, promoting waste reduction and lifecycle-focused design.

In a world championing environmental science, AI's role in supply chain management is indispensable, bridging the gap between sustainability goals and actionable outcomes.

The potential of GreenTech is vast. Yet, it’s our collective commitment, along with policy reforms and global collaboration, that will transform this potential into a tangible, green reality.



Mondays have a bad reputation. They are linked to increases in stress and anxiety, and many other health issues. After enjoying two days off, relaxing, and disconnecting from your weekly tasks and worries, Mondays’ energy may seem brutal and cannot be easily ignored. It starts with floating notifications on your mobile, then there are briefs, meetings, project reminders, assignments, and yes, more meetings. Mondays are tough, indeed, and there’s a nearly global consensus on that. Perhaps, one of the best things about Monday is that it is the most common day for bank holidays.

But what if there is a way out of the stress bubble? What if you could get the support you need to carry out all the tasks that may take you hours and those repetitive activities you could skip and focus on more important matters? What if we could make your Mondays friendlier?

### **In many forms, AI is already part of our lives**

[Artificial Intelligence](https://sentium-consulting.com/ "AI-powered models") (AI) assistants are becoming popular and are used everywhere, from smartphones to educational institutions and medical organisations. They are already part of our lives, and they can perform many of the same duties human assistants do, like reading text, processing emails, taking dictation, scheduling, and making calls. However, they can be programmed to do more complex things. Also, they do not need a break, and you can take them anywhere.

### **AI assistants can be tailored on your needs and behaviour**

What is an AI assistant? It is a software program that uses technologies like Natural Language Processing (NLP) to follow voice and text commands, and it’s often based on cloud, meaning that it can be accessed from anywhere if you have an internet connection. You can use it daily by simply connecting it to your smartphone or other devices.

An AI assistant can not only be integrated into your daily life, but it can be personalised to answer your demands and adapt to your behaviour. They rely on self-teaching algorithms and learn your voice tones, preferences, and anticipate your needs.

 Whether you want a standard AI secretary to take notes during your weekly meetings, transcribe, make summaries, or your own master Yoda to guide you through ‘the force field’, we know the way to make it happen.

[80% of businesses](https://www.servicebell.com/post/chatbot-statistics "Chatbot statistics") worldwide are already taking advantage of technology advancement and use a chatbot or a virtual assistant to connect and engage with customers.

### **How we build your own AI assistant**

To create your own AI assistant, we need to follow a few general steps that help us create a profile and offer you a customised and friendly model.

The first step is to **define your requirements**. We determine what you want your AI assistant to do. You may consider tasks like scheduling appointments or calls, answering questions, providing reminders, sending emails, or performing specific functions.

Based on your needs, we will build **a custom system** using machine learning frameworks like OpenAI and Longchain. We research and select the solution or the platform that best suits your needs.

The next step is to **design the conversational flow**. We plan the interaction flow between the user and the [AI assistant](https://sentium-consulting.com/services/nlp "NLP solutions"), define the types of user inputs and the corresponding assistant responses, and consider the different scenarios and even peculiar cases that may arise during the conversation.

**Collecting and labelling training data** is essential because we need to teach the AI assistant to understand and respond accurately. To achieve this goal, we gather a dataset of user inputs and the desired responses and label the data to provide the assistant with examples of correct behaviour.

Based on the previous process, our team uses machine learning techniques to **train your AI model**. The specific training stage will depend on the platform or framework we choose. It may involve training a Natural Language Processing (NLP) model, a chatbot, or a combination of techniques.

Now, it is time to **implement the AI assistant** by developing the software infrastructure to integrate the trained AI model into your desired platform or application. It could involve writing code, using APIs provided by the chosen platform or configuring a chatbot framework.

Before moving on, we must extensively **test your AI assistant** to identify and fix any issues or bugs. We continuously refine and improve its responses based on user feedback and real-world usage.

