There is a fundamental flaw in information technology, or at least in the way it is most commonly delivered. Most technology systems are developed under the assumption that all people will use the system primarily in the same way. Sure, there are some options built in — perhaps the same action can be initiated by either clicking on a button, selecting a menu item or invoking a keyboard short-cut. The problem is that when every variation needs to be coded into the system, the prospect of providing personalized software programs to every individual is impractical.
Some personalization is relatively easy to accomplish. For example, abstracting some operations to be data-driven, such as lists of recent files or activities, can easily be tracked by an individual. Home page selections and arrangements can be retained and applied for each user. Applications can implement bookmarks and allow people to save them. Another way to personalize is via alerts and notifications to keep individuals informed on topics of interest. However, our Analytics and Data Benchmark Research shows that, despite its relative simplicity, only 1 in 5 organizations (20%) has completely adequate technology to accomplish alerts and notifications. Vendors could also ask individuals a few questions to learn more about what is important to them, what types of metrics do they like to see and what is the scope of their responsibility.
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But as an industry, we can do better. The advent of artificial intelligence and machine learning (AI/ML) has changed the way we can think about and affect personalization. We can use heuristics and AI/ML to recognize who people are, what they do and how they work. We can determine with whom they are similar and use those patterns as well to help personalize the interactions to be more tailored to them. We can observe what they’ve done most often in the past and where they have gotten stuck or hit roadblocks. We can observe whether they are in the office, at home, or at a client’s location. We can observe what time of day it is; whether they are on a mobile phone, a tablet or a computer. All this information can be useful in providing a more personalized experience.
In the context of analytics and data processes, there are many opportunities to get smart about personalizing. For example, when starting a new analysis, the suggested data sources could be narrowed or prioritized based on an individual’s role and their past activity. If I am part of sales operations, I am most likely going to be working with sales data. If I am in procurement, I am likely to be working with purchase orders and invoices. If I am in human resources, I am likely to be working with employee data. There is no reason to present every worker with an exhaustive list of all the data sources in the organization. Of course, there should be an option to access the full list, as needed, but in most cases it will be easier for individuals to execute specific job functions if they are only presented with the data sources they work with most often or have used most recently. This could help speed up the process of data preparation, which is the most common time-consuming task in analytics as reported by over two-thirds (69%) of organizations.
When creating analyses, information about an individual’s past selections can be used to determine preferences for types of tables and charts and how they should be formatted. Information about with whom the analyses are shared can also influence how they are formatted and what information they should contain. Our research shows nearly one-half (46%) of participants said that preparing reports and documents for analytics processes is one of the tasks that requires the most time. Personalization could help reduce that demand. We assert that by 2024, one-half of organizations will realize their analytics are ineffective without AI/ML to interpret and guide the workforce to personalized actions necessary that improve outcomes.
The objective is to provide much more personalized guidance to individuals as they interact with data and analytics. Unfortunately, most organizations are beholden to the application vendors they work with to implement these capabilities. I encourage buyers to seek out products and vendors that are with more personalization features. And if a selected vendor is not offering much personalization, find out what their plans are and, if necessary encourage them to include more. Personalization will improve the bottom line with better-informed decisions, executed more efficiently, and it will increase workforce satisfaction.
Regards,
David Menninger
There is a fundamental flaw in information technology, or at least in the way it is most commonly delivered. Most technology systems are developed under the assumption that all people will use the system primarily in the same way. Sure, there are some options built in — perhaps the same action can be initiated by either clicking on a button, selecting a menu item or invoking a keyboard short-cut. The problem is that when every variation needs to be coded into the system, the prospect of providing personalized software programs to every individual is impractical.
Some personalization is relatively easy to accomplish. For example, abstracting some operations to be data-driven, such as lists of recent files or activities, can easily be tracked by an individual. Home page selections and arrangements can be retained and applied for each user. Applications can implement bookmarks and allow people to save them. Another way to personalize is via alerts and notifications to keep individuals informed on topics of interest. However, our Analytics and Data Benchmark Research shows that, despite its relative simplicity, only 1 in 5 organizations (20%) has completely adequate technology to accomplish alerts and notifications. Vendors could also ask individuals a few questions to learn more about what is important to them, what types of metrics do they like to see and what is the scope of their responsibility.
But as an industry, we can do better. The advent of artificial intelligence and machine learning (AI/ML) has changed the way we can think about and affect personalization. We can use heuristics and AI/ML to recognize who people are, what they do and how they work. We can determine with whom they are similar and use those patterns as well to help personalize the interactions to be more tailored to them. We can observe what they’ve done most often in the past and where they have gotten stuck or hit roadblocks. We can observe whether they are in the office, at home, or at a client’s location. We can observe what time of day it is; whether they are on a mobile phone, a tablet or a computer. All this information can be useful in providing a more personalized experience.
In the context of analytics and data processes, there are many opportunities to get smart about personalizing. For example, when starting a new analysis, the suggested data sources could be narrowed or prioritized based on an individual’s role and their past activity. If I am part of sales operations, I am most likely going to be working with sales data. If I am in procurement, I am likely to be working with purchase orders and invoices. If I am in human resources, I am likely to be working with employee data. There is no reason to present every worker with an exhaustive list of all the data sources in the organization. Of course, there should be an option to access the full list, as needed, but in most cases it will be easier for individuals to execute specific job functions if they are only presented with the data sources they work with most often or have used most recently. This could help speed up the process of data preparation, which is the most common time-consuming task in analytics as reported by over two-thirds (69%) of organizations.
When creating analyses, information about an individual’s past selections can be used to determine preferences for types of tables and charts and how they should be formatted. Information about with whom the analyses are shared can also influence how they are formatted and what information they should contain. Our research shows nearly one-half (46%) of participants said that preparing reports and documents for analytics processes is one of the tasks that requires the most time. Personalization could help reduce that demand. We assert that by 2024, one-half of organizations will realize their analytics are ineffective without AI/ML to interpret and guide the workforce to personalized actions necessary that improve outcomes.
The objective is to provide much more personalized guidance to individuals as they interact with data and analytics. Unfortunately, most organizations are beholden to the application vendors they work with to implement these capabilities. I encourage buyers to seek out products and vendors that are with more personalization features. And if a selected vendor is not offering much personalization, find out what their plans are and, if necessary encourage them to include more. Personalization will improve the bottom line with better-informed decisions, executed more efficiently, and it will increase workforce satisfaction.
Regards,
David Menninger