Senior Manager at a tech services company with 1-10 employees
Reseller
Sep 25, 2021
Anyone who recommends software based solely upon the size of an organisation is clearly NOT a professional.
Augmented Analytics is a variant of analysis tools that utilise both AI (artificial intelligence) and ML (machine learning) to enhance, or rather augment, deeper understanding of the underlying data. Both AI and machine learning capabilities improve with data quality and quantity, and are focused towards particular tasks, using the most appropriate algorithms and methodologies.
Certain specific IBM technologies could be suitable for tasks involved within manufacturing environments for early recognition of potential faults or rejects. Whereas the Board International software platform is probably a more flexible solution for commercial datasets, seeking to identify "outliers" and trends towards better financial forecasting.
Whilst, the more comprehensive solutions are not necessarily the cheapest, it is still possible to gain benefits from more generic systems, subject to the level of reliance and criticality that is being demanded from the solution.
Firstly understand your business requirements, available datasets, and then scope needs.
Search for a product comparison in BI (Business Intelligence) Tools
Augmented analytics is the use of statistical and linguistic technologies (AI and NLP) to improve data management performance, from data analysis to data sharing and Business Intelligence.
In a certain way, it is related to the ability to transform the data warehouse or big data into smaller and more usable data sets in a flexible way.
The main focus of augmented analytics remains in its assistance role, where technology does not replace human teams, but supports them, improving their interpretation capabilities.
BI software with augmented analytics makes use of machine learning and NLP to understand and interact with data as humans would, but on a massive scale. The analysis process usually begins with the collection of data from public or private sources. Once the data is gathered, it must be prepared and analyzed to extract ideas, which must then be shared with the organization, along with action plans to do something with what has been obtained.
These tasks are typically performed by data scientists, who spend 80% of their time collecting and preparing data, and only the remaining 20% searching for information. The goal of augmented analytics is to automate your data collection and preparation processes to save you that 80% of your time. However, the real and ultimate goal of augmented analytics is to completely replace teams of data scientists with artificial intelligence, taking care of the entire analytics process, from data collection to business recommendations for managers' decision-making.
For example, you can imagine asking the augmented analytics tool to lookup opinions online about some of your products and tell you what you should improve to sell more, making the computer respond with a clear textual response and some strong graphics.
In this case, the number of employees of a company is not relevant at all, as is the quantity, quality and type of data as well as the questions that need to be answered.
I've not come across a great integrated solution. We use Power BI, but the AI/ML is limited, so need to supplement it with AutoML in Azure ML studio for more detailed prediction models. The Azure ML workspace is free for the first year, so worth looking into.
Director - Metrics & Analytics at a computer software company with 1,001-5,000 employees
Real User
Sep 27, 2021
Selection of Analytics Software is based on the requirement, Cost & user capability and not company size. There are different analytics tools in the market for different purposes/users. If the requirement is basic with good graphical representation capabilities then Power BI/Qlik are some of the good ones.
If the requirement is for end-users with minimal technical skills then AnswerRocket is a great tool.
AnswerRocket offers a search-powered data analytics platform designed for business users. The product enables you to ask business questions in natural language, and no technical skills are needed to run reports or generate analysis. AnswerRocket features a combination of AI and machine learning, as well as advanced analytic functionality. The platform can also automate manual tasks and answer ad hoc questions quickly. AnswerRocket is mobile-friendly and includes native voice recognition.
Cognos Analytics is another great tool with an expansive range of BI and analytics capabilities.
IBM offers an expansive range of BI and analytic capabilities under two distinct product lines. The Cognos Analytics platform is an integrated self-service solution that allows users to access data to create dashboards and reports. IBM Watson Analytics offers a machine learning-enabled user experience that includes automated pattern detection, support for natural language query and generation, and embedded advanced analytics capabilities. IBM’s BI software can be deployed both on-prem or as a hosted solution via the IBM Cloud.
Another great tool for BI and analytics is Pyramid Analytics.