Once you are happy with the performance recorded during the previous stage, we **deploy your AI assistant** to the desired platform and make it available to you and/or other users. We monitor its performance and collect your feedback for ongoing improvements.

### **Top benefits you may enjoy using your AI assistant**

There are numerous benefits to individuals and businesses that can be enjoyed by choosing a customised AI assistant.  

 Here are the most common:

* **Increased productivity**: automating and handling various tasks simultaneously - managing emails and calendars, booking appointments, setting reminders, and searching for information, without losing focus or efficiency - and saving time and enabling individuals to focus on more critical and complex work.
* **Enhanced efficiency**: performing activities quickly and accurately, often outperforming humans in repetitive or data-intensive activities, [analysing large datasets](https://sentium-consulting.com/services/data-analytics "Data analytics"), and providing data-driven insights, which can improve efficiency in decision-making and operations.
* **24/7 availability**: unlike humans, AI assistants are available round the clock and available to support you, answer questions, and perform duties at any time, even outside regular working hours.
* **Personalised assistance and voice command**: learning from user interactions and preferences, understanding individual needs, behaviours, which allow AI assistants to deliver a more personalised and customised experience; and all the functions can be available hands-free, activated, and controlled through voice commands.
* **Improved accuracy and consistency**: minimising errors and ensuring consistency in their assignments; performing calculations, analysing data, and executing actions with high accuracy, reducing the likelihood of human error and inconsistencies.
* **Language translation and communication support**: helping bridge language barriers by providing real-time translation services, facilitating communication and collaboration with individuals who speak different languages.
* **Continuous learning and improvement**: continuous learning from user interactions, adapting to changing preferences, and improving their performance over time. 
* **Integration with other systems and devices**: integrating with various software applications, devices, and services, allowing seamless interaction and coordination across different platforms. This integration enhances the AI assistant’s functionality and expands its capabilities.

Building a sophisticated AI assistant requires advanced knowledge in the areas of NLP, Machine Learning (ML), and software development. [Our result-driven team](https://sentium-consulting.com/team "Sentium's team") has extensive know-how, technical expertise, and plenty of creativity to design an efficient bespoke AI model and ensure your working days will be brighter.

And wherever your Monday will take you, may the force be with you.



People generate massive amounts of data daily, and each year, the amount of data generated grows rapidly. It’s estimated that by 2025 the total amount of data created, captured, copied, and consumed annually will grow to more than [180 zettabytes](https://www.statista.com/statistics/871513/worldwide-data-created).

Think of it that way, whenever customers visit your website or your retail store, buy from you, leave feedback, or review your product, join your email list, or get in touch with you in various ways, they leave a trace that can be collected, processed, and analysed. On top of that, there is data coming from mobile phones, apps, IoT devices, social media, and even fridges that have various sensors and are connected to the Internet.

But data doesn’t come only from your customers or outside your organisation; it is also generated within your company. Calls, emails, marketing campaigns, sales, and CRM systems all create enormous amounts of information that can give you a business advantage if effectively used.

However, globally only a small part of generated data is kept, and even less is analysed adequately by businesses. To stay competitive in today’s world, your company must be [data-driven](https://sentium-consulting.com/ "Data Automation"), yet simple data gathering is not enough. No matter how much data you collect, you only get value from it if you evaluate and [act on](https://sentium-consulting.com/) your data.

### What is Big Data Analytics?

By Big Data, we mean huge volume, high velocity, and immensely varied data that is too large and complex for processing by traditional database management tools.

Big Data Analytics, in simple words, is a process of examining vast datasets to uncover information that can help your company make informed business decisions. Large-scale data analysis uncover hidden patterns and find correlations, market trends, and customer preferences. With big [data analytics](https://sentium-consulting.com/services/data-analytics), you can make better and faster decisions and model and predict future outcomes.

### How Big Data Analytics works

Data streams encompass such a vast amount of information that Big Data Analytics is more of a combination of different technologies and techniques working together than a single do it all solution.

