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  • AI INSIGHTS FOR BUSINESS VALUE

    Artificial intelligence (AI) brings great value to business in a number of ways. The benefits include: Increased Efficiency and Automation; Enhanced Decision Making; Improved Customer Experience and Innovation and New Products. INCREASED EFFICIENCY AND AUTOMATION AI can automate repetitive tasks, freeing up time for more strategic work. This can lead to significant cost savings and improved productivity. For instance, AI-powered chatbots can handle customer service inquiries, while AI algorithms can analyse data to streamline operations. AI is particularly effective at repetitive tasks and automating entire workflows. Repetitive tasks are prone to human error but well tested AI can deliver the same quality results minimising errors and reducing operational costs. AI can analyse documents such as contract or loan applications, verify content against complex rules and make both rule and risk based decisions. AI can also be used to identify exceptional applications that require human attention. These steps can be used to streamline processes providing faster and more consistent responses to customers. AI can be used to gather data in a variety of ways including soliciting information from customers in a natural series of questions. For example, a travel booking chatbot might start asking what type of holiday the customer was after before discussing possible destination, then accommodation. AI can be used to manage inventory, production schedules or shipping routes to optimise the supply chain. This helps businesses reduce costs, minimises occurrences of being out of stock and ensures timely delivery of goods. AI can continuously adapt to changing data with machine learning (ML). ML algorithms can learn from data and improve their performance over time. This means that AI-powered automation can continuously adapt and become more efficient as it processes more data. This might be used to adapt to changing customer behaviour or market conditions. It could be used to monitor and adapt internal processes to increase efficiency, minimise response times or maximise re-use. AI can be applied to predictive maintenance. Using data about component age and operating metrics an AI can predict potential failure risk and schedule preventative maintenance. These scheduled maintenance slots can avoid peak operating times reducing disruption and dramatically reducing costly unexpected failures. This approach can be applied to the automation of order processing through to customer support. AI can work 24/7 freeing up staff to focus on strategic direction and higher value work. No AI can completely replace the expertise and experience of humans but can work collaboratively. Examples include AI assistants scheduling and summarising meetings, writing or summarising documents and emails. This increases employee efficiency and boosts productivity. Different solutions can make the most of AI-human collaboration with human-in-the-loop and human-over-the-loop. In the former collaborations, AI will defer one or more steps in a process to a human expert. In the latter, an AI will provide the human with summarised information and the option to intervene if required. By automating tasks and streamlining workflows, AI can significantly improve business efficiency. This translates to reduced costs, improved productivity of the workforce and the business, faster response times and better scalability. ENHANCED DECISION MAKING AI can analyse large amounts of data quickly to identify patterns and trends that are not practical with other techniques. This supports businesses in making better, data driven decisions about everything from product development to marketing campaigns. AI is already in use for risk assessment and fraud detection. Traditional data analysis often involves sifting through large datasets, which can be time-consuming and lead to overlooking crucial details. AI algorithms can examine massive amounts of structured data (e.g. sales data) and unstructured data (e.g. contracts or social media posts) faster and more comprehensively than humans. AI can identify subtle patterns and trends in data that might escape human attention. These patterns can provide valuable insights into customer behaviour, buying habits or potential risks. For instance, AI can analyse customer purchase history to predict future buying patterns and tailor marketing campaigns accordingly. AI can be used for predictive analytics, a technique that uses data and algorithms to forecast future outcomes. This allows businesses to make data-driven decisions about everything from inventory management to product development. For example, an e-commerce platform might use AI to predict demand for specific products during peak seasons, ensuring stock levels to meet but do not excessively exceed customer demand. AI can be applied to financial data to identify potential risks, such as fraudulent transactions or loan defaults. This allows businesses to take proactive measures to mitigate these risks, protecting the business from losses. For example, banks might leverage AI to analyse loan applications and flag those with a high probability of default. AI can go beyond just analysing numbers. AI can analyse customer reviews, social media sentiment and other forms of unstructured data to gain insights into customer preferences and satisfaction. This allows businesses to make better decisions about product development, marketing strategies and customer service. All of the above can be done faster with AI providing insight to support decision making faster supporting timely decision making and business agility. IMPROVED CUSTOMER EXPERIENCE AI can personalise customer experience by providing targeted recommendations and support. AI chatbots can answer customer questions 24/7 and AI can be used to suggest products or services that customers are most likely to be interested in. The customer’s experience defines how they perceive the business. Bad customer experience can tarnish a brand and take a long time to recover. Good customer experience can elevate a brand beyond the competition and help drive sales. As already discussed, AI can analyse customer data, including purchase history, browsing behaviour and past interactions, to understand individual preferences. This allows businesses to tailor product recommendations, marketing messages and support interactions to each customer. For instance, an online retailer might use AI to recommend products similar to those a customer has previously purchased. AI-powered chatbots can provide instant customer support around the clock, answering frequently asked questions, troubleshooting common issues and directing customers to helpful resources. This allows customers to get the help they need whenever they need it, improving overall satisfaction. AI can be used to predict potential issues before they arise. For example, an AI system might identify a customer who is likely to cancel a subscription based on their recent activity. The business can then proactively reach out to the customer with retention offers. AI can categorise customer sentiment. This allows businesses to identify areas where they are excelling and areas where they can improve. AI can also be used to create automated feedback mechanisms, including surveys or chatbots, to gather customer feedback in a timely and efficient manner. Virtual assistants and chatbots use natural language when engaging with customers. These interactions provide better and more open ended experiences for customers in answering questions, completing tasks or providing personalised recommendations. INNOVATION AND NEW PRODUCTS AI can be used to develop new products and services that would not be possible without it. For example, AI is being used to develop self-driving cars and new medical treatments. AI is already proving to be a game-changer in the world of innovation, acting as a powerful tool to generate new ideas and develop groundbreaking products. AI excels at pattern recognition in a wide variety of data. It can sift through scientific research papers, material properties and market trends, to identify hidden connections and potential applications that are just not practical any other way. This can lead to the entirely new application of materials, more effective drug combinations or innovative product designs. AI algorithms are already being used on genomic data to identify new targets for drug development with the potential for breakthroughs in personalised medicine. AI can be used to generate new ideas and concepts by drawing inspiration from existing data. AI applied to existing products, customer preferences or even creative text formats can yield fresh ideas for product features or entirely new inventions. For example, AI has been used to design new fashion styles or generate musical compositions with unique characteristics. AI can speed up early product development by creating AI simulations and virtual prototypes of new products, allowing for rapid testing and iteration. This can significantly reduce the time and cost associated with traditional product development cycles. AI can then analyse the results of the simulations and tests to identify areas for improvement, helping to refine the product before it goes into production. AI can personalise products and services to individual customer needs. This allows for the creation of highly customised products or even products that can learn and adapt to user preferences over time. AI can be used to optimise the manufacturing process for these customised products, making mass adoption more feasible. In brief, AI provides businesses with the ability to offer their customers more capability with less effort. If you have a Gen AI application idea and would like to see how our AI and Data Solutions team can help to grow your business, let’s talk!

