Conversational AI and NLP Analytics Reduces the Dependence and Usage of Traditional BI Tools, and Improves User Adoption and Data Democratization
The incorporation of Artificial Intelligence (AI) and Natural Language processing (NLP) in existing business intelligence and self-serve analytics tools has had (and will continue to have) a profound influence on ease-of-use, on user adoption and on the democratization of data across the enterprise, and the use of Conversational AI and NLP is rapidly changing the face of BI tools and business user and organizational expectations.
Traditional BI, Augmented Analytics, or Modern BI/Analytics?
Whether your business is planning to acquire a business intelligence (BI) or augmented analytics tool for a first-time installation or looking to upgrade an existing system, the decision starts with the ‘what’ of the solution. What kind of solution will best fit your needs for infrastructure, integration, user requirements, pricing, upgrades etc. and what kind of approach, features and functionality will be most suitable for your business.
‘When considering a new or upgraded business intelligence or advanced analytics solution, it is important to look at your enterprise requirements to accommodate existing technologies and infrastructure, business processes and models and user needs.’
A Recent Study reveals a number of considerations to be included in your choice of BI tools and analytical platforms, including:
Zero footprint: 100% web-based, no client-side installs or downloads required
Support for role-based reporting and dashboards
Support for mobile Business Intelligence
Communication features like notes, comments, and likes
Updates in real-time: your dashboard is always up to date
Basket, advanced and predictive analysis
Support for machine learning and generative AI
Usability: ease-of-use and ease-of-learning
Insights (graphs, definitions, et cetera) that are reusable across BI applications
While this study carves out what it considers to be the optimal analytics framework and platform, some of all of these considerations may not apply to your business needs.
When reviewing the various iterations of analytics frameworks and platforms, you will need to understand the following capabilities and structures. Understanding how each of these approaches works and what advantages and limitations they include will make it easier for you to choose a solution and approach to suit your needs.
Augmented Analytics
Today’s augmented data analytics incorporates artificial intelligence (AI) and machine learning (ML) to streamline, automate and improve the process of data analysis, so business users can more easily gather, interact with, select and report on data, simplifying data prep, insight and collaboration and allowing your team to explore data in a way that is meaningful to them.
Augmented analytics uses machine learning, natural language processing (NLP), and algorithms to easily analyze and manipulate large datasets and reduces the time needed for processing, thereby allowing business users to leverage analytics in their day-to-day workflow and freeing Data Scientists to perform more strategic tasks.
Benefits of Augmented Analytics
Streamlined data prep and analytics
Optimizes business user and data scientist time and effort
Faster, more insightful decision-making
Increased operational efficiency
Democratized data analytics
Traditional Business intelligence
Traditional BI Tools use conventional analytical techniques and technologies to collect, analyze and present data. These systems typically rely on a centralized data source and a selected set of formatted reports. Users leverage data extraction, transformation and loading tools (ETL) to extract data from a data warehouse and online analytical processing (OLAP) tools to analyze data. Reports and visualization are typically delivered through a dashboard with standard key performance indicator (KPI) software to monitor metrics.
Benefits of Traditional BI Tools
These tools provide support for a range of data types and data sources
IT and/or data scientists can create standardized reports and models to address business and user needs
The enterprise can maintain and control data sources on-site
Modern BI Tools and Augmented Analytics
When an organization integrates the use of modern BI tools with more advanced augmented analytics it can leverage artificial intelligence and traditional tools to satisfy the needs of a diverse set of users, business units, etc. Modern Business Intelligence technologies integrates more sophisticated features to bring analytics to all users and allow them to choose the tools that best suit their needs in a self-serve environment with automated data preparation, fast and easy data access and insight, Natural Language Processing (NLP) that allows users to ask questions using common language and receive answers using visualization techniques.
Benefits of Modern BI Tools and Augmented Analytics Combined
Sophisticated self-serve analytics suitable for business users with limited technical skills
Real-Time insights and reduced time-to-decision
Monitoring and managing of trends, anomalies and other factors that impact results
Collaboration and data democratization with unique tools to suit every user
‘When reviewing the various iterations of analytics frameworks and platforms, you will need to understand the following capabilities and structures. Understanding how each of these approaches works and what advantages and limitations they include will make it easier for you to choose a solution and approach to suit your needs.’