Pyramid Analytics offers data and analytics tools through its flagship platform, Pyramid v2020. The solution touts a server-based, multi-user analytics OS environment that provides self-service capabilities. Pyramid v2020 features a platform-agnostic architecture that allows users to manage data across any environment, regardless of technology. The tool enables those users to prepare, model, visualize, analyze, publish, and present data from web browsers and mobile devices.
Recommendations for any client focus on requirements.
Early on, for NetSuite clients, Adaptive Planning was very tightly integrated with the core NetSuite platform and was billed as 'Advanced Financials' which worked very well for many clients at that point.
FP&A teams, evaluating Adaptive as a stand-alone, tended to select Host Analytics more often on a 1-1 comparison, and in the last couple of years, Anaplan has entered the picture (I mean they were there before, but the product development really kicked into high gear of late.)
But those decisions revolved around what challenges the FP&A teams faced.
Meanwhile, with the Oracle acquisition of NetSuite, Adaptive was de-emphasized and Oracle Planning and Budgeting, the former Hyperion product - now in a much easier to use, re-designed cloud version became the choice for more NetSuite clients. I think SAP bought Adaptive if memory serves - which typically means huge development dollars have been flowing into Adaptive for the last couple of years, we should expect more robust features soon.
There are still cases for pure BI solutions. A professional sports team needs to pull ERP numbers and analysis of CRM sales forecasting, and they also take Ticketmaster data, POS data from Team Shops and Concession stands (both are operated by partner companies) and now we want to scan tickets at the gate and provide real-time analytics against which of our season ticket holders show up for early VIP dining - Tableau works especially well.
Another sales team needs BI data deployed to mobile devices and chose Qlik View for that specific reason.
Microsoft Power BI works well - most of the lower performing instances are with companies that tried to implement it themselves (great if you have a couple of data scientists lounging around the office, disaster if you're relying on an IT staff unfamiliar with Power BI implementations.) The budget for top implementation teams usually undermines the cost advantages of Power BI - but you need to focus on why you're doing the project in the first place, not the cheapest price.
So what would we recommend for an augmented analytics software for an organization of 1000+ EE's? We'd recommend a thorough vetting of several well-known BI tools until one emerges as head and shoulders above the rest.
Caveat: Probably 7 of 10 evaluations end up with a product the lead users have past experience using - not always bad to start out with a staff experienced in what took them years to master - but it does lock you into yesterday's top technologies.
This is another tough question that may mislead some community members who lack some pre-knowledge about the market.
I would like to write my brief point just to contribute. There is none specific!
Between 2000 and 2005, we used to embrace "the best of breech" approach and I believe that after years, we have again come to a similar point. E.g., the one that is perfect with ML does provide for BA part or great visualizations, discovery, etc considering the fact that ultimate success relies on enterprise-wise embracement and usage within the fact that requirements, usage profiles, short-mid-long term expectations are always vital without question. While one technology/platform perfectly suits one enterprise, the other company may not even benefit from it at all.
On the other hand, the bests are being acquired by big guys which creates another question that is: which one is the best-integrated one?
To sum up, I strongly believe that there should be a certain key team, could be even a couple, who will act as digital augmented glue. Those will know the pros, and cons and guide the people in their decisions to prevent facing already known facts.
It seems that this will be a valid fact for a while. What will be left after usage of any of those platforms is algorithms or business value that your team will create., if you are lucky to have it at the end, you can always utilize it any other platform.
Senior Manager at a tech services company with 1-10 employees
Reseller
Oct 2, 2021
IBM Cognos Analytics are due to release the latest version that combines the capabilities of IBM Watson cognitive computing with Cognos BI and Analysis. This could be the start of the Augmented Analytics journey that both existing IBM clients and new Users are seeking.
Follwing the link and register for the IBM announcement that is scheduled for broadcast on October 5th to hear more: https://lnkd.in/dkxnAzUc
Contact LSA Solutions for more information, should your organization be considering an investment in BI Reporting and/or Augmented Analytics (with or without Machine Learning) at any time in the near future.