  • RECOMMENDATIONS FOR BUSINESS LEADERS ON GEN AI

    Are you a CEO, CxO or entrepreneur amazed by Generative AI raw power yet struggling to visualise how to monetise its value for your business or venture? We have completed value-focussed AI projects for several clients. Despite ranging across different industries, there are a few key insights and learnings that apply to all cases. Here you can read about them and consider how to incorporate them in your AI explorations! SO HOW DO YOU DRIVE BUSINESS VALUE FROM AI INVESTMENTS? Here are a few, key learnings from our experience delivering AI POCs and MVPs for clients across several industries including media, analysis, recruitment and financial services: 1. Be very (very) clear what value you want to deliver from your investment, and develop measurable performance criteria to compare the ‘before’ (no Gen AI) and ‘after’ (Gen-AI assisted) scenarios. Review and improve these criteria continuously during your project. Design rigorous measurement and evaluation processes that provide actionable feedback to improve the solution during development. 2. Understand and model the human process. Generative AI projects in particular are often designed to augment or mimic an existing human process: identifying how an expert person would carry out the task can directly inform the design of a viable AI approach. Decide your level of ambition i.e. which tasks to be fully taken away and which to be assisted by the ‘co-pilot’ AI being developed? 3. Have the ambition to explore beyond automation! A Pluralit trained AI model can come up with interesting and unexpected analysis which can complement the judgement and experience of your team acting as a type of analyst co-pilot. And known risks such as hallucinations and accidental copyright infringements can be managed with the right partner like Pluralit. 4. Experiment, using AI experts such as Pluralit to select and implement appropriate AI technologies, initially as low-cost Proofs of Concept. Use rapid iteration, in an open and transparent process that engages people in your business with the AI team. This makes for enjoyable, joint learning and results that are ultimately trusted and adopted in the business. 5. Leverage (in the true sense) the value of your existing, unique data. At the very least, availability of data (such as the existing input and output of human processes) will help the AI team to identify patterns, develop and test approaches. At best, it may enable continuous machine learning where the AI itself is trained, and continues to learn based on your unique business knowledge. 6. It's never just about the tech. You need to pull other levers beyond AI to get value from your digital investment, including: change management, programme management, process engineering, testing expertise and agile innovation. If you have a Gen AI application idea and would like to see how our AI and Data Solutions team can help to grow your business, let’s talk! We have developed the Pluralit AI Skills EngineTM, a reusable framework for rapid Gen AI value delivery.

