Author: Smarten

Embedded BI Provides Insight Into Enterprise Data!

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March 26, 2024

Embedded BI Can Provide Numerous Benefits to the Team and the Enterprise!

When a business invests in technology, either to buy a best-of-breed solution or to develop their own enterprise apps, there is significant expense. If you are developing your own solution, you are using in-house resources or engaging IT consultants. If you are purchasing solutions off the shelf, you may or may not need customization but, in any case, the solution will cost you quite a bit of money and it will take more funding to support and maintain and integrate that solution.

Continue reading “Embedded BI Provides Insight Into Enterprise Data!”

Key Influencer Analytics Tells You How to Succeed!

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March 19, 2024

Use Key Influencer Analytics to Understand What Factors Impact Success!

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:

  • The type of data you want to analyze – Understanding the data type can help you decide whether you need to consider a binary approach or look at categories, etc.
  • The character of the data you want to analyze – Are you looking at product attributes, a specific threshold or data range, etc.
  • What you want to accomplish with your analysis – Do you want to identify trends or patterns or are you trying to understand the relationships among the various factors and which factors affect success?
Key Influencer Analytics Helps You Understand Success

…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:

  • Identify feature importance based on machine learning algorithms
  • Interpret insights in simple language
  • Measure statistics
  • Reveal influencers with impact on the target
  • Auto recommend influencers
  • Identify data relationships with interactive visualization

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.

Original Post: Key Influencer Analytics Tells You How to Succeed!

What Citizen Data Scientists DO NOT Need in Augmented Analytics

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March 13, 2024

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.

What Citizen Data Scientists DO NOT Need in Augmented Analytics

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.

AUGMENTED ANALYTICS SOLUTIONS

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.

  • Self-Serve Data Preparation
  • Assisted Predictive Modeling
  • Smart Data Visualization
  • Machine Learning and Natural Language Processing (NLP)
  • Clickless Search Analytics

EXPECTATIONS AND RESULTS

Once you have chosen the right augmented analytics solution, you must establish appropriate expectations.

Optimize Citizen Data Scientists And Data Scientists

MANAGERS AND EXECUTIVES SHOULD NOT EXPECT CITIZEN DATA SCIENTISTS TO BE DATA SCIENTISTS

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.

CITIZEN DATA SCIENTISTS SHOULD FOCUS ON HYPOTHESIS AND PROTOTYPING

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.

DATA SCIENTISTS SHOULD FOCUS ON STRATEGIC GOALS AND DATA REFINEMENT

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!

Original Post: What Citizen Data Scientists DO NOT Need in Augmented Analytics!

Augmented Analytics with Auto Insights Support Business Users!

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March 7, 2024

Augmented Analytics with ALL Gartner Classified Essential Components AND Auto Insights Too!

Gartner classifies essential augmented analytical components and technologies as follows:

  • Machine learning
  • Natural Language generation and natural language processing
  • Automation

‘Combine the three essential components specified by Gartner to create a comprehensive augmented analytics solution with refined auto insights and clear, concise results, the enterprise and its business users can perform complex data analytics and share analysis across the organization in a self-serve, mobile environment.’

While none of these is considered ‘new’ in the market today, the combination of essential components and the leveraging of new technologies and features is key to keeping augmented analytics fresh and usable for the average business user.

One such feature that users will look to for quick results and clear, concise decision support is the concept of auto insights.

Auto Insights Adds Clarity and Ease-of-Use to Augmented Analytics

Search Analytics is evolving at a rapid pace, and the concept of auto insights builds on the foundation of assisted predictive modeling and Clickless Analytics features, taking natural language processing (NLP) search analytics and predictive modeling to the next level.

Auto Insights frees business users and reduces the time and skills required to produce accurate, clear results, quickly and dependably, using machine learning that frees the business user to collect and analyze data with the guided assistance of a ‘smart’ solution.

Using this approach, business users will no longer have to select data columns or analysis techniques such as classification or clustering. Instead, the user will simply select the dataset to be analyzed. That’s it!. The system interprets the dataset, selects important columns of data, analyzes type and variety and other parameters and then uses intelligent machine learning to automatically apply the best algorithm and analytical technique and provide data insights.