BI Tools transform raw data into actionable insights, enhancing decision-making through visualization and analysis. Enterprises gain a competitive advantage by employing these tools for effective data management. BI Tools analyze vast datasets, providing clear insights through dashboards and reports. Their functionality includes data preparation, visualization, and analytics for improved business decisions. Organizations use them to identify trends, predict outcomes, and optimize...
Anyone who recommends software based solely upon the size of an organisation is clearly NOT a professional.
Augmented Analytics is a variant of analysis tools that utilise both AI (artificial intelligence) and ML (machine learning) to enhance, or rather augment, deeper understanding of the underlying data. Both AI and machine learning capabilities improve with data quality and quantity, and are focused towards particular tasks, using the most appropriate algorithms and methodologies.
Certain specific IBM technologies could be suitable for tasks involved within manufacturing environments for early recognition of potential faults or rejects. Whereas the Board International software platform is probably a more flexible solution for commercial datasets, seeking to identify "outliers" and trends towards better financial forecasting.
Whilst, the more comprehensive solutions are not necessarily the cheapest, it is still possible to gain benefits from more generic systems, subject to the level of reliance and criticality that is being demanded from the solution.
Firstly understand your business requirements, available datasets, and then scope needs.
Augmented analytics is the use of statistical and linguistic technologies (AI and NLP) to improve data management performance, from data analysis to data sharing and Business Intelligence.
In a certain way, it is related to the ability to transform the data warehouse or big data into smaller and more usable data sets in a flexible way.
The main focus of augmented analytics remains in its assistance role, where technology does not replace human teams, but supports them, improving their interpretation capabilities.
BI software with augmented analytics makes use of machine learning and NLP to understand and interact with data as humans would, but on a massive scale. The analysis process usually begins with the collection of data from public or private sources. Once the data is gathered, it must be prepared and analyzed to extract ideas, which must then be shared with the organization, along with action plans to do something with what has been obtained.
These tasks are typically performed by data scientists, who spend 80% of their time collecting and preparing data, and only the remaining 20% searching for information. The goal of augmented analytics is to automate your data collection and preparation processes to save you that 80% of your time. However, the real and ultimate goal of augmented analytics is to completely replace teams of data scientists with artificial intelligence, taking care of the entire analytics process, from data collection to business recommendations for managers' decision-making.
For example, you can imagine asking the augmented analytics tool to lookup opinions online about some of your products and tell you what you should improve to sell more, making the computer respond with a clear textual response and some strong graphics.
In this case, the number of employees of a company is not relevant at all, as is the quantity, quality and type of data as well as the questions that need to be answered.
I personally recommend that you take a look at the Qlik Sense solution on the page: https://www.qlik.com/us/augmen...
I've not come across a great integrated solution. We use Power BI, but the AI/ML is limited, so need to supplement it with AutoML in Azure ML studio for more detailed prediction models. The Azure ML workspace is free for the first year, so worth looking into.
Selection of Analytics Software is based on the requirement, Cost & user capability and not company size. There are different analytics tools in the market for different purposes/users. If the requirement is basic with good graphical representation capabilities then Power BI/Qlik are some of the good ones.
If the requirement is for end-users with minimal technical skills then AnswerRocket is a great tool.
AnswerRocket offers a search-powered data analytics platform designed for business users. The product enables you to ask business questions in natural language, and no technical skills are needed to run reports or generate analysis. AnswerRocket features a combination of AI and machine learning, as well as advanced analytic functionality. The platform can also automate manual tasks and answer ad hoc questions quickly. AnswerRocket is mobile-friendly and includes native voice recognition.
Cognos Analytics is another great tool with an expansive range of BI and analytics capabilities.
IBM offers an expansive range of BI and analytic capabilities under two distinct product lines. The Cognos Analytics platform is an integrated self-service solution that allows users to access data to create dashboards and reports. IBM Watson Analytics offers a machine learning-enabled user experience that includes automated pattern detection, support for natural language query and generation, and embedded advanced analytics capabilities. IBM’s BI software can be deployed both on-prem or as a hosted solution via the IBM Cloud.