  • FOUNDATIONS FOR SUCCESSFUL DATA GOVERNANCE

    Data governance is the cornerstone of an organisation's success in managing, using and protecting data. In a landscape that is increasingly reliant on data-driven insights, understanding the nuances of data governance is imperative. This article explores the fundamental principles and essential elements that are critical to building a robust data governance framework. From defining data governance to exploring its critical components, discover how establishing a solid foundation can guide organisations towards effective data management and decision making. WHAT IS DATA GOVERNANCE? Governance is the act of making and enforcing decisions and data governance is the act of making and enforcing those decisions for data within an organisation. Decisions are not made in a vacuum and successful data governance is an integral part of a data strategy describing the organisation's approach to acquisition, management, use and disposal of data. Data governance is the set of policies and processes for the use of data in the organisation. Establishing data governance provides assurance to the accountable executives, the board and other stakeholders of an organisation that data is managed appropriately without the need to be directly involved in every data decision. The critical attributes of data governance are: Accountability: clearly defining data ownership. Transparency: ensuring clarity of what data is collected, used and shared. Proportionality: implementing governance measures proportional to associated data risk. Limitation: collecting and using data solely for legitimate purposes. Accuracy: knowing data completeness, currency and accuracy. Why is Data Governance essential? Data governance is important for every organisation for two key reasons. The first is obviously the value of data. Organisations are increasingly moving towards data driven decision making and increasing the use of AI with its deep dependence on data. Organisations gain competitive advantage with data by better understanding potential customers, stakeholders, partners and critically, their own operation. The second shouldn’t be a surprise but often is. Data is a liability. Every byte of data held by an organisation is a liability with associated costs for storage and management. Data is subject to legal restrictions on what can be held, for how long and what may not be lost. To be valuable, data must be accessible to those who should have access and protected from those without rights. Data needs to be findable to avoid duplication and promote efficiency. It often requires additional information or metadata to record its origin, age, licence and quality. Data theft or data loss can expose an organisation to substantial regulatory and reputation risks. Without appropriate governance, the opportunities and risks associated with data cannot be managed in a systematic way. DATA GOVERNANCE FRAMEWORK There are many data governance frameworks available including those by some of the larger consultancies. However, understanding the purpose and importance of each governance element is critical. Without this understanding, effective application of governance is unlikely. Successful data governance needs to be part of an encompassing data strategy. The strategy will elucidate the value and risk of data and the organisation's appetite to that risk and its approach to liberating the value in the data. Without a strategy, there is nothing to govern against and decisions become matters of opinion. Assuming we have a sound data strategy, what aspects should the data governance framework cover? Purpose Perhaps the single most important aspect of data and its governance is to define and record the purpose of the data. It is remarkable how many organisations are maintaining data stores for data they have no purpose for or don’t even know about. Data purpose provides essential context for governance decisions, ensuring alignment of data acquisition, storage and use to the purpose. It also informs non-functional data storage requirements, including resilience and accessibility. The purpose of data justifies its cost. The purpose identifies the value, avoided cost or mitigated risk for the organisation. Purposes change or disappear which we discuss below under “roadmap”. The purpose is key in selecting an appropriate owner for the data. Ownership The owner of the data is the person accountable for the day to day decisions regarding the data. This is not the data storage system owner but the business owner of the data asset. Their role is to ensure the purpose is up-to-date and that the decisions about the use of the data is aligned to the provenance, rights, licences and quality of the data. They understand the security risk and implications of the data. They own the data, the roadmap for the data and the cost of managing it. In the UK Government terminology, they are the Information Asset Owner. If there are changes to the sources or sinks of the data, it is the owner who approves the source or accepts the new sink. The owner understands the data and is accountable for its quality and ensuring it is fit for purpose. They should be familiar with its sources and the terms of use and familiar with the sinks, ensuring that the data’s use fits within its licence. Without an identified owner the value and liability of the data is not understood. This can lead to issues including ongoing costs of unused data, out-of-date access controls, breach of data rights or licences, inappropriate quality, duplication and nugatory work. Roadmap All data in an organisation needs a roadmap. It should include major changes in purpose or scope and include plans for the retirement of the data. The roadmap is managed by the owner and helps to communicate the anticipated future state of the data store. This is particularly useful in sink and application planning. The roadmap should anticipate changes in organisational strategy, legislation and supplier agreements. It provides useful input into negotiating supplier agreements and licences and allows proactive decision making to support future needs. Sources Understanding data sources offers valuable insights into data provenance. Some data is directly created from a measurement or human input but most data is in some sense derived. It may be the combination of multiple sets of data, the result of some processing or often both. Data sources are the suppliers of data. They may be internal or external suppliers but require management regardless. Data sources can have planned and unplanned outages, changes in format, frequency, latency, licence or rights that could affect the use, value or risk of the data. Different data sources have different attributes in terms of rights, licences, availability and data latency, the latter being particularly important for real time data feeds. Sourcing data typically has an ongoing cost, whether charged by external suppliers or incurred internally. Understanding these costs is critical for meaningful cost attribution. Managing and cataloguing data sources avoids duplication and improves the efficiency of data acquisition. Sinks Just as a good understanding of the sources of data is a critical element of data governance, so too is understanding data sinks. Occasionally data is stored without further use for regulatory or risk mitigation purposes or the data may be used in situ. Often the data is passed to a destination or sink for further processing, analysis or retrieval. The data sinks of a data store may constitute part or all of the purpose of the data store. The sinks will require specific data with specific age or currency, rights and accuracy. Management of the store and management of the sources must be aligned to the requirements of the sinks. Data may also need to be correlated with data from other sources. Rights and licences All data has associated rights, either owned by the organisation or by a third party, with open or restricted usage rights and associated licences. A contract may have been entered into to acquire or make use of the data. In a large number of cases, the rights and licence are restrictive and legally binding. Data, particularly personal data, is also subject to specific legislation depending on the locality. Breach of the rights, licences or legislation can result in significant penalties for an organisation and its officers. It is therefore essential that all data acquired and stored has its rights and licences recorded appropriately. These records should be kept up to date and reviewed regularly. Rights expire and licences often have end dates. It should not be assumed that data can be used just because it is accessible. The rights, licences and legislation can also be restrictive in how it is used. Personal data collected for one purpose will not be generally useable for other purposes under GDPR1. Organisations have a responsibility to understand and comply with the regulatory and commercial restrictions of data. The data owner is responsible for this compliance. Management Data management is necessary to support the owner in discharging all their responsibilities. The management of data encompasses a number of important activities including technology selection and maintenance, storage, administration service and delivery continuity. Data management also includes the removal and/or archiving of old data from the store. Appropriate technology is essential in meeting the access needs of sources, sinks and users and allowing the platform and data to be administered. The technology needs to support the required level of service including availability and redundancy ensuring that critical data assets are available and not lost in the event of a failure. The data needs to be accessible to be useful and the technology needs to ensure users with appropriate permissions can find and obtain the data they need easily. The technology is also used in all aspects of data maintenance up to and including deletion. Cost Data has storage, management and governance costs that should be aligned with its value. There are direct costs associated with physical storage, backups, management and networking costs for data movement. In addition, there are costs associated with the oversight, management and governance of the data as an asset and a liability for the organisation. There are also licence costs which can be substantial in addition to the cost of physically acquiring, cleansing and managing the data. Finally, there is often some relatively small cost in disposing of data. It should also be remembered that the organisation carries a risk of additional costs in the unlikely event of breach of licence or legislation. It is important for an organisation through the data owner to understand the full cost of data from acquisition through to disposal together with risk exposure all of which should be offset against the value of the data to the organisation to fully understand the return on investment. Quality Data quality is critical to any organisation. Its quality has a direct impact on the decisions made based on it, the standard of the AI models built on it, and the overall value of the products using it. Data governance focuses on two key aspects of data quality: measurement and improvement. Measurement is important as it is the basis for detecting changes in quality and identifying when data quality could cause issues in use. It may be more appropriate to take a data service offline than to deliver low quality data. Measuring quality is also useful in holding sources and suppliers to account for the quality of delivered data. Data quality issues include currency, gaps and erroneous values. Sometimes the currency, gaps or erroneous values are obvious, sometimes they are not. These defects may be addressable through cleansing. WHY IS DATA GOVERNANCE A CRITICAL ISSUE FOR ORGANISATIONS? Data is a significant asset and liability for organisations. Data governance is critical for all organisations to ensure decisions about data are appropriately made and should be an integral part of a data strategy. Frameworks provide a structure for data governance and should include essential elements above. The purpose and roadmap of the data and an accountable owner are central to any approach which should also cover sources, sinks, rights and licences, management, cost and quality. Only by explicitly addressing each of these areas can assurance be given to accountable executives, the board and other stakeholders of an organisation. 1 GDPR: General Data Protection Regulation is a regulation for data protection and privacy in the European Union and European Economic Area.