Users can easily understand data and apply correlation, classification, regression, or forecasting, or other appropriate technique(s) based on the data the user wishes to analyze. Results are displayed using visualization types that provide the best fit for the data, and the interpretation is presented in simple natural language. This seamless, intuitive process enables business users to quickly and easily select and analyze data without guesswork or advanced skills.

By combining the three essential components specified by Gartner to create a comprehensive augmented analytics solution with refined insights and clear, concise results, the enterprise and its business users can perform complex data analytics and share analysis across the organization in a self-serve, mobile environment.

This approach provides support for sophisticated, advanced analytics and smart data visualization to automate the analysis process, so business users can simply point to a dataset and let the system do the rest.

‘The combination of essential components and the leveraging of new technologies and features is key to keeping augmented analytics fresh and usable for the average business user.’

Find out how Smarten Auto Insights can help your team and your organization. Leverage the essential components of Augmented Analytics, as defined by Gartner, and provide seamless, easy-to-use, sophisticated features and functionality to support your business users and transition your team members to Citizen Data Scientists.

Original Post: Augmented Analytics with Auto Insights Support Business Users!

Citizen Data Scientists Need These 3 Things to Succeed!

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March 5, 2024

3 Primary Components for Citizen Data Scientist Success!

The Citizen Data Scientist phenomenon is in full swing and, while the approach has its detractors, the proof is in success, and many organizations are actively succeeding using the Citizen Data Scientist approach.

Gartner has predicted that, in the future ‘…40% of data science tasks will be automated, resulting in increased productivity and broader usage by citizen data scientists.’

‘The enterprise does not expect to hire a legion of data scientists to perform analytics for every day-to-day need within the organization.’

There are many benefits of transitioning business users to a Citizen Data Scientist role, including:

  • Improved data literacy
  • Increased Data Democratization
  • Improved Collaboration
  • Increased Productivity
  • Improved Alignment with Goals and Objectives
  • Optimization of Data Scientist and IT Resources
  • …and more!
3 Keys to Citizen Data Scientist Success

There are many factors and components inherent in the success of a Citizen Data Scientist. If you are a Citizen Data Scientist candidate, there are three primary components of success:

  1. Organizational Commitment and Support – A business cannot just say they are committed to the Citizen Data Scientist approach. It must plan carefully and include a complete review of the current workflow, business processes and technology in order to make the changes required to support the new program. Citizen Data Scientists must be supported with revisions to performance evaluations and promotions. These revisions should encourage and enable the use of augmented analytics and collaboration so that business users are rewarded for acquiring and using new skills.
  2. Appropriate Augmented Analytics Tools – As with any other type of position or job, a Citizen Data Scientist needs the right tools to succeed. Without the right analytics tools, business users cannot make a successful transition to a Citizen Data Scientist role. The enterprise does not expect to hire a legion of data scientists to perform analytics for every day-to-day need within the organization but, if business users are expected to perform analytical activities, they will need easy-to-use tools that are sophisticated enough to achieve results, without requiring complex analytical skills or lengthy training. Augmented Analytics tools that are designed for business users should provide a foundation of machine learning and natural language processing (NLP) so search analytics is as easy as asking a question in a Google-type interface, with features like Smart Data Visualization, Assisted Predictive Modeling and Self-Serve Data Preparation.
  3. Curiosity and the Willingness to Explore – A prospective Citizen Data Scientist should have at least an average technology capability, with above average curiosity and a willingness to learn and collaborate. The ideal candidate should be recognized as someone who interacts well with others, and is willing to mentor others and help them become comfortable with new tools and processes.

‘If you are a Citizen Data Scientist candidate, there are three primary components of success.’

These are just a few of the factors you must consider when implementing a Citizen Data Scientist approach. Business users who are interested in becoming a Citizen Data Scientist must be willing to embrace new technology and tools and working at the leading edge of a new approach to collaboration and decision-making. initiative. Consider engaging an expert for your Citizen Data Scientist. IT consultants with experience and skill in this area can provide crucial support to help you succeed with your Citizen Data Scientist initiative and can provide simple Training Programs to bring your team on board and help them see the value to themselves and to the organization.

