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.
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.
Whether your business wishes to implement its first business intelligence solution, or you wish to upgrade your solution to satisfy additional requirements, or your business divisions have diverse business needs, the RIGHT business intelligence and augmented analytics solution is crucial. To protect your Return on Investment (ROI) and reduce your Total Cost of Ownership (TCO), you must select a solution that will suit all your team members, and be flexible enough to grow with your organization and provide support now and in the future.
Gartner names 12 mandatory and common features of a comprehensive BI tool, which includes:
Data visualization
Governance
Reporting
Analytics Catalog
Data preparation
Data science integration
Automated insights.
Metrics layer
Data storytelling
Natural language query (NLQ)
Collaboration
Composability
For many businesses today, the decision can lead them down a winding path to the question: ‘Can I truly find one solution to suit all my needs and, if I can’t, is it possible to successfully combine and integrate more than one BI tool?’
Let’s take a closer look at these questions in an effort to help you understand the tradeoffs and the decision-making process.
Choosing ONE BI Tool
Benefits
Data Centralization – Team members can seamlessly access a single data solution
Provides a centralized, simplified platform for user management and access rights
Maintenance and Administration – The IT team has only one system to manage
Reporting – Dashboards and reports leverage one data model, and reporting formats and scenarios are uniform and interconnected
Ensures one focused, experienced IT and technical team
Challenges
Scalability – As your organization grows, you may have issues supporting an expanded user base and data volume
Performance Issues – For organizations with a large data volume, users may experience a lag in response time
Data Security – If your business has multiple business units, the administration of a single solution may be a challenge, as the model will require multi-layered, granular security and permissions
Constrains the business to one solution, with one roadmap for the future provided by one vendor
Choosing Multiple Solutions
Benefits
Scalability – Smaller, more modular solutions allow the organization to execute development independently without affecting other systems
Performance – User experience (Ux) for dashboards and reporting is likely to be better, with suitable speed and responsive dashboards and reporting
Support – Each independent module or solution can be supported independently without affecting other solutions
Challenges
Duplicate Data or Analytics – If proper architecture is not in place, the systems may duplicate data or analysis or a system may use the wrong model for reporting
Integration – Combining and integrating multiple systems requires a sophisticated roadmap to accommodate cross-functional reporting and analytics and data access
Continuity – Data mapping, distribution and consistency can be a challenge
User Management and Access Rights – It can be challenging to manage these processes across disparate platforms
Technical and IT – Multiple tools require expertise across all frameworks and platforms
These are just a few of the examples of benefits and challenges of the one vs. many approach of business intelligence solutions. While the temptation to consolidate and choose one tool can seem practical, the enterprise should consider the benefits and the challenges of each approach and compare them to their needs. Each tool may offer specific benefits that cannot be achieved by a one solution approach, and may better address user expectations and needs and satisfy the goals of the organization.
For a long time, the analytics industry has touted the idea of ‘best of breed,’ and there is a case to be made for choosing the ‘best’ for each scenario. Gartner’s most recent Magic Quadrant places 6 of 20 vendors in the niche quadrant and many of these niche vendors may offer solutions that meet a specific need within your own enterprise. So, don’t be too quick to eliminate the possibility of multiple solutions.
Many CIOs think that at standardization on one platform should always be considered at the enterprise level. While that approach has its advantages, it has lot of disadvantages too.
Advantages include:
One vendor, one relationship
Concentrated skill set within the team
A simple, uncluttered application landscape
Disadvantages include:
Commitment to one vendor (who may or may not be dependable)
Enterprise is confined to one vendor roadmap, limitations, upgrades and future development
One solution platform limits enterprise platform choices and flexibility
One tool may not satisfy niche or specific use cases across all divisions, departments and teams
While a one solution choice may work for a smaller organization or one without complex needs, you may wish to consider a balanced approach. When you strike a balance using a practical, limited best of breed approach, you can address the needs of specific business units or users. Be judicious about your choices, so as not to clutter the application landscape with too many applications and complicate your architecture.
You may wish to choose one solution for enterprise IT reporting, or one for smart data visualization for business users, or one that supports Citizen Data Scientists, or a tool that is focused on big data processing. Your assessments should be based on your use cases, user needs, license needs, budget, etc. While some organizations use as many as five (5) BI tools, the enterprise should limit their expansion to 2-3 tools in order to avoid chaos in integration, maintenance, the need for IT skills, costs etc.
While the assessment of needs and requirements for ANY software solution can be time consuming, it does pay off in the end and, especially if you are considering multiple BI tools, it is worth taking the time to thoroughly assess your needs so your business can create the ideal analytical landscape for IT, data scientists, business analysts and business users.
Self-Serve, Augmented Analytics IS Suitable for Data Scientists
The world of data scientists and business analysts is chock full of data and busier than you might expect – especially today! Businesses have discovered the value of data in decision-making and, as markets and competition shift and change, these businesses have come to depend on IT staff and on data scientists to provide data to make decisions at the department, divisional, operational and strategic level.
The problem is, as always…TIME! There aren’t enough analysts and data scientists, the IT team is busy working on other tasks and the clock does not stop ticking.
And there is one other factor at play in the data analytics movement. As data democratization and data literacy drive the enterprise strategy and business users begin to leverage augmented analytics and business intelligence (BI) tools, the data scientist is also called upon to refine and present analytics and reports created by team members in order to ensure that these are appropriate for more strategic decisions.
The world-renowned technology research firm, Gartner, states that, ‘Data Scientists typically spend more than 40% of time in preparing and enriching data.’
Imagine what you, as a data scientist could do with a data analytics solution that can save time on data preparation and data enrichment!
‘When an enterprise includes data scientists, business analysts and IT staff in the roll-out of augmented analytics and self-serve BI tools, it enables productivity, streamlines and speeds the analytical process and improves results.’