When considering a new or upgraded business intelligence or advanced analytics solution, it is important to look at your enterprise requirements to accommodate existing technologies and infrastructure, business processes and models and user needs.
Support Your Large Business User Base with the RIGHT BI Tools
According To Finance Online, Allied Market Research reports that small and medium businesses are driving enterprise use of analytics, but World Data Science Initiative reports that 80% of global companies are investing in data analytics, thereby revealing analytics growth across, small, medium and large enterprises.
For your large enterprise, data analytics can and will be a definitive issue impacting your business and market growth. Choosing the right solution to support data democratization and improved data literacy across the enterprise will ensure that your team can create, share, collaborate and report on data, make recommendations and suggestions based on fact, and use data-driven metrics to ensure that strategies and operational decisions are relevant, accurate and effective.
‘While many large organizations are daunted by the idea of implementing analytics across the organizations, it is completely feasible to plan for an deploy this software and to modify business processes to build on fact-based decisions.’
Today’s self-serve augmented analytics and modern business intelligence solutions are designed to support users across the enterprise, and a solution that is built on the right technology platforms, will enable rapid implementation and user adoption and requires very little training or transition time. Auto-suggestions and recommendations allow users to work on their own, no matter their skill levels.
To support a large user base, you will want to select an augmented analytics solution designed with a low-code, no-code, artificial intelligence and machine learning environment to ensure scalability and seamless, responsive, mobile access.
Your team can untangle quality and maintenance issues, refine customer targeting and marketing optimization, make appropriate financial investment decisions, and even use external data to analyze trends and patterns and make forecasts and predications.
Real-time data management lets users connect to data sources in real time, and compiles data for fast performance to deliver real time analytics. Cached data management caches data and performs pre-aggregation and other computations for superior performance and analytics, and refreshes data from data sources at a defined frequency.
Your enterprise can choose on-premises or private or public cloud-based data management to access the analytics solution from any business location around the world, or for remote workers or those working on the road, in hotels or airports.
‘For your large enterprise, data analytics can and will be a definitive issue impacting your business and market growth. Choose the right solution to support data democratization and improved data literacy and use data-driven metrics to ensure that strategies and operational decisions are relevant, accurate and effective.’
While many large organizations are daunted by the idea of implementing analytics across the organizations, it is completely feasible to plan for an deploy this software and to modify business processes to build on fact-based decisions. Work with your IT partner to plan a reasonable roll-out and address cultural concerns, and to budget for and implement an affordable, dependable analytics solutions across the enterprise. No matter how many business users your business has, you CAN adopt and leverage self-serve augmented analytics to support your business, improve competitive advantage and gain crucial insight into data for planning, problem-solving and identification of trends, patterns, issues and opportunities.
Ten years ago, the term ‘Citizen Data Scientist’ was coined by the world-renowned technology research firm, Gartner. The term refers to business team members whose expertise and role are not focused on analytics as a primary job function. Using self-serve analytics solutions, these team members can leverage analytics to create models, reports and analysis to collaborate, share and make decisions. Gartner predicted the emergence of this role within businesses as part of the growing importance of data analytics and data-driven decisions within the business environment.
A decade later, it is worth reviewing the status of this role in the business enterprise and within the average organization. Is the Citizen Data Scientist role a standard role within most businesses today? Does a Citizen Data Scientist replace or work independently from a Data Scientist or Business Analyst? Has the Gartner prediction come to fruition?
While there are no current statistics regarding the number of companies currently using a Citizen Data Scientist approach, the trend toward data-driven planning and forecasting is clear. As with many other business trends, the larger organizations usually take the lead. They have the budget and the depth of resources to plan for and deploy changes across the enterprise and to test theories and enforce cultural changes.
Here are some statistics that reflect the growth of the Citizen Data Scientist movement and the supporting technologies that engender this approach:
Studies reveal that the number of Citizen Data Scientists is growing five times faster than the number of Data Scientists.
Automation technologies support the growth of the Citizen Data Scientist approach with over 40% of data science tasks automated through augmented analytics and/or machine learning.
The Machine Learning (ML) market is growing at a compounded annual rate of more than 15%, reflecting the need for data analytics capabilities within self-serve solutions.