Another great tool for BI and analytics is Pyramid Analytics.
Pyramid Analytics offers data and analytics tools through its flagship platform, Pyramid v2020. The solution touts a server-based, multi-user analytics OS environment that provides self-service capabilities. Pyramid v2020 features a platform-agnostic architecture that allows users to manage data across any environment, regardless of technology. The tool enables those users to prepare, model, visualize, analyze, publish, and present data from web browsers and mobile devices.
Recommendations for any client focus on requirements.
Early on, for NetSuite clients, Adaptive Planning was very tightly integrated with the core NetSuite platform and was billed as 'Advanced Financials' which worked very well for many clients at that point.
FP&A teams, evaluating Adaptive as a stand-alone, tended to select Host Analytics more often on a 1-1 comparison, and in the last couple of years, Anaplan has entered the picture (I mean they were there before, but the product development really kicked into high gear of late.)
But those decisions revolved around what challenges the FP&A teams faced.
Meanwhile, with the Oracle acquisition of NetSuite, Adaptive was de-emphasized and Oracle Planning and Budgeting, the former Hyperion product - now in a much easier to use, re-designed cloud version became the choice for more NetSuite clients. I think SAP bought Adaptive if memory serves - which typically means huge development dollars have been flowing into Adaptive for the last couple of years, we should expect more robust features soon.
There are still cases for pure BI solutions. A professional sports team needs to pull ERP numbers and analysis of CRM sales forecasting, and they also take Ticketmaster data, POS data from Team Shops and Concession stands (both are operated by partner companies) and now we want to scan tickets at the gate and provide real-time analytics against which of our season ticket holders show up for early VIP dining - Tableau works especially well.
Another sales team needs BI data deployed to mobile devices and chose Qlik View for that specific reason.
Microsoft Power BI works well - most of the lower performing instances are with companies that tried to implement it themselves (great if you have a couple of data scientists lounging around the office, disaster if you're relying on an IT staff unfamiliar with Power BI implementations.) The budget for top implementation teams usually undermines the cost advantages of Power BI - but you need to focus on why you're doing the project in the first place, not the cheapest price.
So what would we recommend for an augmented analytics software for an organization of 1000+ EE's? We'd recommend a thorough vetting of several well-known BI tools until one emerges as head and shoulders above the rest.
Caveat: Probably 7 of 10 evaluations end up with a product the lead users have past experience using - not always bad to start out with a staff experienced in what took them years to master - but it does lock you into yesterday's top technologies.
This is another tough question that may mislead some community members who lack some pre-knowledge about the market.
I would like to write my brief point just to contribute. There is none specific!
Between 2000 and 2005, we used to embrace "the best of breech" approach and I believe that after years, we have again come to a similar point. E.g., the one that is perfect with ML does provide for BA part or great visualizations, discovery, etc considering the fact that ultimate success relies on enterprise-wise embracement and usage within the fact that requirements, usage profiles, short-mid-long term expectations are always vital without question. While one technology/platform perfectly suits one enterprise, the other company may not even benefit from it at all.
On the other hand, the bests are being acquired by big guys which creates another question that is: which one is the best-integrated one?
To sum up, I strongly believe that there should be a certain key team, could be even a couple, who will act as digital augmented glue. Those will know the pros, and cons and guide the people in their decisions to prevent facing already known facts.
It seems that this will be a valid fact for a while. What will be left after usage of any of those platforms is algorithms or business value that your team will create., if you are lucky to have it at the end, you can always utilize it any other platform.
IBM Cognos Analytics are due to release the latest version that combines the capabilities of IBM Watson cognitive computing with Cognos BI and Analysis. This could be the start of the Augmented Analytics journey that both existing IBM clients and new Users are seeking.
Follwing the link and register for the IBM announcement that is scheduled for broadcast on October 5th to hear more: https://lnkd.in/dkxnAzUc
Contact LSA Solutions for more information, should your organization be considering an investment in BI Reporting and/or Augmented Analytics (with or without Machine Learning) at any time in the near future.