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Businesses building software products often need additional development capacity, specialized technical skills, or faster delivery timelines. Two common ways companies expand their development capabilities are through a dedicated development team and staff augmentation. While both models allow organizations to work with external developers, they operate very differently. Understanding how each approach works can help businesses choose the right model for their project, team structure, and long term development goals.
What Is a Dedicated Development Team
A dedicated development team is a group of software professionals assigned exclusively to work on a specific client project. The team usually includes developers, designers, quality assurance engineers, and project management support who collaborate together throughout the development lifecycle. Instead of hiring developers individually, companies gain access to a full team that works as an extension of their internal organization. The team focuses entirely on the client’s product or platform, allowing developers to build strong familiarity with the project and its technical requirements.
Businesses that require long term development support often choose to work with a dedicated software development team because it provides consistent resources, deeper collaboration, and better long term knowledge of the product.
What Is Staff Augmentation
Staff augmentation is a hiring model where companies temporarily add external developers to their existing internal development team. These developers typically fill specific skill gaps or help increase development capacity during periods of high workload.
Unlike the dedicated team model, staff augmentation focuses on individual contributors rather than a complete team structure. The external developers work under the client’s management and follow the same internal processes as the rest of the organization’s engineering team.
This approach is often used when a company already has an established development department but needs additional developers with specialized expertise or short term support.
Key Differences Between Dedicated Development Teams and Staff Augmentation
The primary difference between these models lies in how the development resources are structured and managed. A dedicated development team provides a fully organized group of specialists working together on a project. The development partner handles recruitment, team composition, and operational management while the client focuses on product strategy and priorities. Staff augmentation, on the other hand, simply adds individual developers to an existing team. The client remains responsible for managing the augmented developers, assigning tasks, and overseeing their daily work. Another key difference is project continuity. Dedicated teams typically remain involved with a project for a longer period of time, which allows them to develop a deeper understanding of the product. Staff augmentation is often used for shorter engagements or to address temporary development needs.
Advantages of a Dedicated Development Team
A dedicated development team offers several advantages for organizations building complex software products or planning long term development initiatives. Because the entire team works exclusively on a single project, developers gain deeper familiarity with the software architecture, technical requirements, and business objectives. This continuity often improves productivity and reduces the time required to onboard new developers.
Another advantage is scalability. Companies can expand or adjust the team size as the project evolves without going through lengthy hiring processes. Many businesses also prefer the dedicated team model because it reduces administrative overhead. The development partner handles recruitment, payroll, and team management, allowing the client to focus on product strategy and innovation. Organizations that require long term collaboration and stable development resources often choose a dedicated development team to support ongoing software development.
Advantages of Staff Augmentation
Staff augmentation offers a flexible way for companies to quickly add technical expertise to an existing development team. This model works particularly well when organizations already have experienced engineering leadership and project management in place. By adding external developers with specific technical skills, companies can accelerate development without restructuring their existing team. Staff augmentation also allows businesses to scale resources temporarily. For example, a company may bring in additional developers to complete a specific feature, implement a new technology, or handle increased workload during a critical development phase. Once the project demand decreases, the augmented developers can transition off the project without long term commitments.
When to Choose a Dedicated Development Team
The dedicated team model is often the best option for projects that require long term development and continuous collaboration. Startups frequently use dedicated development teams when building new software products because the model provides a complete engineering team without the cost of hiring internally. Growing companies also benefit from dedicated teams when expanding their platforms or launching new digital services. Organizations with limited internal development resources may find that working with a dedicated software development team allows them to build complex solutions while maintaining consistent technical expertise throughout the project.
When Staff Augmentation Is the Better Choice
Staff augmentation is typically the better choice for companies that already have a strong internal development structure but need additional resources. If a company requires developers with specialized expertise such as cloud architecture, machine learning, or a specific programming framework, staff augmentation can provide that expertise quickly. This model is also useful for short term development needs, temporary workload increases, or situations where hiring full time developers is not practical.
