The Client is a state government with a population of approximately 30 million citizens, and an annual budget of $5 billion USD. This government agency focuses on implementing IT and e-governance initiatives to enhance citizen services and governance efficiency. By leveraging advanced technology, the agency aims to improve the quality of life for citizens and to streamline administrative processes across and throughout departments.
The Client is an India-based construction and infrastructure development company, committed to delivering quality infrastructure for private, public, and government projects. With over fifty (50) years of accumulated knowledge and experience, the organization applies its extensive resources to ensure superior infrastructure solutions.
What makes a great Mobile Business Intelligence (BI) solution? There are many factors to consider and, if you do not choose wisely, you risk a) spending a lot of money and getting very little in return, b) working with incorrect, old or incomplete data, c) having to upgrade and spend even more money, d) disappointing your team and falling short of objectives, e) missing your Citizen Data Scientist initiative goals.
No matter the reason or the goal, when an enterprise chooses the right Augmented Analytics solution and carefully plans for and executes its implementation, it can optimize business results, reduce expenses and improve its market position, customer satisfaction and user adoption, and it is key to transforming business users to Citizen Data Scientists to improve results and team skills. Here, we examine the benefits of Augmented Analytics and how to plan and successfully execute an Augmented Analytics initiative.
There are many business issues surrounding the use of data to make decisions. One such issue is the inability of an organization to gather and analyze data. These enterprises will typically focus on building a team of data scientists or business analysts to help with this task OR they might take on an augmented analytics initiative to provide access to data and analytics for their business users. This is where businesses will often face a second issue; namely that the analytics solution they choose is not designed to easily and quickly provide insight into data and to ensure data quality.
Nothing…and I DO mean NOTHING…is more prominent in technology buzz today than Artificial Intelligence (AI). The use of Generative AI, LLM and products such as ChatGPT capabilities has been applied to all kinds of industries, from publishing and research to targeted marketing and healthcare. Gartner recently estimated that the market for AI software will be nearly $134.8 billion, with the market growing by 31.1% in next several years. In a recent survey of C-suite executives, 80% of said they believe AI will transform their organizations, and 64% said it is the most transformational technology in a generation.
There are many reasons to consider adding embedded BI to your enterprise technology landscape. When you are making a critical business decision, one of the factors you probably include is how your competitors and other businesses are addressing the problems you are trying to solve. When making a decision about Embedded BI consider these survey results. Businesses that are currently using embedded BI within their enterprise, report the following:
Recent surveys reveal that the mobile BI market is growing by leaps and bounds. Of those surveyed, over 35% of companies said that mobile BI is in use among their business users. While some businesses have yet to adopt BI tools or augmented analytics for their business users, many have done so, and are now in the process of adding mobile BI to the set of tools available to users. There is no doubt that the COVID pandemic affected this strategy, since many businesses are now embracing a full-time or a hybrid remote work environment that requires team members to have access to tools from wherever they are working.
Suppose you are trying to understand why a marketing campaign is failing, or what factors cause your customers to buy your services again. What if you need to know whether the color of a product affects the number of units sold in a particular country or area of a country? There are so many variables and components that can affect business success, and understanding the relationship among all the factors of success can seem like a guessing game.
When you are faced with this quandary, it is wise to use analytics to take the guesswork out of the equation. But how do you begin to analyze all the factors at play?
‘Can an augmented analytics tools tell you what algorithm or analytical technique to use? Can it help you understand the relationships of various factors and issues and determine what changes will help you improve the revenue for a product, or help you create a new service package?’
Statistical and analytical experts will tell you that there are three primary factors that can help you decide on the metrics to use for your analysis:
…and there is one more critical issue you must consider. Namely, who is doing the analysis? If you want to democratize data and improve data literacy across your enterprise, you will want your business users to understand and use analytical tools. But your team members are not statisticians or data scientists. So, they will need easy-to-use augmented analytics tools.
But can an augmented analytics tools tell you what algorithm or analytical technique to use? Can it help you understand the relationships of various factors and issues and determine what changes will help you improve the revenue for a product, or help you create a new service package?
One of the most frustrating tasks a business user has in analytics is finding and gathering the right data for analysis and ensuring that all factors, variables and data that may affect the outcome of the analysis is included. Depending on the size of the dataset a user selects, there may be hundreds or thousands of variables, and business users often find it difficult to identify the rights ones. Yet without the ability to identify the right variables, the business is likely to measure and attend to the wrong things.
That’s where Key Influencer Analytics comes into play! This approach puts the power and clarity of targeted analytics in the hands of business users and support Citizen Data Scientist initiatives and the critical goals of Data Literacy across the organization.
The user can simply point to the dataset they want to analyze and the system will identify the target and the influencers or predictors that will affect the target, along with its impact and it provides crucial metrics such as mean, outliers, and others and identifies relationship and distribution among variables. The system will auto-suggest relationships and present distribution and impact using the most appropriate visualization.
Users enjoy interactive features that allow them to see and explore other combinations and impacts and can select target and predictors, and use them for models, reports or KPIs. Key Influencer Analytics empowers every business user and allows them quickly select and target data to achieve results without the assistance of a data scientist, IT professional or analyst.
Key Influencer Analytics will:
With these tools, business users can identify what matters most within the data, and how the various factors and relationships impact success, and they can understand the interdependence of variables and leverage auto-suggestions and machine learning functionality to gain insight. Users can also leverage the features within the tool to consider various combinations and the impact of those combinations on the success of the project, product or plan.