  • EVALUATING CLOUD COMPUTING: COST, AGILITY AND UNIQUE CAPABILITIES

    Why should organisations be using the cloud over on-premise hosting? Many organisations are facing unexpectedly high costs in the cloud today and some organisations have made the strategic choice to retrench back to on-premise data centres. If cloud is really better than on-premise, why are some companies choosing to leave or pursue hybrid approaches? COST BOMBS When organisations first started to adopt cloud, it was naturally infrastructure teams that took up the challenge. These teams had a long history of understanding, specifying and ordering and commissioning hardware on-premise. Hardware delivery takes weeks and it makes little sense to tune the ordering and commissioning processes to reduce latency. These same, relatively slow processes, were used to deliver cloud assets masking one of the cloud’s raison d'etre, the near instant provisioning and deprovisioning of assets. The assets considered were typically Infrastructure as a Service (IaaS), that is network, storage, virtual machines and later containers. The infrastructure teams had less experience metering and delivering other capabilities and the result was organisations typically received an “on-premise in the cloud” experience. In response to this, engineering or development teams were sometimes given and sometimes they took the role of cloud management. Developers were naturally quick to embrace software defined infrastructure and proceeded to unlock a good number of benefits of cloud. However, up until this point developers had always worked in a hardware constrained environment. Some determination of the necessary on-premise hardware had been made early in a project and the software was written and tuned to fit that constraint. In selecting the necessary hardware a formal or informal process ensured that the cost matched in some way, the value of the solution. With cloud, developers were no longer constrained, every log message could be stored, new instances could be brought online and more memory was just a click away. This resulted in the cloud cost bomb that organisations are trying to defuse today. According to the 2022 State of Cloud Cost Intelligence report, “only 3 out of 10 organisations know exactly where their [cloud] spend is going” *. Those that do tend to find it being spent on inefficient or wasteful solutions. It is this cost bomb that is driving many organisations to reevaluate their cloud strategy in favour of building on-premise solutions. However, it is bad execution which is creating the high costs not the strategy. In most organisations, the strategy to move to cloud is driven by cost, agility and increasingly, the unique capabilities of cloud. PAY ONLY FOR WHAT YOU USE The cloud's lower cost potential comes from only paying for what you use. This requires a change in the way organisations think about, and use IT capabilities. For compute resources, you only need peak capacity at peak demand (or maybe slightly before). If a solution can be built to scale up and down effectively with demand there are significant cost savings available. Additionally, environments that are not required full-time, such as development or test rigs, can be turned off up to three quarters of the week. The dynamic capacity argument also bears fruit in storage. On-premise, typically storage is budgeted, purchased and provisioned for the year. Anticipated storage needs for the year are purchased up front. The cloud can be cheaper in three ways: 1. Storage is only rented when it is required and not months in advance, saving money*. 2. Storage is charged for usage not capacity i.e. you don’t pay for free disk space on a drive. 3. If the storage is no longer needed, it is no longer rented. Again, the dynamic capacity extends to the network. On-premise, you size your internet connection requirement based on anticipated need and then enter a multi-year deal where you typically under utilise capacity in the early years and are constrained in the latter years. In the cloud, you pay for what is used, not for what might be provisioned. It is extremely hard to compare cloud and on-premise costs. The on-premise solution benefits from an unattributed share of the data centre(s) costs including: Capital costs: building acquisition, power supply, initial network connection, cooling and fire suppression installation; Operating Expenses: ongoing power consumption, internet network connection, security personnel, building maintenance and equipment-related costs. Network costs: Includes costs for internal and external networking, cabling and network staff. Hardware management: server setup, commissioning, testing, patching, upgrades, decommissioning, cleaning and disposal. Support and maintenance: Expenses such as after-hours support, replacement equipment, servers and storage, software licences and their management. The cloud always includes capabilities that may not be required such as data being duplicated, networks are segregated and redundant, logging always built in and so on. Cloud costs are more complete and more easily tracked and attributed to different projects, solutions and services. This clearer view of spending and return on investment equips the organisation to make better decisions. AGILITY Agility is the ability to react easily to changing organisational demands to seize new opportunities or resolve emerging problems. Paying only for what you use enables agility and encourages the organisation to try things with no ongoing commitment, unlock learning through experimentation, rapidly iterate solutions and test ideas for better decision making. The flexibility of cloud computing fully supports agile development where spikes of activity with specific hardware can be supported and changes of direction don’t carry a cost overhead. Cloud flexibility also enables compute operations that are not practical on-premise. For example, an entire solution could be temporarily replicated to investigate a subtle issue or examine a “what if” question. Large amounts of training data could be used for a one-off training of an AI model. Large amounts of data could be rapidly migrated using many machines in parallel. A full scale performance test could be run on an entire solution. In each case, these needs could be easily satisfied by provisioning cloud resources which are released at the end of the work. These transient projects have significant compute needs and are time criticality and are just not practical on-premise. The agility unlocked by this capability is significant, speeding up work, encouraging progress, reducing risk and improving information. The flexibility also ensures that the right tool is employed for each problem rather than accepting the challenge of bringing together a collection of disparate “spare” kit. The servers are always the current generation with enough memory, accelerator cards for AI are available when required, the network is segregated and low latency and near infinite storage is instantly accessible. The specific technology can be chosen for the specific problem without the constraint of existing licences or prior purchases. If another technology choice turns out to be better the organisation can simply migrate with minimal cost from the original choice. This again promotes action by reducing the penalties of wrong decisions. The rise of the dominant cloud providers has led to a standard set of Azure, AWS and GCP skills in the market. Skilled individuals joining the organisation on a temporary or permanent basis have less to get up to speed on. They are already familiar with the infrastructure, administration and other key capabilities in the cloud. This improves productivity and raises the quality of solutions as individuals bring best practice from other organisations. CAPABILITY Whilst cost and agility have been significant drivers from the inception of the cloud the unique capabilities of the cloud are becoming increasingly compelling. This includes normal engineering capabilities synonymous with cloud computing like databases and LLMs. It also includes the often overlooked built-in capabilities such as remote administration, monitoring and logging, security tooling and geographically diverse hosting. It is no longer common for solutions to be built only using physical or virtual machines. Modern solutions require a plethora of managed capabilities from relation to graph databases, from directory services to content delivery networks, from AI services to API management and more. These managed functionalities play a crucial role in solution engineering by simplifying and speeding up the development process. They abstract the complexity and drudgery of installing and maintaining the infrastructure underpinning a capability allowing delivery teams to be productive quickly. Cloud computing offers the ability for solutions to connect to niche services such as mobile text, mobile notification services or AI Large Language Models (LLMs) with minimal effort. These services are sometimes only available in the cloud and often the most up-to-date offerings available. While these services can be accessed from on-premise there is both a latency and cost penalty. The cloud is likely to continue to be the focus of compelling future capabilities as cloud providers vie to attract users to their platform. Administration capabilities are at the core of the value add of cloud providers. Remote administration, logging, monitoring and instrumentation makes infrastructure, platform, and software as a service possible. During the Covid-19 pandemic, organisations continued to manage their cloud assets from home without a second thought. Today it supports distributed teams and external collaborators from anywhere in the world. Cloud providers offer a shared security model where the application builder is responsible for a portion of the security. Additionally, the cloud provider secures the physical infrastructure and, depending on the service, the operating system and platform software. Network segregation and sensible defaults promote secure by default deployments. Tools are provided that support both proactive and reactive security monitoring. Few organisations can hope to do better than outsourcing a defined portion of their security responsibilities to cloud providers. The cloud also offers the ability to host all of these capabilities in the widest range of geographic locations. This supports the most extreme service continuity geography requirements, allows network latency to critical users to be minimised and data sovereignty restrictions to be satisfied. All these functionalities improve productivity, enhance workforce flexibility and remove the need to purchase and manage alternative services. In other words, they also represent a cost saving. At its core, the cloud enables organisations to build solutions that are just not practical or in some cases possible on-premise. Many organisations are facing high cloud costs and believing a cloud strategy is flawed are returning on-premise, to some extent at least. However, cloud strategies driven by cost, agility and capability are well grounded. Costs are hard to compare with many on-premise costs hidden and absorbed rather than attributed to projects and solutions. Paying only for what you use in the cloud offers the potential for large scale removal of cost. This pay per use model is at the heart of the agility of cloud computing. The flexibility allows organisations to do things that simply are not practical or even possible on-premise. The cloud also ensures that the optimum solution components are available for use with abundant skills in the market to move quickly. This cloud agility encourages organisations to iterate and experiment gaining insights leading to earlier and better decision making. However, it is increasing unique cloud capabilities that drive organisations from on-premise to the cloud. A wide range of management/administration, solution and security capabilities that can be deployed anywhere in the world enable organisations to deliver for their customers. Those that can make the move encounter a world of convenience supporting incredible flexibility, access, visibility and delegated access. This world reduces complexity and noise for an organisation allowing it to focus on their customers and differentiating aspects of their organisation. That is, after all the point, there are more important things to do than run an environment for IT. While there are many advantages to the cloud, perhaps the most important is simply “someone else is taking care of it”. Organisations are freed up to focus their time and energy on their customers and stakeholders.