By some estimates, interest in the Citizen Data Scientist role has tripled in the past decade, as medium and small enterprises embrace new, intuitive, more affordable technologies to support the Citizen Data Scientist concept within their organization.
As this concept became mainstream, the industries saw a trend toward increasing data-driven insight while reducing dependence on Data Scientists.
While the Citizen Data Scientist role began as a basic initiative to gather data and create simple reports, today’s Citizen Data Scientists are now using business intelligence (BI) tools and augmented analytics with Natural Language Processing (NLP), machine learning, low-code and no-code platforms and other technologies to leverage limited technical skills and create sophisticated analytics with clear results. Reports, dashboards and data sharing allow team members to create and use data models and to increase data literacy and data democratization.
Team members can use smart data visualization and assisted predictive modeling to gain insight and solve day-to-day problems, advise management and collaborate with other team members to understand trends, patterns, challenges, and opportunities and leverage metrics to make fact-based decisions.
This evolution of the Citizen Data Scientist role within the organization can free Data Scientists to perform more strategic activities without the daily distraction of simple report requests. If and when a particular data model or analytical approach must be refined to be more strategic, the Citizen Data Scientist can work with the Data Scientist to achieve that goal.
Using this approach, the enterprise can empower team members with the tools to analyze data and to use their knowledge of the industry, market, customers and business environment to make decisions and improve results.
When we consider the last decade of Citizen Data Scientist evolution, we see that businesses across all industries are working toward a more data-driven approach to decision-making, and embracing data democracy as a means to improve productivity and the quality of decisions and to reduce re-work and missteps.
Transform Your Business with Citizen Data Scientists
The business environment today is competitive. Whether your business is global or local, you are challenged to do more with less, to set and achieve goals more quickly and to stay ahead of your competitors by gaining a comprehensive understanding of what your customers want, what they WILL want, and how to best attract their attention and retain them.
To meet these challenges, every team member and employee must have a thorough understanding of how their roles and responsibilities fit into the grand scheme of things and how the projects, tasks and activities they pursue on a day-to-day basis will affect revenue, outcomes and results.
When a business (large or small) makes the decision to transition business users to Citizen Data Scientists, it supports the alignment of goals and objectives with fact-based decision-making and improved data literacy, encouraging its users to embrace and understand data and use that data to collaborate, present information to management and gain insight into results to identify opportunities and address issues.
‘The Citizen Data Scientist approach transforms the organization by improving time to market, reducing rework and mitigating market missteps and improving productivity and the alignment of workflow and tasks with the goals and objectives of the enterprise.’
World renowned technology research firm Gartner first coined the term ‘Citizen Data Scientist’ in 2016 and defined the role as ‘a person who creates or generates models that leverage predictive or prescriptive analytics, but whose primary job function is outside of the field of statistics and analytics.’
Nearly a decade later, the role has been refined and structured within many organizations and recent Gartner research reports that, ‘Citizen Data Scientists can be leveraged to perform repetitive and redundant tasks in the analytics workflow, and therefore create value to the organization, while allowing expert data scientists to focus on more complex tasks.’
The increased pace and tenor of competition has forced businesses to accommodate rapid change in markets and customer buying behavior by using analytics, data scientists and business analysts to work with IT and create reports and presentations to be used for decisions. But there just aren’t enough professional resources of funds to support this approach. Hence, the evolution of Citizen Data Scientists.
The evolution of self-serve augmented analytics tools and technologies like natural language processing (NLP) and NLP search, machine learning, flexible data visualization, and artificial intelligence (AI) provide support for business users without technical skills to gather and analyze data and produce reports, collaborate with other users and make recommendations using insight derived from advanced analytics. And this approach supports data democratization and data literacy.
The Citizen Data Scientist approach also transforms the organization by allowing business users to interact with and collaborate with IT and data scientists to take day-to-day data analytics and translate them into strategic initiatives with measurable results, accurate predictions and rapid flexible processes.
The Citizen Data Scientist approach transforms the organization by improving time to market, reducing rework and mitigating market missteps and improving productivity and the alignment of workflow and tasks with the goals and objectives of the enterprise. It provides a career path for business users and advances their knowledge and skills, allowing them to understand how their role directly influences results and to create and innovate.