Conclusion
Both dedicated development teams and staff augmentation provide businesses with flexible ways to expand their software development capabilities. The best choice depends on the structure of the organization, the complexity of the project, and the level of collaboration required. A dedicated development team provides a complete group of specialists focused on a single project, making it an excellent choice for long term development and complex software initiatives. Staff augmentation, on the other hand, allows companies to temporarily add individual developers to their existing team when specific skills or additional capacity are needed. By understanding the differences between these models, businesses can choose the approach that best supports their development goals and helps them deliver high quality software efficiently.
Many companies need skilled developers to build software products, web applications, or digital platforms, but hiring and maintaining an internal development department is not always practical. Recruiting developers, managing infrastructure, and maintaining a technical team can require significant time and resources.
A dedicated software development team offers an alternative model where businesses partner with a development company to work with a group of developers who focus entirely on their project. This approach allows companies to access experienced engineering talent while maintaining flexibility and control over the development process. Organizations that need long term development support often choose to work with a dedicated software development team to accelerate product development while avoiding the cost and complexity of building an internal engineering department.
Understanding the Dedicated Development Team Model
A dedicated software development team is a group of software professionals assigned to work exclusively on a client’s project for an extended period of time. The team is typically provided by a software development company but operates as an extension of the client’s internal team. Instead of working on multiple unrelated projects, the developers focus on a single product or platform. This allows them to develop a deeper understanding of the software, the technical requirements, and the long term goals of the project.
The client remains closely involved in the development process by setting priorities, defining features, and guiding product direction. Meanwhile, the development partner manages hiring, team organization, and operational support. Businesses that need this level of collaboration often rely on dedicated development team services to ensure they have consistent engineering support throughout the entire product lifecycle.
How Dedicated Development Teams Integrate With Your Business
One of the defining features of the dedicated team model is how closely it integrates with a company’s existing workflow. Rather than functioning as a separate vendor, the development team collaborates directly with internal stakeholders. Developers participate in planning sessions, sprint reviews, and ongoing product discussions. Communication typically happens through modern collaboration tools such as project management platforms, version control systems, and team messaging applications.
Because the same team remains assigned to the project, knowledge of the software grows over time. This continuity allows the team to improve performance, identify potential improvements, and maintain consistent development standards. Companies that need long term technical collaboration often work with providers offering dedicated software development team services to maintain steady progress across development cycles.
Key Specialists in a Dedicated Development Team
Dedicated development teams usually consist of multiple specialists who work together to build and maintain software systems. Software developers handle the technical implementation of features and system functionality. Frontend developers focus on the visual and interactive elements users see, while backend developers build the server side infrastructure that powers the application.
Design specialists are responsible for creating user friendly interfaces and ensuring that the product delivers a clear and intuitive experience. Quality assurance engineers test the software throughout development to identify bugs, security risks, and performance issues. Many teams also include DevOps specialists who manage deployment pipelines and infrastructure, as well as project managers who coordinate tasks, timelines, and communication across the team. The structure of the team can change depending on the complexity of the project and the technologies involved.
Why Companies Use Dedicated Development Teams
Companies choose dedicated development teams primarily because the model provides flexibility and long term development capacity. Instead of going through lengthy hiring cycles, businesses can quickly assemble a team with the required technical skills. This allows projects to begin sooner and development to move forward without delays. Dedicated teams also provide stability for long term projects. Because the same developers remain involved throughout the product lifecycle, they build deep familiarity with the software and its architecture. This knowledge improves efficiency and helps prevent many of the issues that arise when development teams frequently change. For companies looking to scale development quickly, partnering with a provider that offers a dedicated software development team can provide access to experienced developers without the challenges of internal recruitment.
Dedicated Teams Compared With Other Development Models
Businesses exploring software development options often evaluate several different engagement models. An in house development team involves hiring employees directly and managing the entire development department internally. While this provides maximum control, it can be expensive and difficult to scale quickly.
