Understanding the role of a Citizen Data Scientist and the opportunities provided by this approach is the first step in leveraging the Citizen Data Scientist approach. Engaging a vendor and choosing the right Augmented Analytics Business Intelligence tools, services, training and deployment will ensure success. The prospect of transforming business users into Citizen Data Scientists may seem daunting but, with the appropriate planning and support, and the appropriate Augmented Analytics solution, the initiative can be extremely successful.
Fact-Based Analytics and Citizen Data Scientists = Results
So, you want your business users to embrace and use analytics? You want your business to enjoy the benefits of fact-based decision making? You want your business to use the tools of business intelligence to improve market presence, customer satisfaction and team productivity and collaboration?
‘Whether you are trying to solve a business problem, get to the heart of that problem, find a business opportunity, predict the need for resources, new products or locations or understanding changes in your customer buying behavior, you don’t have time to learn complex tools or take training in analytics.’
Gartner has predicted that, ‘a scarcity of data scientists will no longer hinder the adoption of data science and machine learning in organizations.’ And that is the good news. But, if the business is to leverage the potential of analytics within the organization, it must choose the right analytics tools to ensure that business users will adopt analytics in day-to-day tasks.
If your enterprise wishes to transition business users into Citizen Data Scientists and use augmented analytics to gain a competitive advantage, it must provide easy-to-use tools that do NOT require team members to be business analysts or IT professionals – tools that allow users to quickly gather data using self-serve data preparation, using data integrated from disparate data repositories with smart data visualization to view, format and share results that are clear, concise and actionable.
The business market is more competitive than ever and in today’s environment it isn’t enough to simply analyze historical data. To make good business decisions, adjust strategies and forecast and plan, you must use that historical data to plan for the future.
Businesses that can gather data from disparate sources and use historical data to understand trends and patterns and forecast for the future can establish and sustain a competitive advantage and plan more effectively and accurately, avoiding missteps in the market and costly mistakes.
If your business wishes to sustain a competitive advantage, if you as a user wish to advance in your career and build your value to the organization, it is incumbent upon you to embrace the trend of data democratization, data literacy and self-serve, augmented analytics.
Today, augmented analytics and smart data discovery make it easier for business users, data scientists, IT staff and the organization to benefit from fact-based decision-making, collaboration, data literacy and the ability to easily, gather, integrate and analyze data.
Whether you are trying to solve a business problem, get to the heart of that problem, find a business opportunity, predict the need for resources, new products or locations or understanding changes in your customer buying behavior, you don’t have time to learn complex tools or take training in analytics.
What you need are apps and solutions that allow you to ask easy questions in your own words and receive guidance and recommendations on how to best visualize and present your data and what techniques to use to gain the most insight.
Use a simple low-code, no-code analytics platform and augmented analytics and BI tools designed for business users with real-world business cases to find answers and solve problems. You 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 predictions, helping users and the business to achieve success in industries and businesses like retail, pharmacy and wellness, insurance, manufacturing, government and public sector, utilities, and other industries.
‘If your enterprise wishes to transition business users into Citizen Data Scientists and use augmented analytics to gain a competitive advantage, it must provide easy-to-use tools that do NOT require team members to be business analysts or IT professionals.’
To find out more about how to ensure Return on Investment (ROI) and Total Cost of Ownership (TCO) results, gain a competitive market advantage, and enable user adoption, read our free White Paper, ‘A Roadmap To ROI And User Adoption Of Augmented Analytics And BI.’ Explore The Benefits of 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.
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.
Like every other business, your organization must plan for success. In order to do this, the team must have a dependable plan and be able to forecast results and create reasonable objectives, goals and competitive strategies. These plans and forecasts will support investment in technology, appropriate resources and hiring strategies, additional locations, products, services and marketing strategies, partnerships and other components of business management to ensure success.
Forecasting and planning cannot be based on opinions or guesswork. It must be based on historical data, facts and clear insight into trends and patterns in the market, the competition and customer buying behavior. To accomplish these goals, businesses are using predictive modeling and predictive analytics software and solutions to ensure dependable, confident decisions by leveraging data within and outside the walls of the organization and analyzing that data to predict outcomes in the future.