‘When a business (large or small) makes the decision to transition business users to Citizen Data Scientists, it supports the alignment of goals and objectives with fact-based decision-making and improved data literacy, encouraging its users to embrace and understand data.’
To plan for and execute a Citizen Data Scientist initiative, the organization must engage an expert in augmented analytics and develop a comprehensive understanding of technology and cultural changes in order to advance this new idea within the ranks of IT, data scientists, business users, managers and executives.
Business and Team Member Benefits of Citizen Data Scientists!
When a business considers the prospect of implementing business intelligence and augmented analytics tools to align objectives and streamline processes and decision-making, the business management and executive team can often see the project as time-consuming and expensive.
In fact, most proposals that include process changes, training and new technology get stalled because:
a. change is hard
b. there is a tendency to say that everything is just fine the way it is
c. the management team believes that the expense is not equal to the value of the transition.
When it comes to BI tools and self-serve analytics, all of these claims can be disproven by results.
‘By providing business users with easy-to-use BI tools and augmented analytics features that produce results and allow them to make the most of their knowledge and time, the organization is more likely to retain its human resource assets and to identify and utilize its team member skills to achieve results, thereby creating a competitive advantage and a more agile, streamlined enterprise.’
In fact, Gartner has predicted that ‘Early adopters of augmented analytics have the potential to realize more strategic and differentiating business benefits from their analytics investments than those who wait until these technologies are widely adopted.’
Results and benefits are proven across the enterprise by businesses that have already adopted business intelligence and augmented analytics.
For the organization, these benefits include:
Support for data-driven, fact-based decision making
Insight and perspective into history, results and trends and patterns for strategy and operational decisions
Encouraging collaboration among team members, IT, business analysts and data scientists
Business agility and efficiency
Leveraging human resource assets and improving productivity
Creating a competitive advantage with rapid decision-making and market response
Improving and optimizing financial investments, forecasts and predictions
For business users, transitioning to Citizen Data Scientists, the advantages include:
Combining professional and industry knowledge with data to achieve accurate results
Leveraging simple BI tools and self-serve analytics to receive recommendations and suggestions and make quick, clear decisions
Reducing the need for IT and data scientist assistance
Collaborating with other business users to create and share data, reports and information
Advancing career goals by learning and using tools that combine existing skills with new analytical capabilities
Improving data-driven decisions and reducing re-work
By providing business users with easy-to-use BI tools and augmented analytics features that produce results and allow them to make the most of their knowledge and time, the organization is more likely to retain its human resource assets and to identify and utilize its team member skills to achieve results, thereby creating a competitive advantage and a more agile, streamlined enterprise.
‘Gartner has predicted that ‘Early adopters of augmented analytics have the potential to realize more strategic and differentiating business benefits from their analytics investments than those who wait until these technologies are widely adopted.’
If your business wishes to capitalize on the potential of the Citizen Data Scientist approach, it important to work with an IT Partner who can help you define your requirements and strategize for optimal success, providing the augmented analytics tools and knowledge of the industry that is required to position you for success. Get started today with our self-paced FREE Online Citizen Data Scientist course.
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.
Ensure ROI, TCO and User Adoption with the Right BI Tools
When a business sets out to implement BI tools or self-serve augmented analytics, it must consider the entire technology landscape of the business, the expectations of end-users, the cost of the solution, how easily it can be upgraded and integrated and, well…so much more!
‘To ensure user adoption and achieve the ROI and TCO the business deserves, take the time to do the work. Develop requirements, consider the features and functionality of a solution and compare those to your use cases and your user expectations.’
Management teams do not take kindly to poor Return on Investment (ROI ) or Total Cost of Ownership (TCO) and if the business has had difficult projects in the past, the next time a software solution is presented for approval, senior managers will remember the challenges and failures of the past!
Still, when you are attempting to transition business users into Citizen Data Scientists, there is bound to be some pushback from users and that pushback, combined with senior management concerns can and WILL bring your project to its knees if you do not anticipate and address these concerns.