  • BOOST YOUR PROJECTS: BENEFITS OF LATAM'S OFFSHORE SOFTWARE ENGINEERS

    In the rapidly evolving landscape of digital innovation, software engineers are the architects of transformation. If you are looking to expand your software development capabilities, look no further than the talent-rich regions of Latin America. This article highlights Pluralit's role as your gateway to this exceptional expertise and explores the many benefits of using offshore software engineers. UNLEASHING LATIN AMERICAN EXPERTISE FOR GLOBAL IMPACT 🌎 While California's Silicon Valley has traditionally held the reins of global technology innovation, a changing landscape is challenging its dominance. The emergence of tech hubs worldwide is reshaping the industry, and one region that's making significant strides is Latin America. This dynamic landscape is home to several emerging tech startups and industry giants such as Rappi, Mercado Libre and Nubank, who are collectively redefining the tech narrative. Latin America's tech ecosystem is experiencing exponential growth, positioning itself as the preferred destination for global tech leaders to invest in local startups. During this transformation, Latin America is shining as a thriving reservoir of software engineering talent, offering a diverse pool of skills and experience. From Argentina to Mexico and Brazil, the region has nurtured a vibrant community of exceptional software developers. This growing talent pool is now being used to develop new products and services. THE POWER OF OFFSHORE SOFTWARE ENGINEERS In recent years, LATAM's software development landscape has experienced remarkable growth. Remote collaboration with the US and Europe has not only bridged the economic gap in the LATAM countries but has also created numerous high-paying opportunities by regional standards. Now the question arises: How can your business capitalise on the benefits of outsourcing to Latin America? Explore the compelling reasons to integrate offshore software engineers from this thriving region into your projects: 1. Expertise and Passion With a rich tradition of software development, Latin American professionals combine mastery with unwavering passion. By integrating offshore software engineers, you can infuse your projects with the expertise and dedication that drive innovation. Over the years, there has been a massive amount of tech investment in the region, leading to its growth and development as a region with booming tech. 2. Efficient value proposition Offshore software engineers provide an exceptional cost advantage, delivering unparalleled value without sacrificing quality. Competitive hourly rates allow companies to optimise resource allocation and prioritise overall growth. Staff augmentation is beneficial not only for start-ups but also for SMBs and large enterprises, as it reduces full-time positions and salary expenses. It also eliminates ancillary costs such as paid leave, benefits and other perks. 3. Seamless global collaboration With offshore or nearshore software engineers, borders dissolve as Latin America's time zones align with the United States. Real-time communication becomes standard, accelerating project development and minimising delays. It can be challenging to get remote work done in Asian and European countries since most have a 12 to 24-hour time difference. Most Latin American countries have only a 1 to 3-hour difference from the U.S., making communication easier. 4. Embracing cultural synergy The cultures of North, Central and South America may have diverse origins, but there are striking similarities. Over time, core values such as an unwavering work ethic and a shared belief in individual impact have endured. Pluralit's inclusive ethic aligns seamlessly with Latin American values, creating an environment where innovation and collaboration flourish. The shared affinities between Latin American and North American cultures facilitate smooth communication and cultivate a fertile ground for breakthrough ideas. NAVIGATE THE OFFSHORE SOFTWARE ENGINEERING LANDSCAPE WITH PLURALIT When you enter the world of offshore software engineering, think of Pluralit as your trusted ally. With a proven track record of connecting companies with the best offshore software engineers from Latin America, Pluralit is your gateway to unparalleled excellence. Our deep understanding of technology and culture ensures a seamless match of talent, resulting in a partnership that delivers impressive results: - Define your project vision: Clearly articulate your project objectives and requirements. Pluralit's personalised approach will ensure the selection of offshore software engineers that precisely match your mission. - Precision in partnership: Discover Pluralit's expertise in offshore software engineering. Our in-depth understanding ensures a seamless integration of talent to drive your project forward. - Cultivate Collaborative Synergy: Foster an environment of open communication and trust, guided by our inclusive values. This collaborative foundation is critical to building a thriving partnership. STRENGTHEN YOUR PROJECTS WITH PLURALIT'S OFFSHORE SOFTWARE ENGINEERS Integrating offshore software engineers from Latin America through Pluralit's pipe is a strategic way to supercharge your software development efforts. Witness the fusion of expertise, passion and inclusive culture that will transform your digital landscape. To explore this transformative opportunity further, schedule a call with us today. Let's embark on a journey to elevate your projects through the dynamic potential of offshore software development.

  • SMART INVESTMENT IN A SOFTWARE PLATFORM (SaaS)FOR YOUR BUSINESS

    Are you an entrepreneur constantly thinking about how to add value to your business? Perhaps you have the idea of an app or clever software platform that, if deployed in your company, will enhance your client experience or make your business more agile or save costs or accelerate new business. And, if this piece of tech is developed by your company (rather than bought off-the-shelf), and you hold its IP, it will most likely also increase the valuation of your business. If so, it's worth it to keep reading ⤵️ Pluralit works with many business owners and founders to create their proprietary software platform that grows the value, and valuation, of their business. We are often asked what is the right approach for a growing business to get going when starting on such a significant journey. Taking the right/smart approach, and working in stages or steps, can help maximise the value delivered to the business and its clients, whilst minimising risk and allowing for a lot of learning and optimisation along the way. We (and a lot of tech startups and scaleups) use this kind of approach. In this article, we take a strategic approach to making a smart investment in a Software Platform. By phasing your investment and adopting an agile methodology such as Scrum, you can efficiently develop and launch a software product that adds exceptional value to your business. The best part? The journey itself provides a treasure trove of customer research and invaluable insights. 