Staff augmentation focuses on adding individual developers to an existing internal team. This approach works well when companies already have a strong internal structure but need additional expertise or short term support. Traditional project outsourcing transfers responsibility for the entire development project to an external vendor. While this can reduce internal workload, it may also reduce direct involvement in product decisions. The dedicated development team model offers a balance between these approaches by providing a complete team that works closely with the client while remaining flexible and scalable.
When the Dedicated Team Model Makes the Most Sense
Dedicated development teams are particularly effective for projects that require continuous development over an extended period of time. Startups often use this model when launching new digital products because it allows them to build and refine software without committing to long term internal hiring. Growing companies may use dedicated teams to expand their technical capabilities or accelerate development timelines. Large organizations frequently adopt the model when building complex enterprise systems or when scaling development efforts across multiple products.
Choosing the Right Development Partner
Selecting the right development partner is an important factor in the success of a dedicated team arrangement. Companies should evaluate potential partners based on their technical expertise, communication processes, and experience working on similar projects. A strong development partner will provide transparency throughout the development lifecycle, maintain clear documentation, and follow established development methodologies such as agile or iterative development. Reviewing previous work, case studies, and client feedback can also provide valuable insight into how effectively a team delivers software projects.
Conclusion
A dedicated software development team provides businesses with a flexible way to build and maintain software products while accessing experienced engineering talent. By working with a team that focuses exclusively on a project, companies can accelerate development, maintain consistent product knowledge, and scale their technical resources as needed. Businesses that need reliable engineering support for long term projects often benefit from working with a dedicated software development team that can integrate directly into their development workflow and help bring software products to market faster.
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Original Post : Smarten Support Portal Updates – March – 2026!
Minimum Viable Products Are Key to Start-Up Success
Your business has a great idea for a software application or solution. If your enterprise is a start-up with a money-making concept, or you have an existing app and you want to make it more competitive, it is imperative that you take the most direct, effective route to product launch.
Microsoft Estimated that there were more than 50 million start-ups launched in 2022 alone. According to Crunchbase data and a CB Insights report, global venture funding declined 35% year over year in 2022. What does this mean? This decline in funding reflects a correction by venture capitalists and investors who no longer wish to bet on a unicorn.
SO…if you still think you have a great idea, and you want to fund a product or an upgrade, you MUST do so with a cost-effective approach; one that will provide the best chance of success with the least investment.
That’s where the Minimum Viable Product (MVP) concept comes in!
By engaging an expert in Minimum Viable Products, your start-up can plan for a product test to test theories and create features for feedback from users and customers without the extended cycle and expense of full product development, thereby ensuring success without betting all of your funding on a product that is not suitable for the market or the users you wish to target.
- Strategy – Identify core product features that will deliver the most value for the largest user audience
- Market – Define the market need and the target user or customer
- Metrics – Establish metrics for success and a feedback and evaluation loop to gather and process data for the final product
- Implementation and Roll-Out – Establish a scalable, workable plan for implementing the test strategy
- Involve Stakeholders – Align goals and strategy and plan for reporting and stakeholder engagement
- Alignment – Assure that the MVP features align with business value, revenue growth and the goals of the organization
- Quality – Assure quality and security of MVP product
- Define Success – Work with the team and stakeholders to define success and customer or user acceptance criteria
- Incorporate Feedback – Establish feedback loops to ensure continuous improvement, and methods to incorporate appropriate features in final product
The examples provided above will give you an idea of the components and considerations included within a Minimum Viable Product (MVP) strategy. The MVP process provides swift, affordable market feedback and builds a process of iterative improvement to speed and enhance product development.
Expert MVP services include rapid prototyping, user testing, and iterative development to help you prove your idea’s potential before large scale investment. Be sure that the partner you choose has proven experience in MVP development.
Contact Us to find out more about our Minimum Viable Product (MVP) expert services. Our minimum viable product software development process focuses on identifying essential features, crafting an intuitive user experience, and delivering a functional prototype that investors and users can experience. Whether you need MVP development for startups or tailored enterprise solutions, we make sure your product stands out from the start. Review our MVP Case Studies: Recruitment Industry Start-Up Sourcing Platform, and Integrated Web and Mobile App for Holistic Healthcare.