Here are some statistics that will help you to understand the issues faced by businesses in implementing BI tools and analytics:
The global BI adoption rate is 26%
On average, businesses use at least four different BI tools
97% of the data gathered by businesses is not used
74% of employees express dissatisfaction and are overwhelmed when working with business data
In order to assure successful deployment, user adoption, improved Return on Investment (ROI) and Total Cost of Ownership (TCO), the business should include detailed requirements in its solution selection. When choosing a BI tool and augmented analytics solution for your business, one of the most crucial concerns is the features and functionality of the prospective solution and how this solution will meet user and organizational needs. When planning for deployment of BI tools and augmented analytics, the business should take the time to work through a process, ensuring that it has considered what users need and want to ensure user adoption and the ease-of-use and features the solution will provide.
It is important to remember that there are also specific features a business should consider, like embedded BI and easy-to-employ Integration APIs, that will assure user adoption and appropriate ROI and TCO. These considerations will affect how well the solution is received, whether users will adopt it and be satisfied with the selection, and how the investment will perform when considering total cost of ownership (TCO) and return on investment (ROI) when compared to other possible uses of the same investment funding.
With this foundation of documented features, services, skills and capabilities, the business can implement, manage, upgrade and support Business Intelligence and Augmented Analytics capacity and growth within the organization.
‘When you are attempting to transition business users into Citizen Data Scientists, there is bound to be some pushback from users and that pushback, combined with senior management concerns can and WILL bring your project to its knees if you do not anticipate and address these concerns.’
To ensure user adoption and achieve the ROI and TCO the business deserves, take the time to do the work. Develop requirements, consider the features and functionality of a solution and compare those to your use cases and your user expectations. INVOLVE your users and middle managers in planning to understand how and when to deploy the tools and how to support your business users as you transition them into Citizen Data Scientists.
To find out more about how to ensure Return on Investment (ROI) and Total Cost of Ownership (TCO) results, and enable user adoption, read our free White Paper, ‘A Roadmap To ROI And User Adoption Of Augmented Analytics And BI.’ To find out more about our Augmented Analytics And BI Tools, Contact Us. Keep pace with changing enterprise needs and support business agility. Let us help you realize your business goals and objectives with fact-based information, and flexible, scalable technology solutions that will support Citizen Data Scientist initiatives, and improved data literacy and data democratization.
Find Out the How of the Citizen Data Scientist Approach
In 2016, the technology research firm, Gartner, coined the term ‘Citizen Data Scientist,’ and defined it as ‘a person who creates or generates models that leverage predictive or prescriptive analytics, but whose primary job function is outside of the field of statistics and analytics.’
‘When business users make the transition to Citizen Data Scientists with access to augmented analytics solutions, they can provide additional value to the team, to managers and executives and allow IT and data scientists to focus on strategic goals.’
In the ensuing years, the Citizen Data Scientist role has become more refined, and those businesses that embrace this approach have seen real benefits. But just who are Citizens Data Scientists, and how does a business recognize candidates and benefit from enabling this role?
How Do I Find Citizen Data Scientist Candidates Within My Business? You will find your Citizen Data Scientist candidates among your business users and team members. They are curious and eager to learn new skills to contribute to the organization and to hone their skills for career advancement. Team members who make great Citizen Data Scientists are often power users, and are acknowledged as leaders within their own team. They are NOT IT professionals, analysts or data scientists but they share a common characteristic for precision and wanting to get it right the first time.
What Does a Citizen Data Scientist Do? Within the context of their roles and responsibilities, every business user needs clear, meaningful information to make fact-based decisions and recommendations. Citizen Data Scientists use data to create reports on a daily basis. As the Citizen Data Scientist role evolved, team members have leveraged the advantages of this data to share reports, to create and format presentations for recommendations and suggested changes to support pricing decisions, hiring, production, new products and services, financial investments, marketing and advertising campaigns, and many other decisions. As the movement grows within your organizations, you can enable data democratization and improve data literacy. Citizen Data Scientists can also work with IT, data scientists and business analysts to share their research and analytics when the business feels it is necessary to take the analytics to another level to ensure credibility for strategic decisions.