‘There are so many variables and components that can affect business success, and understanding the relationship among all the factors of success can seem like a guessing game.’
Find out how Key Influencer Analytics can benefit your business users and support Citizen Data Scientists, and how it can provide an advantage to your data scientists, business analysts and IT team members.
If you are an IT professional, a business manager or an executive, you have probably been following the progress of the Citizen Data Scientist movement. For a number of years, Gartner and other technology research and analysis firms have predicted and monitored the growth of this phenomenon.
In fact, Gartner predicted that, ‘…40% of data science tasks will be automated, resulting in increased productivity and broader usage by citizen data scientists.’
So, how is it going? It’s actually going quite well.
However, it is worth noting that some businesses have not had the success they expected when implementing a Citizen Data Scientist approach. One of the primary reasons for falling short of results is to set inappropriate expectations regarding the role of Citizen Data Scientists vs. Data Scientists within the organization.
As the Citizen Data Scientist approach gained momentum, businesses seemed to develop an expectation that Citizen Data Scientists could replace Data Scientists. Nothing could be further from the truth. Augmented Analytics and Citizen Data Scientists are not meant to replace refined data modeling or the role of Data Scientists, but rather can supplement and support analytics across the enterprise. The enterprise should not discount the value of strategic data analytics and its place in the organization but rather should see augmented analytics and Citizen Data Scientists as a way to drive fact-based decisions and provide clarity and data-driven actions across the enterprise.
The fact is that there is a place for Citizen Data Scientists within your business, AND a place for Data Scientists and the strategic use of their skills.
If you want your Citizen Data Scientist initiative to succeed, and you wish to achieve data democratization and data literacy, you must understand how augmented analytics should be used to support business users and organizational objectives. So, let’s dive in and explore this issue further.
When selecting an augmented analytics solution, your enterprise must choose tools that are designed specifically for business users with average technology and analytical skills. Easy-to-use, intuitive tools will ensure user adoption. If you choose business intelligence or advanced analytics tools that are meant to data scientists, IT professionals or business analysts, you are setting your team up to fail. These tools are focused on the needs of Data Scientists. The tools are powerful and can produce undeniable value in the right hands, but they are not designed for your business professionals. To use these tools, users must manually gather and prepare data, scrubbing, cleaning, etc., and then write complex queries and use complicated algorithms and analytical techniques. Users must be expert in R programming or in Python or other scripting and programming languages. In short, this kind of software, app or solution is not for the feint of heart, and it’s certainly not suitable for a Citizen Data Scientist.
The right business user solution is an augmented analytics should be designed with all the tools a business user needs to get swift, dependable results.
Once you have chosen the right augmented analytics solution, you must establish appropriate expectations.
That is not the purpose of this strategy. The purpose of a Citizen Data Scientist approach is to give your team members tools that will allow them to discover trends and patterns, and to gain insight into what is working and what is not working in their current process, workflow and in their day-to-day activities. If and when an issue is identified that will require adapting a strategy or a major goal or objective, the enterprise must have a process in place that will allow a team member to refer her/his research to a Data Scientist, IT team member or other analytical professional, where the initial analysis will be refined and studied for use in strategic goals. When a Citizen Data Scientist uses augmented analytics, they should not be expected to perform complex modeling or to establish predictive models that will be rolled out in production mode or dictate a new strategy.
If a business user/Citizen Data Scientist discovers an issue or an opportunity, that user can explore the issue, look for relationships among the variables and factors that affect success and failure, develop an understanding of the challenge or the possibilities for product bundling, changing a marketing campaign, etc., and then share and collaborate with the team to further analyze and discuss the issues. It is this day-to-day access to analytics and clear data that will allow business users to make fact-based decisions and to build an understanding of data and analytics and how the information contained in data repositories and software systems can be integrated and analyzed to gain more clarity and to provide real metrics and measurements, so decisions are based on facts, rather than guesswork and opinion.
Role – Day-to-day business decisions, team collaboration and data sharing.
Benefits – Improved team collaboration, improved data literacy and perspective, improved business agility, timely decisions.
Most organizations cannot afford a team of Data Scientists and, even if they could, they do not want those professionals pulled away from crucial, strategic focus by day-to-day requests and projects that have short-term outcomes and importance. Rather than trying to replace Data Scientists within the business, the enterprise can optimize their time and reduce the need to hire more resources, by improving focus and enabling a workflow that allows them to concentrate on those areas that will reap the most benefit to the organization.
Role – Analyze and refine data for 100% accuracy and strategic use, act as expert, statistical expert.
Benefits – Focus on strategic issues with fewer day-to-day requests, collaborate on projects that require data refinement for 100% accuracy, focus on mature modeling requirements.
When an organization sets out to leverage the Citizen Data Scientist approach, it can ensure success by taking the time to plan appropriately AND by establishing appropriate expectations for how and when business users will engage in analytics and the results they can and should produce. When an organization understands the true meaning and purpose of the Citizen Data Scientist role, it can incorporate this strategy and align business users and Data Scientists to achieve greater collaboration and synergy.
Be sure you choose a vendor with comprehensive augmented analytics features and functionality designed specifically for business users, to support the transition of your business users to Citizen Data Scientists and ensure that your project will succeed. Contact Us to find out how we can help you plan and achieve your goals. It really IS possible!