Original Post : How Can Minimum Viable Products Help My Start-Up Business!
How Predictive Tools Empower Non-Technical Users
Many organizations collect large amounts of information but struggle to turn it into decisions that feel clear and reliable. Smarten approaches this problem from a human POV. Instead of assuming Analytics belongs to specialists, it treats prediction as a shared responsibility across teams. This shift changes how people think, how they ask questions, and how decisions are made inside organizations.
Read as I talk about the power Non-Technical Users hold in business decision-making and how tools like Smarten pave the way for future-ready analytics workflows.
The Potential of the Citizen Data Scientist Approach and Augmented Analytics
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Why Non-Technical Users Need Predictive Tools Just as Much
I have encountered several instances where business users created Predictive Models without a formal background in data science. These users were not guessing or experimenting blindly. They were applying years of domain experience through a system that respected how they already think. The right predictive tools guided them step by step, allowing them to focus on meaning rather than mechanics.
What stood out most in these stories was how quickly confidence developed. When people understand what a model is doing, they are more willing to rely on it and improve it. Users did not wait for validation from technical teams before acting. They could see how inputs affected outcomes and why certain patterns appeared. This visibility removed fear and hesitation.
Modern CDS Tools create that structure by guiding decisions without dictating them. This balance allows non-technical users to succeed without feeling overwhelmed or constrained.
Why Explainability Is Not Optional
Predictive models fail when people cannot explain them to others. A result that cannot be explained cannot be defended, trusted, or improved. When explainability becomes a requirement and not an extra feature, every outcome gets connected to visible drivers that users can understand in simple language. This clarity changes how people interact with predictions.
When explainability is built in, conversations improve across teams. Sales, finance, and operations can discuss the same model without confusion. People focus on what changed and why it matters. Meetings become more productive because participants share understanding instead of debating definitions, which reduces friction and speeds up decision-making.
Explainability also protects organizations from silent mistakes. When assumptions are visible, they can be questioned early. Citizen data scientists can think critically rather than accept results without reflection, creating a culture where Analytics supports thinking instead of replacing it.
How CDS Bridges the Skill Gap
Many analytics tools expect Non-Technical Users to adapt to complex systems. CDS tools take the opposite approach by adapting the system to the user. They democratize data, allowing organizations to combine analysis with the professional knowledge and domain skills of the individual. This enables a better understanding of trends, patterns, issues, and opportunities, and improves business agility and efficiency in the long run.
Here’s how CDS bridges the skills gap:
- Designed around familiar business steps: CDS tools are designed around familiar business steps that feel logical and intuitive; users are guided through the modeling process without needing to learn new technical concepts.
- Provides context at every step: CDS recognizes that skill gaps are simply differences in training and focus. It bridges those gaps by providing context at every step. Users know what they are doing and why it matters. This understanding helps them make better choices and avoid common mistakes.
- Keeps workflows clear and structured: CDS allows users to build reliable models without shortcuts. Validation checks help users see weaknesses early without discouraging exploration. This balance encourages learning while maintaining responsibility. Over time, users grow more capable and confident in their analytical thinking.
What Future-Ready Analytics Actually Looks Like
Future-ready analytics is not about complexity or volume but about flexibility, clarity, and learning. Today’s users expect models to change as conditions change, and that’s what modern CDS tools like Smarten do. They help decision makers stay connected to data and focus on understanding direction and impact.
Smarten CDS gradually integrates into existing processes. It empowers business users to take responsibility for insights instead of waiting for reports, while allowing analysts to focus on deeper problems instead of routine requests. This redistribution of effort increases overall capacity without increasing headcount.
Over time, data becomes a shared resource rather than a specialized asset. Decisions feel deliberate because people understand the reasoning behind them. Confidence grows from clarity, and organizations can act decisively even when outcomes are uncertain.
FAQs
1. What does Smarten CDS help users do?
Smarten CDS helps business users build and understand predictive models without needing technical skills.