Original Post: Citizen Data Scientists Need These 3 Things to Succeed!

Extend Your Business Reach with Mobile BI!

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February 27, 2024

Support Your Team and Achieve Business Results with a Mobile BI Solution!

Gone are the days when every employee in your enterprise was tied to a desk inside the walls of your office building. Today’s team members are working remote, or visiting clients, or on the road, or perhaps they are in the office in a staff meeting and in need of crucial information to make a decision. Whatever the case, your team needs flexible, mobile solutions to do their jobs in today’s high-stakes, competitive environment, and mobile BI is definitely among the solutions they need.

‘If you want to support your business user team, explore mobile augmented analytics and mobile BI, you can add powerful functionality and access for your business users with out-of-the-box Mobile BI.’

Mobile BI solutions provide data for analysis on mobile devices to support objective, fact-based decision-making improve overall business performance.

Mobile BI Offers Numerous Benefits to Users and the Enterprise

According to Mordor Intelligence, ‘The growth in the adoption of mobile BI can be attributed to numerous factors, such as the growing adoption of data analytics, an increase in mobile data generation, proliferation of mobile devices and apps, and the improved efficiency of BI tools.’

There are many benefits to creating a Mobile BI environment:

  • Your users can leverage a native app, with a seamless user interface for a great user experience (Ux)
  • The enterprise can extend the office environment, allowing for swift analysis and decisions from anywhere and improving productivity
  • Mobile BI will encourage user adoption and provide support for BI investments and data democratization and improved data literacy
  • The organization can ensure data security with access rights defined on the server, so security and privacy is ensured at all levels
  • The mobile BI app is supported from anywhere within IT infrastructure –on premises, public or private cloud
  • Business Users have access to dashboards, reports, Clickless Analytics – Google-type Natural Language Processing (NLP) Search functionality

Whether you have an existing business intelligence or augmented analytics platform that you wish to upgrade or change, or you are exploring the potential of augmented analytics for your business user team and want to implement a Mobile BI environment to support your team within and outside the office, you can enjoy the benefits of this approach, with a cost-effective solution that is easy to implement and requires minimum training and roll-out time.

‘Your team needs flexible, mobile solutions to do their jobs in today’s high-stakes, competitive environment, and mobile BI is definitely among the solutions they need.’

If you want to support your  business user team, explore Smarten Mobile Augmented Analytics And Mobile BI, you can add powerful functionality and access for your business users with out-of-the-box Mobile BI and advanced analytics for every team member. For more information on Mobile BI and Augmented Analytics, read our article, ‘Understanding The Truth About Mobile BI.’

Original Post: Extend Your Business Reach with Mobile BI!

White Paper – Generative AI (GenAI): The Benefits and Application of AI in Analytics

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December 15, 2023

White Paper – Generative AI (GenAI): The Benefits and Application of AI in Analytics

If your enterprise wishes to consider AI in analytics, and plan for its future potential and growth, it is wise to first understand the state of the technology today, the various ways in which AI can inform and improve analytics for your business, the factors you will need to consider to choose the right analytics solution and the things your business should include in its vendor and solution review.

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Understanding BI Tools in Today’s Market

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November 17, 2023

How Do We Define Business Intelligence Today?

Business Intelligence (BI) is the lifeblood of an organization. Without business intelligence, the enterprise does not have an objective understanding of what works, what does not work, and how, when and where to make changes to adapt to the market, its customers and its competition.

You may be interested to know that TechJury reports seven out of ten businesses rate data discovery as very important, and that the top three business intelligence trends are data visualization, data quality management and self-service business intelligence.

As the Business Intelligence solution market evolves, it may be difficult for an organization to know when to invest in these tools, and which tools are best for enterprise and user needs.

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Should I Start a Citizen Data Scientist Program?

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October 4, 2023

Is it the Right Time for My Business to Initiate a Citizen Data Scientist Program?

Whether you are a business owner, a business executive or a business manager, or you just like to keep up with industry trends, you no doubt have read about the transition of business users to Citizen Data Scientists. The topic has been in industry journals and publications for years, and it is still relevant today.

Continue reading “Should I Start a Citizen Data Scientist Program?”