Before your data can be appropriately analysed, it must be collected, processed, and cleaned. The information collection process will look different depending on your business model and niche. The type of data affects how it is sourced, collected, and scaled. You can generally collect structured or unstructured data, with the latter being more commonly collected from [various sources](https://www.ibm.com/cloud/blog/structured-vs-unstructured-data\).).

Structured data is stored in tabular formats - such as Excel sheets or SQL databases - that take less space. It is very scalable since it can be stored in data warehouses.

On the other hand, unstructured data is saved as media files or NoSQL databases, which take up more space. It can be stored in data lakes, making scaling difficult.

When data is properly collected and stored, the next step is to arrange the data correctly to get actionable results on analytical queries. There’s more than one way to organise data, and the method will depend on the task and your company’s resources.

We classify processing environments into centralised and distributed. Centralised processing happens when all the processing takes place on one computer system, for example, a dedicated host. Distributed processing, on the other hand, happens on multiple systems and is often required with vast datasets as it allows separating large datasets into smaller chunks and processing them in parallel. 

Based on the [processing](https://www.geeksforgeeks.org/difference-between-batch-processing-and-real-time-processing-system/#:~\:text=In%20batch%20processing%20processor%20only,and%20active%20all%20the%20time.\&text=Jobs%20with%20similar%20requirements%20are,the%20computer%20as%20a%20group.) time, there is batch processing and real-time processing. Batch processing is a slower process where large pieces of data are collected over time and processed in batches. It’s preferred where there is a need for accuracy over speed.  

Real-time processing is used when speed over accuracy is preferred. It’s more complex and costly than batch processing. The final step before data can be evaluated is data cleansing. This is an important step that improves the data quality and ultimately leads to more accurate results. In short, the process can be described as scrubbing for any errors, duplications, irrelevant data, etc. Any wrong or irrelevant data must be removed or taken into consideration.

### Types of Big Data Analytics

There are 4 types of [data analytics](https://sentium-consulting.com/services/data-analytics "Data Analytics"):

**1. Descriptive Analytics - “What happened?”**

These tools tell you what happened by summarising past data and presenting it in a way that is easy to understand, for example, as simple reports or visualisations.

Descriptive tools seldom incorporate AI and machine learning techniques. A good example would be a sales report that shows you all the sales data from the past few years in a simple graph.

**2. Diagnostic Analytics – “Why did this happen?”**

Diagnostic tools answer why something happened and help you find the root cause of the problem. They are more advanced than Descriptive Analytics and often use techniques like [data mining](https://www.simplilearn.com/what-is-data-mining-article) but don’t necessarily need to rely on AI and machine learning to be accurate.

Imagine that you are in the [SaaS](https://sentium-consulting.com/blog/blog-optimising-saas-architecture-key-principles "Saas architecture") business, losing users. You could use diagnostic analytics to find out why your users are cancelling their subscriptions and then develop a solution to counter this problem.

**3. Predictive Analytics - “What might happen in the future?”**

Predictive analytics rely on data mining, AI, and [machine learning](https://www.simplilearn.com/tutorials/machine-learning-tutorial/what-is-machine-learning) to interpret current and historical data and predict the future. It’s widely used in many different sectors, from retail to health to finance.

In practice, predictive analytics can be used to predict market and customer trends or a soon-to-happen failure in a piece of machinery. It’s also used in credit scoring systems to help approve or deny loans within minutes.

**4. Prescriptive Analytics - “What should we do next?”**

Prescriptive analytics, in short, provides the solution to a particular problem. Based on data and considering all relevant factors, it helps you decide what you should do next.  

Those tools are widely used in business. They help companies make data-driven decisions in sales and marketing, among others. One example would be lead scoring, a process of ranking the sales readiness of a lead.

Some of the biggest companies, like Netflix, use prescriptive analytics. Netflix employs this analytics to predict what viewers are most likely to watch and decide the optimal timing for those recommendations.