2. Why is explainability important?
Explainability allows users to trust results, defend decisions, and improve models over time.
3. How does CDS bridge skill gaps between business users and advanced analytics?
CDS bridges skill gaps by guiding business users through predictive modeling using familiar steps, clear explanations, and built-in validation, allowing them to apply domain knowledge without needing technical training.
What’s the Difference Between Traditional BI and Augmented Analytics?
Whether you are considering a first-time implementation of business intelligence (BI) software, or you wish to upgrade your existing installation to include new features and technology, the market for BI tools and augmented analytics solutions can be confusing. Adding to the confusion are the seemingly conflicting predictions of research firms.
Gartner Predicted that, ‘Transitioning from a data-driven to a decision-centric vision is crucial. Prioritizing urgent business decisions for modeling, aligning decision intelligence (DI) practices, and evaluating DI platforms are recommended steps.’
So, will data-driven reporting, simple metrics and alerts be enough to drive your business into the future? Should your organization select a traditional BI tools, a ‘modern data quality solution,’ or review and select multiple analytics solutions to meet varying requirements across the enterprise?
‘Every enterprise has different needs, a different culture and varying business user, IT and data scientist requirements. As you review the capabilities of Traditional BI and Augmented Analytics, analyze the suite of services and functionality and compare those to the needs of your users and your organization.’
To help you answer these questions, it is important to first understand the difference between traditional Business Intelligence and Augmented Analytics.
Traditional BI Tools
Traditional BI tools support power business users with self-serve report creation without the need for advanced technical skills, SQL skills or IT assistance. To improve accuracy and outcomes, enterprise business users who are working on day-to-day projects and activities need access to easy-to-use, intuitive tools that allow for swift, accurate action and decisions. With an integrated, mobile approach to BI tools, business users can leverage personalized dashboards, multidimensional key performance indicators, and KPI tools, report software, Crosstab & Tabular reports, GeoMaps and deep dive analytics and enjoy Social BI and collaboration.
Features include:
- Personalized Dashboard
- Multidimensional Key Performance Indicators (KPIs)
- Deep-Dive Analytics
- Social BI tools for data sharing
- Graphical Analysis and Cross-Tab Analytics for Intuitive reporting
- What-If Analysis to test pricing, budget and cost information
- GeoMap support with interactive maps
- Out-of-the-Box Mobile BI tools for access from anywhere
- Personalized alerts
- Real time and cached data management
- R Integration for all with no programming experience required
- Intuitive, informative reporting
- Publishing and delivery agent
Augmented Analytics Solutions
Augmented Analytics solutions are designed with intuitive, sophisticated features that take users beyond basic BI tools to enable swift gathering and analysis of trends, patterns, opportunities and challenges. Augmented Analytics solutions include self-serve data preparation, smart data visualization, assisted predictive modeling and natural language processing (NLP) foundational search analytics so users can ask a simple question and get a simple answer without coding or queries. This type of solution includes key influencer analytics, anomaly detection, alerts, clickless analytics and other tools designed to support the transformation of business users to Citizen Data Scientists and to increase data literacy in the enterprise.
Augmented analytics that is designed with sophisticated features for use by team members, IT, data scientists and others, provides many advanced features and enables improved data literacy and data democratization across the enterprise. With products and modules that include self-serve data preparation and ETL for business users, predictive analytics with auto-suggestions and recommendations to help users choose the right predictive modeling techniques, and smart data visualization with recommendations for the type of visualization best suited for a particular type of data, the users can make choices confidently and gain insight into issues without the programming or data science knowledge needed in decades past. Embedded BI with integration APIs allows users to leverage single sign-on within familiar software and perform analytics from within that solution.
Features Include:
- Sentiment Analysis
- Assisted Predictive Modeling
- Key Influencer Analysis
- Clickless Analytics with Natural Language Processing (NLP) and easy ‘Google-type’ searches and queries
- Auto Insights and toolsets that leverage machine learning
- Anomaly Monitoring and Alerts
Features are designed to support Citizen Data Scientists and organizational transformation for Data Literacy.