‘Every industry, business function and business users can benefit from predictive analytics.’
According to CIO publications, the predictive analytics market was estimated at $12.5 billion USD in 2022 and is expected to reach $38 billion USD by 2028.
Predictive analytics encompasses techniques like data mining, machine learning (ML) and predictive modeling techniques like time series forecasting, classification, association, correlation, clustering, hypothesis testing and descriptive statistics to analyze current and historical data and predict future events, results and business direction.
When a business selects predictive analytics tools that are suitable for business users and team members, it can leverage sophisticated algorithms and analytical techniques in an easy-to-use environment to enable every team member to contribute to the bottom line by allowing them to gain insight into data and use that insight to make confident, fact-based decisions.
With these tools, users can explore patterns in data and receive suggestions to help them gain insight on their own without dependence on IT or data scientists. The enterprise can provide the tools needed at every level of the organization with tools and data science for business users that are sophisticated in functionality and easy-to-use for users at every skill level.
The benefits of augmented analytics and self-serve predictive modeling include:
No complex algorithms or data manipulation
Auto-recommendations for algorithms to explore underlying data
No advanced data science skills required
Analyze, share, collaborate and optimize business potential
Business users can prototype and hypothesize without professional assistance
Recommend optimal actions to achieve specific goals
Every industry, business function and business users can benefit from predictive analytics. Here are some examples of the use of predictive modeling:
Retail – Predictive Analytics tools can be used to understand customer buying behavior and to suggest products and product bundling based on previous purchases, buying patterns, and demographics. This creates a more personalized and targeted shopping experience that is unique to each customer.
Supply Chain – The organization can forecast demand and manage the supply chain to optimize inventory using machine learning to predict customer demand, seasonality, product trends etc., to that the enterprise can mitigate stock shortages and avoid warehouse and inventory overstock.
Healthcare – By using historical data regarding specific diseases, conditions and treatment plans, providers can forecast treatment outcomes, limit risk and improve overall care, thereby reducing complications, readmission and provider resource, medication and hospital bed shortages.
Energy Infrastructure – Using predictive analytics allows these businesses to monitor and analyze data and performance and to detect patterns and trends that may indicate downtime, breakdowns and maintenance issues.
Financial Services, Banks and Loan Businesses – Predictive analytics provides support for credit risk and fraud mitigation and allows businesses to create scoring models for loan approval, etc. based on credit history, and other financial considerations. Predictive modeling allows the organization to identify transactions that are outside the norm, and alert the business and its customers of hacks, fraud, etc.
‘When a business selects predictive analytics tools that are suitable for business users and team members, it can leverage sophisticated algorithms and analytical techniques in an easy-to-use environment to enable every team member to contribute to the bottom line by allowing them to gain insight into data and use that insight to make confident, fact-based decisions.’
These are just some of the benefits and use cases your business can consider to decide on how best to implement predictive analytics and integrate the use of these tools into day-to-day use for business users to improve data-driven decisions and results.
To find out more about AI And Predictive Analytics, 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.
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.
When a business decides to undertake a data democratization initiative, improve data literacy and create a role for Citizen Data Scientists, the management team often assumes that business users will be eager to participate, and that assumption can cause these initiatives to fail.
These days, most people understand the term Natural Language Processing (NLP). It has been around a while, and represents perhaps the most significant information tool in the past century.
Machine Learning and Natural Language Processing (NLP) have unlocked a vast library of knowledge, making it accessible to the average person, requiring no significant technical skills, and leveling the playing field for millions of people, seeking to learn and understand the world.
‘NLP search technology significantly simplifies the user experience and encourages team members to learn and incorporate augmented analytics into their daily activities.’
Just a few years ago, Gartner predicted that, ‘50% of analytical queries will be generated via search, NLP or voice, or will be automatically generated.’ Today, this prediction is a reality.
When an enterprise wishes to implement augmented analytics and business intelligence, and make these tools available to its business user community, it must select a solution that uses natural language processing (NLP) search capabilities to allow business users with average technical skills to gather and analyze data and achieve results. Without these simple tools, the enterprise cannot ensure user adoption of the solution.