### **Benefits and Advantages of Using Big Data Analytics in Business**

Large-scale data analytics helps organisations in many ways. It often has a tremendous impact on sales, customer service, [marketing](https://www.forbes.com/sites/forbescommunicationscouncil/2022/02/03/market-smarter-how-to-personalize-customer-interactions-with-data-analytics/?sh=58a5be806516), and new product development, to name a few. Data-driven decisions lead to more efficient operations, higher profits, and happier customers.

Depending on your sector and business model, the benefits of deploying big data analytics vary. Most commonly, businesses use it to:

**1. Drive Innovation**

Innovative companies constantly look for new products and ways to improve existing processes and services. Vast datasets examination helps organisations interpret sales data, product performance, and customer feedback, improving existing products or leading to new product development.

Evaluating the collected company’s insights can reduce costs, identify new opportunities for revenue generation, improve efficiency, and lead to new business strategies and marketing techniques that will create [competitive advantage](https://sloanreview.mit.edu/projects/analytics-as-a-source-of-business-innovation/).

**2. Accelerate Business Transformation**

Data streams analysis is also crucial for implementing operational strategies and facilitating business transformation. Evaluating and acting on your data can help you increase operational profitability and provide your company with new innovation-driven opportunities.

Companies that use big data analytics see vast improvements in many areas, including financial management, product development, and time/cost saving.

According to a recent survey, the COVID pandemic has speeded up organisations’ data-driven strategy, and [78% of IT decision-makers agree](https://foundryco.com/tools-for-marketers/research-data-and-analytics/) that collecting and analysing data will change the way their companies do business in the next 1 to 3 years.

Again, high-volume data can help you transform your business in different ways depending on your niche and business model. Examples include automating manual processes, thus saving hundreds of hours per year and reducing costs. Or, in the case of retail, learning and analysing customer habits to create recommendations based on purchase history.

**3. Drive Better Customer Service**

Data streams can also be used to improve your customer service. Companies commonly use big data analytics to improve customer experience and increase customer retention and royalty.

Analysing inputs from your website, social media, chatbots, sales, and marketing can help you solve customers’ problems faster or prevent them from happening in the first place. And in some sectors, just a small, [5% increase](https://media.bain.com/Images/BB_Prescription_cutting_costs.pdf) in customer retention can result in more than a 25% increase in profit.

**4. Increase Security**

Another critical area for big data analytics is  [IT security](https://www.exabeam.com/siem-guide/siem-analytics/). In the past, security incidents were detected by two analytical techniques: correlation rules, network vulnerabilities, and risk assessment. They were good at detecting known bad behaviour but suffered from false positives and exposure to zero-day attacks.

Thanks to large-scale data, it is now possible to collect and evaluate all internal and external information to bring your IT security to the next level. Big Data [solutions](https://sentium-consulting.com/contact) can identify personnel, network, or device behaviour anomalies. Thanks to machine learning, it is possible to stop zero-day malware.



We develop state-of-the-art machine learning systems that can identify patterns and make unprecedented predictions in various domains. We utilise advanced neural network architectures normally reserved for academia and apply them to commercial applications.



We help businesses make data-driven decisions by unlocking valuable insights and presenting them with intuitive interfaces that simplify accessing, understanding, and analysing data. Data is fuelling innovation across all sectors. However, due to cumbersome software or reliance on busy technical staff, senior executives often cannot quickly and conveniently build an accurate and complete picture of the context around key decisions.



Most organisations suffer from fragmented and siloed data, making it impossible to get a complete overview of business performance or perform company-wide analytics. We connect to your data wherever it may be and design modern data pipelines to bring your data reliably and securely to one place.



Our advanced natural language processing solutions can read and ‘understand’ your data, helping you extract the full value your data holds. We specialise in designing and implementing bespoke state-of-the-art NLP tools and solutions that can read and ‘understand’ the meaning of your data, helping you get the most out of it.



Our robotic process automation solutions can help your business improve operational processes’ efficiency, reduce costs and kickstart digital transformation. We provide full-fledged RPA solutions covering everything from automation consulting to end-to-end RPA development.


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