‘Should your organization select a traditional BI tools, a ‘modern data quality solution,’ or review and select multiple analytics solutions to meet varying requirements across the enterprise?’
Every enterprise has different needs, a different culture and varying business user, IT and data scientist requirements. As you review the capabilities of Traditional BI and Augmented Analytics, analyze the suite of services and functionality and compare those to the needs of your users and your organization.
You may wish to implement Augmented Analytics across the enterprise and elevate data literacy and the access and use of advanced analytics across your user organization, or you may wish to focus on specific metrics, data monitoring and reporting to address unique needs or targeted goals within the organization.
Some organizations choose to leverage both the Traditional BI and the Modern BI and Augmented Analytics approach to ensure that all user and enterprise needs are addressed and avoid the one-size-fits-all approach. By giving their users, IT team, executives and business analysts the tools they need, they can align their data gathering and analytics with their strategic goals and operational objectives.
The choice is yours!
Contact Us to discuss the unique needs of your organization and your users and find out more about Smarten Technology. Explore our free article: ‘Which BI Or Analytics Tool Is Best For My Business?’
Original Post : Should My Business Choose Traditional BI or Augmented Analytics?
Creatio and Low-Code No-Code Development: What’s the Story?
Software development publications and industry journals have been talking about Low-Code and No-Code (LCNC) development for some time now. If you are wondering what this is and how it can help your business, let’s dive in and explore the world of LCNC, and one of its premier development platforms.
Let’s start with the definition of Low-Code and No-Code development:
Low-Code Development
This approach supports the development team by decreasing the amount of ‘from scratch’ coding required, and creating a foundation of reusable code with components that become building blocks for future development. This development approach allows programmers to leverage low-code user interface components roughly 80% of development tasks, thereby limiting the manual coding efforts to 20% of developer time. Because low-code application development requires a knowledge of other tools and development skills, it is typically used by professional programmers with coding skills and knowledge. A Low Code development platform allows developers to extend component libraries and web frameworks to address specific use cases. Developers can work faster and more precisely with proven tools and combine coding and programming experience with tools designed for rapid software development.
No-Code Development
The No Code approach utilizes a visual workflow in an Integrated Development Environment (IDE), eliminating the need for manual coding. This technique creates a kind of ‘snap in’ system of components that developers can use and reuse to solve problems and create features and functionality. This closed system of tools restricts the developer by limiting use to pre-existing capabilities and user interface tools. The No-Code approach can provide a start-up with quick results and foundational features and allow them to enter the market and build visibility. Now that we have a better understanding of low-code/no-code app development, let us consider how technology research firms and publications see these development techniques.
According to the world-renowned Technology Research Firm, Gartner, ‘low-code platforms will be used in 65% of application development.’
‘As with any solution or platform choice, the right fit for your organization is based on your requirements, your technology landscape, your users, your products and services and other considerations.’
A recent survey of businesses revealed several primary reasons for using the Low-Code/No-Code approach.
- Accelerate Digital Innovation and Transformation
- Reduce Current IT Backlog and Increase Responsiveness
- Reduce Dependency on Professional Technology Skills, and Increase Developer Productivity
- Protect Against Technology Churn
- Improve Time to Market
If you are considering a low-code/no-code development project, you may wish to review the capabilities of the Creatio platform. Creatio is used today in many LCNC projects and has numerous capabilities and advantages.
Creatio is a well-recognized low-code, no-code development option that can be used for many purposes and in many industries.
There are some primary reasons for selecting Creatio as your LCNC choice.
- First, the development process supports programmers with simple tools, making it easier and faster to develop components and launch an app.
- Secondly, many LCNC platforms are open-source, so they can be easily combined with other frameworks and platforms to create a comprehensive solution.