Natural Language Processing Search Analytics (NLP) is crucial component to search analytics in that it allows business users to perform complex searches without endless clicks, coded queries, or complex navigation and commands. Users can access and view clear, concise answers and analysis quickly and easily, leveraging a familiar Google-type interface to compose and enter a question using common language.
When you choose Augmented Analytics with machine learning and natural language processing (NLP), your users can enjoy a self-serve environment that is easy and intuitive, and will increase user adoption, data democratization, and return on investment (ROI).
NLP search technology significantly simplifies the user experience and encourages team members to learn and incorporate augmented analytics into their daily activities. Finding information is easy! Let’s suppose a team member wants to understand the trends in regional bakery sales. With NLP, the user can simply ask, ‘how many bakery products were sold in the Southwest and Southeast regions in 2023?’
Natural Language Processing (NLP) and search capability allows users to avoid scrolling through menus and navigation. The user only has to enter a simply worded search query, and the system will translate the query, and return the results in natural language using an appropriate form, e.g., visualization, tables, numbers or descriptions. There is no advanced training required. Users can analyze data and receive results in a way that is meaningful to them.
The benefits of augmented analytics using natural language processing (NLP) enable swift, easy searching and allows business users to create context-rich searches that provide in-depth information and concise results and can be used to solve problems, identify opportunities, spot trends and patterns and present data and recommendations. There is no need to request reports or information from IT, business analysts or data scientists. The business user has the tools and the capability to get results when and how they need the information.
‘Just a few years ago, Gartner predicted that, ‘50% of analytical queries will be generated via search, NLP or voice, or will be automatically generated.’ Today, this prediction is a reality.’
Avoid Complex Analytics Solutions (Your Users Will Hate)
When a business is considering a business intelligence or analytics solution, it is important to recognize that today’s solutions are very different than the solutions of the past. Not only do they include more analytical techniques and features, but they have come a long way in providing access to sophisticated analytics for the average enterprise team member.
Harvard Business Review Analytics Service reports that
a) businesses can substantially improve business performance by giving frontline workers modern self-service analytics tools to enable fast intelligent action and,
b) not all self-service analytics provide this effective approach.
The Harvard Business Review Analytics Service surveyed nearly 500 executives and found that they reported significant performance improvement when they empowered frontline workers with augmented analytics. More than one-third of those surveyed noted improvement in customer and employee engagement and in product and service quality.
While some businesses may still be using business intelligence and analytics that are designed for data scientists and IT professionals, most of those are actively working to upgrade and/or migrate to augmented analytics and solutions that are designed for self-serve business user access.
Here’s why:
Search-based, self-serve analytics provides swift access to data and familiar natural language processing (NLP) search capability so business users can ask a question, get an answer and drill down to discover the root cause of issues. There is no need for the user to wait for IT or a data scientist to produce a report. They can continue to work on a task or a problem with full insight into results, challenges and possibilities.
The enterprise can enable data democratization and data literacy across the business landscape, thereby ensuring that there is a rapid response to market and competitive changes and to changing customer buying behavior.
Business users can leverage their industry knowledge and functional skillset and combine data insight with experience to produce the best results.
Intuitive, easy-to-use solutions help to combat user resistance and ensure user adoption. While there are always cultural issues surrounding this type of adoption and the perceived changes in responsibilities, when business users see the value of having crucial information at their fingertips, the enterprise can ease the transition and ensure user adoption.
No matter the role of the user, the team can enjoy the benefits of augmented analytics and make the transition to Citizen Data Scientists to improve collaboration, data sharing and fact-based decision-making.
The business can understand quality and maintenance issues, refine customer targeting and marketing optimization, and make appropriate financial investments, and they can analyze trends and patterns and make forecasts and predictions.
When the enterprise adopts these tools and techniques, they allow Citizen Data Scientists to perform analytics on a day-to-day basis and, where appropriate to effectively interact with and collaborate with the IT team and data scientists to refine data and prepare it for more strategic initiatives, so there is a seamless handoff from the business user to the analytical community, when and as necessary.
When the business is ready to acquire augmented analytics or to upgrade from existing, more restrictive solutions designed for professional analytical resources, it is important to choose the right solution – one with sophisticated tools that are presented in an intuitive user interface with auto-suggestions and recommendations to assist business users, and ample personalization of dashboards and reports.
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