You have the tools and systems for data extraction, transformation and loading (ETL), you have scripting tools like R, you have spreadsheets and more, but using all of those tools to gather, analyze, scrub and present that data takes time.
As a Data Scientist, you have the responsibility and accountability to produce reliable analytics to satisfy all manner of needs within the organization. You never know what corner of the enterprise might need your services but you DO know that your analysis and services must be 100% accurate and dependable. When business users depend on you to produce information on a day-to-day basis, it is nearly impossible to focus on the more strategic, crucial imperatives.
What if you could more easily derive data from disparate sources and prepare it for analysis?
What if you could integrate R scripting with advanced analytical tools to take your analysis to the next level?
What if you can use quick hypothesis and prototyping to choose the right influencers and model accuracy for your project?
What if you have a platform where you can roll out interactive dashboards, reports and results of your model production environment in minutes?
When an enterprise includes data scientists, business analysts and IT staff in the roll-out of augmented analytics and self-serve BI tools, it enables productivity, streamlines and speeds the analytical process and improves results. Data Scientists can optimize time and resources and to use their core expertise to achieve results. Data Scientists can use self-serve data preparation, to quickly create datasets without deep SQL or ETL skills, and they can use smart data visualization and tools to use output of algorithms in R, Python or other Data Science platforms to leverage existing investments in these technologies, and they can create and roll-out predictive models in a production environment to support the organization and business user needs.
‘Imagine what you, as a data scientist could do with a data analytics solution that can save time on data preparation and data enrichment!’
As business users create analytics for quick decisions and the organization needs to refine this analysis for strategic use, a data scientist can use the same augmented analytics tools to focus on that project and ensure accuracy, collaborating with Citizen Data Scientists and IT to align analytics with key objectives and goals.
With the time your Data Scientists will save, they can focus on the most critical strategic initiatives and move the enterprise forward with fact-based decision-making.
To learn more about Augmented Analytics that are suitable for data scientists, business analysts, IT staff and business users, and explore the potential of Advanced Reporting Tools, Contact Us now.
The Client is a prominent Indian construction and infrastructure company providing services in numerous sectors, including highway, rail, mining, energy, irrigation, and water supply. Known for their expertise in managing large-scale and complex projects, the Client has played a significant role in enhancing the India infrastructure and has achieved robust growth and financial stability. The Client is currently managing sixty two (62) projects and employs more than 5000 employees in numerous market sectors across India.
Gartner predicts that ‘organizations that offer users access to a curated catalog of internal and external data will derive twice as much business value from analytics investments as those that do not.’ In order to make the most of the data that resides in all the corners of your enterprise, you must make sense of that data, and leverage it to create products and services, to compete in your market of choice and refine your workflow and tasks to improve productivity and agility.
The key to making the most of your data is analytics, and the ability to gather and analyze data and produce results that are intuitive and clear. Machine learning has evolved to support the average business user with tools and techniques that make it easier to gather and analyze data using simple techniques that are supported by analytical techniques, without requiring business users to have data science skills.
‘Auto Insights analytics allows business users to select the dataset to be analyzed, and let the system do the rest.’
Today’s business users expect to have access to software solutions and techniques that are easy to understand and navigate. As consumers, they have the world at their fingertips, with simple techniques that require little to no technical knowledge. If your enterprise plans to adopt augmented analytics tools and business intelligence techniques, it must provide simple tools that are easy enough for the average business users to incorporate into workflow and tasks. This approach will help the organization achieve its analytical goals while ensuring an appropriate return on investment (ROI) and decreasing Total Cost of Ownership (TCO). It can also encourage and enable Citizen Data Scientist initiatives and improve data literacy.
The Auto Insights approach to analytics provides a foundation of Assisted Predictive Modeling and easy-to-use tools, making it simple enough for every business user to adopt as an important tool in the team toolkit, and supporting the path from business user to Citizen Data Scientist, with tools that encourage data literacy.
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.
Auto Insights allows business users to select the dataset to be analyzed, and let the system do the rest. The tool will interpret the dataset, select important columns of data, analyze its type and variety and other parameters and then use intelligent machine learning to automatically apply the best algorithm and analytical technique and provide data insight so the user can easily see and understand the results.
The Auto Insights concept is applied to data repositories to enable the enterprise and its business users to perform complex data analytics and share analysis across the organization in a self-serve, mobile environment, bringing the power of sophisticated, advanced analytics and smart data visualization to the members to automate and analyze, so enterprise users can quickly get the answers they need and move on to make confident decisions.
‘The key to making the most of your data is analytics, and the ability to gather, analyze and view data easily, and produce results that are intuitive and clear.’
If you are interested in finding out more about the advantages of the Smarten Auto Insights approach to Augmented Analytics and self-serve search analytics, Contact Us to explore how these techniques can help your enterprise apply analytics to achieve results. Let us help you realize your business goals and objectives with fact-based information.
How Can I Ensure Data Quality and Gain Data Insight Using Augmented Analytics?
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.
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?
…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.
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.
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.
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!
The Client is India’s largest omni-channel jewelry brand, and is recognized and renowned by India consumers. The Client has 165 retail stores in 66+ cities across India, as well as a thriving jewelry eCommerce presence online. Its product line includes rings, earrings, pendants, necklaces, chains, bangles, bracelets, mangalsutra, and nose pins, as well as 22k (916) and 24k (995) gold coins with certification and BIS Hallmark stamp guarantee. The Client customer base is growing rapidly, and to attract and retain customers, the business provides new designs and uses a mobile application to bridge the gap between brick-and-mortar stores and the virtual world. The mobile app provides a Virtual Try-On feature that allows customers to ‘try on’ jewelry and designs using a virtual reflection and image.
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