Advantages of Creatio
- For Developers – Creatio provides an intuitive, simple environment in which to build apps with templates, connectors and apps that make it easy to get started, and its design tools allow the team to track project milestones and tasks. The platform allows for easy integration of Active Directory Federation Services (ADFS), OneLogin portals, Microsoft Exchange and 365, Webitel and Google and others. Creatio also includes integration capabilities for REST API support, OData protocol, SOAP services, OAuth authentication and LDAP protocols. The Creatio support team provides responsive access for developers, and the solution can be easily customized to suit various industries and business functions.
- For the Enterprise – The Creatio environment provides the capability for customer overview from sales conversion to retention and marketing campaigns, and enables insight into planning and budgeting with analytical features to produce reports and help the business improve performance. Creatio allows the business to monitor tasks and projects and forecast outcomes, and tracks email, phone, social media and web interactions to provide a complete picture of customer interaction. Its customer relationship management (CRM) solutions include sales and customer service components and a marketing automation tool. The launch of the Creatio Energy 8.2 platform incorporates unified agentic AI, generative AI (GenAI) and Prescriptive AI in a single platform AI Command Center, making it possible for the business to incorporate AI functions and features into its product or app roadmap.
Creatio combines generative, agentic, and predictive AI into one no-code platform to enable businesses and industries to achieve market advantage, improve productivity and customer satisfaction and support business decisions and strategies.
According to the world-renowned Technology Research Firm, Gartner, ‘low-code platforms will be used in 65% of application development.’
As with any solution or platform choice, the right fit for your organization is based on your requirements, your technology landscape, your users, your products and services and other considerations. Work with a domain expert to review your needs and decide if low code or no code development is right for you and if Creatio is the right platform and framework for your organization.
Contact Us to find out how Low-Code/No-Code Development and Creatio Development can help your business and provide swift, targeted results. Read out free White Paper: ‘What Is Low-Code, No-Code Development? How Can It Support My Business Application And Software Product Development Needs?’ and ‘Creatio Low-Code and No-Code Development: What is the Creatio LCNC Platform and When and How Can My Business Use Creatio?’
Ten Years Into the Citizen Data Scientist Revolution
It has been a decade since Gartner first defined the role of a Citizen Data Scientist. In 2018, Forbes published an article, referencing Gartner’s updated analysis of the role and stating that, ‘And the best part is, mid-market organizations already have potential citizen data scientists on their staff—it’s just a matter of tapping those with potential and interest in the work, and cultivating an analytical mindset across their workforce. Then, those who serve as citizen data scientists can grow their own skillsets, all the while being active players in their companies’ ability to tap the value of big data and drive transformation.’
‘It is likely that the Citizen Data Scientist role will continue to evolve, and it is important that the enterprise facilitate collaboration and knowledge sharing and build a sustainable technology environment with appropriate policies.’
A lot has happened in the past decade, and today the role of Citizen Data Scientist is no longer new. So, what has changed in the ensuing years? How as this role changed? Has the average enterprise embraced the role and made the technological and cultural changes required to truly support this approach?
Here are a few of the ways in which the Citizen Data Scientist approach has evolved within the organization.
- In the early days of the Citizen Data Scientist approach, Data Scientists often worried that their positions would become obsolete. Nothing can be further from the truth. The use of Data Scientists to perform strategic analytics and to refine analysis performed and submitted by business users has kept Data Scientists busy.
- The evolution of augmented analytics and self-serve tools has expanded analytical capacity, the speed at which business users can gather and analyze data and the dependability of the outcomes. Drag and drop capabilities, machine learning and, more recently, artificial intelligence (AI) have significantly improved tools and made it easier and more desirable for business users to dive into analytics and make it part of their day-to-day role.
- As cloud-based access expanded, the enterprise leveraged improved access and data platforms to enable collaboration and create multi-disciplinary teams and power user roles that would further encourage the use of these tools across the enterprise. Methods and guidelines improved data literacy and encouraged business user expertise creating more confident Citizen Data Scientists and supporting the role as a mainstream concept.