1. DEFINE THE PRODUCT Before diving into the development process, it's important to clearly define your product. This stage involves fleshing out your high-level idea to a level where it can be worked on effectively. What is the product vision? Which market needs will it cover better than competitors? Which features are a must to launch? And which features, in particular, differentiate the product? If the product conception is mature, this step can take as little as a couple of weeks mainly to map it in the right ‘language’ for agile software development. Otherwise, much longer time will be needed to discuss and agree on the features that will largely define the product and answer the strategic and marketing questions. This step, if properly done, can and will save time and resources later on. Falling into the temptation of cutting product design short to start developing will almost certainly cost time and money later. A vital part of this process is to consider which parts of the software platform encapsulate the strategic value of the business, and which should therefore be designed and largely built from scratch to be unique IP. In most cases, a significant part of the platform can be built using existing commercial or open-source components. For example, there are many existing solutions for user identity management or scaleable search. Making effective use of this technology will massively reduce delivery time and cost and will ensure the platform continues to benefit from wider industry investment. 2. START SMALL, WITH A PROOF OF CONCEPT (PoC) A Proof of Concept (PoC) is a small version of your software product designed to validate your idea and test its relevance and usability. The main purpose of the PoC is often to gather market research and feedback from early users, to support a pitch to investors, or both. It will also validate any technical assumptions and allow more accurate estimates and plans to be developed. This stage should be developed quickly and cost-effectively. During this phase, you will identify a few customer journeys, develop their experience (front-end) and assess the technical feasibility in the background. Properly managed, PoC development typically takes about 8 weeks, followed by 4-5 weeks of testing and learning from friendly users (and perhaps feedback from potential investors). 3. DEVELOP A MINIMUM VIABLE PRODUCT (MVP) The MVP will focus on a key area of functionality and demonstrate the business value that the final platform will deliver. The main objective is to elicit a positive response from key stakeholders, enabling them to truly understand your vision and ensure it resonates with potential users. By focusing on a compelling user experience and core functionality, the MVP approach avoids feature overload, helps deliver a lean and successful product, and gathers valuable feedback and insights. 4. MINIMUM MARKETABLE PRODUCT (MMP) Once the MVP has been successfully validated, it's time to complete the product for its proper commercial launch. This usually involves adding additional backend and infrastructure features and functionality to ensure the right security, data protection, scalability and performance across multiple platforms, all upon having real customers. This is the smallest set of features that delivers value to customers and can be released to the market. Sometimes we also have the chance in this phase to correct identified issues or further refine the user experience. For most software or products that followed the above process, this phase should not take longer than 8 to 12 weeks for the MMP launch (R1 Commercial). From here onwards, typically the product team runs quarterly releases (R2, R3, …) to continuously evolve the product (add further client features, strengthen the platform security/scalability/interoperability, correct bugs, etc.). 5. MARKET AND MONETISATION Once you have a reasonably stable product design (no earlier than POC, no later than MVP), it's important to focus on defining the marketing and monetisation strategies for commercial launch. This includes: defining the pricing strategy for your product and derived feature pricing; creating a go-to-market plan; setting up a website (if necessary); and planning a promotion plan. Many launch failures can be traced to having the wrong pricing strategy, a poor communication plan, or both. We should give these product aspects as much thinking and energy as to the technical development if not more! A smart project starts working on pricing and marketing plan as soon as the client feature set is reasonably stable (somewhere in between POC and MVP milestones), and constantly iterates and refines as the product evolves towards an MMP to launch. More often than not we hear of entrepreneurs shortcutting this step to launch quickly, only to fail due to insufficient clarity of pricing or communication. The effective pricing and promotion of your product is essential to both its initial traction and long-term success. 6. MAINTENANCE AND SUPPORT Investing in ongoing maintenance and support is essential to ensure that your software platform or product remains reliable, bug-free, secure and up-to-date. Regular updates and bug fixes will help you provide a seamless user experience and address any issues promptly. This ongoing investment will contribute to the long-term success and growth of your SaaS platform. We are confident that by following this approach you can create business value with smart investment and modest, controlled risk. For example, we use an agile methodology such as Scrum to ensure a streamlined and flexible development process. Scrum enables rapid prototyping and delivery, with client checkpoints every couple of weeks, in a highly- collaborative team with the ability to respond quickly to change. With self-organising teams and short sprints (typically 2 weeks long), Scrum empowers your development team to incrementally deliver high-quality, working software, and gives the client peace of mind of checking the value delivered every 2 weeks. Deciding to invest in your software platform is not an everyday decision. This recommended approach can help you take ‘small incremental steps’ (in time, money, effort) rather than one big jump. At Pluralit we help companies mature their Software Platform (SaaS) vision and, when ready, bring it to life on their behalf. If you are interested in making this move in your business and would like to learn more about how it works, we recommend you read the article "HOW TO INCREASE BUSINESS VALUE AND VALUE BY INVESTING IN PROPRIETARY TECH" Contact us to find out more about smart software investment!