What Tools and Training Does a Citizen Data Scientist Need? One of the primary reasons the Gartner predictions have come to fruition is he evolution of the business intelligence (BI) and augmented analytics market to support the concept of Citizen Data Scientists. Today’s analytics solutions are easy-to-use, self-serve tools driven by Natural Language Processing (NLP), and machine learning, as well as Artificial Intelligence (AI). All of these technologies come together to support the business user and provide tools that are sophisticated in their functionality, yet intuitive and easy for a business user. These tools do not require IT skills or data science knowledge. When the team uses these tools, they can adopt a common language and techniques to work with IT and data scientists to create use cases and refine and share reports, formats and outcomes. The more complete, and intuitive the solution, the less training and onboard time the user will require. There are simple, Free Training Courses available that can help your business and your team understand the uses and benefits of this approach and enable user adoption.
When business users make the transition to Citizen Data Scientists with access to augmented analytics solutions, they can provide additional value to the team, to managers and executives and allow IT and data scientists to focus on strategic goals. Using Augmented Analytics Tools like self-serve data preparation to gather and prepare data, and smart data visualization to receive suggestions and recommendations on how to best view data, users can combine predictive analytics to forecast and model, and sophisticated tools like anomaly monitoring, key influencers, and sentiment analysis to gain crucial insight into changes in customer buying behavior, supplier issues, product time-to-market, trends, patterns and opportunities with dependable metrics to make data-driven decisions.
‘The Citizen Data Scientist role has become more refined, and those businesses that embrace this approach have seen real benefits.’
If your business wishes to capitalize on the potential of the Citizen Data Scientist approach, it important to work with an IT Partner who can help you define your requirements and strategize for optimal success, providing the augmented analytics tools and knowledge of the industry that is required to position you for success.
Low Code No Code Development Supports Analytics Performance
Within the very near future, it is estimated that 70% of all software and application design will include a component of low-code or no-code development. So, it is no surprise that analytics software and tools are also affected by this trend. While advanced analytics and augmented analytics solutions provide a sophisticated, complicated underpinning of algorithms and analytical techniques, the average enterprise expects (and should look for) tools that are easy to use, so they can improve data literacy and data democratization and leverage analytics within the organization at the business user level, to improve results and efficiency.
It may be difficult to understand how such complex systems can benefit from the no code, low code approach, since the very concept of this approach seems at odds with the complexity of an analytical solution, but nothing could be further from the truth. When applied appropriately, these techniques can benefit the foundation of the augmented analytical solution and the users of those solutions.
Time and Expense – In a world where new features and functionality must keep pace with market demand, the emergence of no-code and low-code allows developers to add analytical functionality quickly, while controlling costs and time to market.
Business and Market Requirements – As organizations and business users embrace analytics, the need for new types of visualization, reporting and features changes quickly. In order to stay abreast of these changes and offer businesses the products they need, analytical vendors can quickly leverage, modify and develop new approaches to satisfy user requirements. Vendors can accommodate business-specific needs and data visualization requirements without time-consuming, expensive customization.
Integration of Third-Party Apps – Low-Code, No-Code capabilities support the easy integration of other enterprise applications and solutions and allow data analysis across the organization.
Performance and Scalability – Low-Code and No-Code solutions and platforms enable high-performance, scalable solutions and ensure that businesses can accommodate an expanding user base and data volume.
Compliance, Data Security and Industry Standards – No Code, Low-Code development includes data encryption features and user access security controls to mitigate risk, and protect data integrity and privacy.
If you are still wondering whether low-code and no-code approaches are appropriate for software and applications, consider these predictions and statistics from technology research organizations:
Gartner predicts that 75% of new software solutions will incorporate a low-code approach to development.
By some estimates, the use of low-code, no-code and artificial intelligence in analytics solutions has increased user access to analytics by as much as 56%.
Gartner predicts that organizations that lack a sustainable plan to operationalize and manage data and analytics will face a two-year setback in their data and technology efforts.
Choosing the right self-serve, augmented analytics solution can help the enterprise build a crucial foundation for analytics, for transition of business users into a Citizen Data Scientist role and for improved time-to-market, decision-making and collaboration. The use of new and cutting edge technologies and the seamless incorporation of these technologies is critical to the success of the analytical application implementation and to return on investment (ROI) and total cost of ownership (TCO) metrics.
Look for Analytics with Low-Code/No-Code Technology!
The advent of low-code, no-code app and software development has enabled rapid, innovative changes to all types of development projects and that new environment is evident in Modern Business Intelligence (BI) and Augmented Analytics products and solutions.
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