Every enterprise must do more with less, increase productivity and reduce missteps in order to remain competitive. So, it is likely that the Citizen Data Scientist role will continue to evolve, and it is important that the enterprise facilitate collaboration and knowledge sharing and build a sustainable technology environment with appropriate policies for user access, upgraded technology and data analytics tools and standards and regulations to govern and manage risk and maintain alignment with enterprise strategies and goals.
‘Those who serve as citizen data scientists can grow their own skillsets, all the while being active players in their companies’ ability to tap the value of big data and drive transformation.’
If you wish to know more about the Citizen Data Scientist approach and how augmented analytics tools and your industry and market knowledge can position you for success in this role, Contact Us today to find out how our team can help you to improve business results and increase team collaboration, data literacy, productivity and competitive advantage. Get started today with our self-paced FREE Online Citizen Data Scientist course.
Original Post : Assessing Ten Years of the Citizen Data Scientist Approach!
Users LOVE Conversational AI and Intuitive Analytics
Forbes Reports that Conversational AI is making autonomous agents capable of completing end-to-end workflows, so much so that Deloitte projects that 25% of businesses using GenAI will deploy AI agents in 2025 (growing to 50% in 2027).
Analytics solutions represents an ever-expending segment for conversational AI for features and function, and the use of Natural Language Processing (NLP) and Conversational Artificial Intelligence will continue to impact the industry as end users demand easier, more intuitive ways to prepare, analyze, share and report on data.
When we consider this evolving technology, and how it will continue to affect BI solutions and augmented analytics software, one thing is clear. The average enterprise and its users will push for more intuitive products and services, for ease-of-use and improved productivity, improved user adoption, affordability, the flexibility to grow and change with new technology and with business markets.
While we cannot make specific predictions about the evolution of AI and NLP, we can predict that businesses and users will push software vendors and technology companies to provide simpler tools, better, more accurate insight and results and automated features that provide users with alerts, suggestions and support to make their jobs easier and improve results.
Incorporating conversation AI bots and NLP within BI tools will allow users to monitor and manage results more easily and optimize their time by focusing on other, more crucial tasks.
When the organization invests in new or upgraded self-serve analytical tools, it must first ensure that users will adopt these tools and that the features and analytics provided will result in increased productivity and collaboration and resource optimization. When the enterprise selects BI tools with Conversational AI and NLP features, it can achieve all of these goals and assure a good Return on Investment (ROI) and Total Cost of Ownership (TCO).
As Artificial Intelligence (AI) and Natural Language Processing (NLP) technologies evolve, these technologies will be applied within advanced analytics, self-serve analytics and business intelligence (BI) tools to further improve features, provide better, faster insight and clearer, more concise results and optimize human resources and productivity.
The future of AI and NLP in BI tools will support:
- Processing large volumes of unstructured data (text, speech, written) to produce actionable insight and reports.
- Automatically identifying trends, patterns and sentiments to alert users of changes and shifts that may affect goals.
- Supporting decisions with conversational business intelligence to allow team members and executives to interact through NLP.
- Real-time analytics to reveal sentiment, emotion, context and tone in customer interaction.
- Automated reporting to save time and analyze complex data.
- External data integration to analyze markets, social media, financial markets, investments, and other aspects of external data that will affect businesses.
- Understanding and responding to voice commands, analyzing and working with contextual information, predicting user needs.
- Eliminating static reporting, using conversational, dynamic analytics that can leverage predictive and analytical algorithms to produce results.
Contact Us to find out more about how your business can incorporate Artificial Intelligence (AI), natural language processing (NLP) and machine learning into your products, your business processes and your Digital Transformation (Dx) initiatives. Our AI-Enabled Advanced Analytics Solutions and Products And Services can help you achieve your vision with affordable, dependable technology. Explore our white paper ‘Conversational AI And NLP Analytics Reduces The Dependence And Usage Of Traditional BI Tools, And Improves User Adoption And Data Democratization,’ and our free articles about Agentic AI: ‘The New Age Of AI: Agentic AI’, and ‘What Is Agentic AI And Why Should I Consider It For Apps?’
Original Post : Conversational AI and NLP Expand Access to Analytics!