  • LEGACY ENTERPRISE SYSTEMS - WE CAN HELP GET YOUR HEAD OUT OF THE SAND!

    Managing legacy systems is one of the toughest challenges faced by Enterprises. Failure to maintain ageing systems can lead to instability or security vulnerability with immediate business consequences. In the medium term, older systems which remain critical to current business processes cannot be developed quickly reducing business agility. With the emergence of AI a further problem can be added to this list. Unless an organisation has complete control of all of its data it will not be able to take full advantage of AI capabilities. Data languishing in old systems, hard to access and with many corrupt or out of date records will make effective use of AI technologies extremely hard. The challenges related to legacy systems include the fact that it may be running on old unstable and unsupported hardware, it may be written in a software language where skills are very hard to find. Finally, there maybe no one left from the original team who really understands the code. Some of these issues can be “band-aided” by a lift and shift into the cloud. At the very least businesses must ensure all systems are fully documented and have some coverage in terms of access to software engineers able to maintain the code and handle urgent fixes If the business wants to develop new features a full migration onto modern technology will be required to untangle the legacy code and take advantage of all the advanced cloud features for managing performance and security. Projects of this type need to be approached very carefully. Changes need to be made incrementally in order to avoid de-stabilising operational processes and the migration of data must be thought through in extreme detail with all future uses in mind. In order to have any chance of success a legacy programme must be visible to senior management and have their explicit support. This will enable resource tradeoffs to be made effectively and will clear the way for closing down legacy business processes which are otherwise expensive to support. Pluralit can help by providing access to experienced software engineers with experience of managing business critical systems on many legacy platforms . We can also provide guidance and leadership on deploying complex environments in both Azure and AWS and access to data engineers able to make data available for future needs. See our white paper on ‘Managing Enterprise Legacy Systems’ for further technical details and recommendations.

  • HOW TO INCREASE BUSINESS VALUE AND VALUATION BY INVESTING IN PROPRIETARY TECH

    At Pluralit, we are moved by helping businesses grow using digital tech, often through developing proprietary tech platforms. In this article, we will focus on the multiple ways we have seen that tech investments help increase business value and valuation. We will look at the drivers of value and valuation in marketing and service-oriented businesses, and then see how, in our experience, tech investments have contributed to growth in those value dimensions. TECH INVESTMENTS GROW BUSINESS VALUE AND VALUATION Marketing and service type businesses tend to be valued in one or both of two ways: 1. Revenue x Multiple 2. EBITDA x Multiple Tech investments can grow all of these factors: revenue, EBITDA and their respective multiples. The multiples are a bit more subjective though, and investors will take a view based on several factors that can include (not an exhaustive list!): - Growth rate: how fast revenues and profits are accelerating, - Scalability: how easily the business can grow, ideally without huge growth in costs, - Recurring revenue: whether there are reliable, recurring revenues such as subscriptions or retainers, - Contracts: whether future revenues are secured through contracts with clients, - Risks: investors will look at a multitude of potential risks, but also at how these are managed and mitigated, - Cost to replicate: this is another lens for valuation, where an investment (e.g. in a software platform) can inform a view of what it would cost to build the business from scratch, - Condition of code (in software / digital assets): investors may want to audit any tech assets to see if they’re well maintained, documented and extensible. Complementing that, the next drivers of value and valuation in terms of tech investment, specifically bespoke software or similar platforms, which drive businesses. 1. REVENUE GROWTH The first is whether the platform or software itself is a revenue generating entity. As an example, a gaming platform where, itself, is the product and the revenue generating part of the business. However, it could also be that the platform supports revenue growth to expand an existing business, such as developing an analytics platform that could help gain market share. 2. COST REDUCTION The second driver is obviously cost reduction, which has a big focus now with the economic challenges in many markets. The examples often to do with businesses that have started up by using external technology partners, which is a super smart and low risk way to build and scale your business. But at some point, these businesses have wanted to bring that capability internally, so they can bring those revenues and margins that they will pay to the external partner and they can increase their own margin. They can start to integrate capabilities within their own business to increase operating efficiency. And finally, by being able to bring those capabilities in and having your own software, there are possibilities to automate that part of the operation as well. 3. DIFFERENTIATION There is a big search for how we can really stand out from the crowd, particularly among marketing businesses. And that is where having control of your own roadmap, of your own features that you are prioritising for your business really makes a difference, especially, again, if you were relying on external partners. Those can be good partnerships. But often we have met businesses that want to drive their own features, a roadmap faster than the external partner can help them with or to take a different route that is tightly aligned to their market opportunities and their client needs. 4. ASSET CREATION Several of our clients wanted to create an asset that sits on the balance sheet of the business and really becomes something tangible within the business. The motivation is often not just creating something valuable that can be carried within the business, but also something which captures some of the business know-how in terms of intellectual property. That is important for investors because it reduces risk - it means that a lot of the functioning of the business will carry on, for example, even if key people such as the founders depart from that business at some point in the future. It also means that the business has a certain autonomy and that the know-how is codified and not just in the minds of people. 5. BUSINESS MODEL We have seen that having a technology platform will often allow businesses to scale to serve more customers and more markets while their cost base will only increase marginally - with the same exponential growth potential as famous Silicon Valley examples like Uber, Netflix and similar tech platforms businesses. As a first value-creating step towards this, we often see clients building a platform that their internal teams can use to boost sales, for example, or a platform that their clients can access directly to self-serve (e.g. to access campaign analytics or other dashboards) and can be a tool in expanding their businesses by driving operating efficiencies. Ultimately, there is often a realistic pathway toward a software-as-a-service (SaaS) model with recurring revenues. With this knowledge, Pluralit delivered a scalable AdTech asset to Mobsta that grows its value. Mobsta specialises in helping major brands design and activate location-based advertising campaigns. They wanted to have more independence from external partners and bring some of those capabilities in-house so that they could deliver a unique set of insights designed specifically for their client base. We can also help increase your company value and valuation with technology!

  • WOMEN IN TECH: LOOKING FOR AN INCLUSIVE AND BALANCED ENVIRONMENT

    Women have had an impact on technology breakthroughs for a very long time. Ada Lovelace is often recognised as the “world’s first computer programmer” for her work in the 1850s that inspired Alan Turing, almost a century later, to invent the computer. Hedy Lamarr patented in 1942 a frequency-hopping idea that inspired well-known technology used by all of us today: Wi-Fi, GPS and Bluetooth. The first batch of computer programmers appeared during WWII, all women. A decade later, Grace Hopper led the team that developed COBOL programming language (and recorded the first ever real computer bug!). They bring different perspectives to ideas that enrich our lives, projects and companies nowadays. Despite all this, only 26% of the tech workforce is female. This shows that significant challenges remain in representation and advancement of women in tech companies. Studies show that diverse teams outperform on both creativity and productivity. This means companies that value diversity are more likely to attract top talent. Therefore we can see how the industry would benefit from an increased representation of women in its teams. And recent changes in work patterns further increase access and flexibility. AT PLURALIT, INCLUSIVE IS OUR IDENTITY At Pluralit we believe that an open and inclusive work environment is essential to the wellbeing and engagement of our people, as well as innovation and growth. From day one we have been committed to building an inclusive and accommodating environment where everyone can contribute, develop and explore to reach their full potential. Our first social responsibility initiative, “Connecting Women”, aimed to incorporate women from all walks of life into the tech industry. And through our work, we aim to give women opportunities to enter and advance in the technology sector, both as IT specialists and across other company roles like legal, comms and finance. Women are also central to Pluralit through our young history: 2 of our co-founders are women and women make up large parts of our sales, talent and staff teams. In addition, each of these teams is respectively run by a woman. Today, 41% of our overall collaborators are women, amongst 71% hold non-technical roles. However, only 17% of our tech specialists are women. This means our next challenge is to entice more women to look at our tech roles and relocation opportunities. In this line we are delighted with Bruna and Amanda, our pioneering female tech consultants recently relocated to Belgium. “Being a woman in tech can be challenging since most tech and IT professionals are men. Yet I can see how this is rapidly changing and, each day, we have a more equal workspace and then more women working in tech. I am a Marketing graduate that never imagined working with technology. Looking back, jumping into Tech was one of my best life decisions - for instance it provided the opportunity to fulfil my dream and experience life abroad in Europe. I am extremely happy with my job and the projects that I am working on are challenging and fun” said Bruna Damião, Salesforce Business Analyst at Pluralit, coming out of a career in Marketing and deciding to go into tech. Nanci Abarca, Software Developer at Pluralit, got into this world following her dream and found exactly what she was looking for: “One aspect of working in tech that I especially like is that I can leverage my natural curiosity and passion for learning, since in this industry is fundamental to stay up-to-date and constantly adapt to new tools and techniques. Thus far, I've had an excellent experience at work in Europe. I'm part of a multicultural team of smart and diverse individuals, and we are working on an engaging project that keeps me motivated. I'm grateful for the chance to work in such an enriching environment”. Our goal is to support women at every stage of their careers – from those who are just getting started to those who are well established. We constantly seek for a balanced environment with tailored positions for each candidate and work-life balance. We are focused on creating opportunities for everyone regardless of gender, race, ethnicity or background. Our commitment with diversity and equality is an essential part of the Pluralit vision and values and we hope to inspire other technology companies to do the same. Interested to know more about our company and how to become one of us?

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