How Intelligent is your artificial intelligence?
Like most organizations, you've probably invested in Data Science. Perhaps you've hired a Data Science team or executed a number of use cases. You may have even taken the next step and embedded analytics into core business processes or created new products and services using analytics. Some of you are considering where you should begin your Data Science journey.
Regardless of where you are on the path, Increase Alpha can help. From articulating the art of the possible to the executive team and business leaders to validating the algorithms and data quality, we have the experience and perspective to help you on your journey. Take a look at our services below to see how we can help you.
Regardless of where you are on the path, Increase Alpha can help. From articulating the art of the possible to the executive team and business leaders to validating the algorithms and data quality, we have the experience and perspective to help you on your journey. Take a look at our services below to see how we can help you.
Executive vision
Where is your organization going?
The #1 reason that analytics projects fail has nothing to do with data, technology, or the availability of talented data scientists. No, the primary reason that they fail is lack of executive vision as to what analytics can truly deliver. We can think of this as the “Moneyball Effect.” It’s extremely difficult for executives who have run businesses for years and have been told time and time again that there are systems and data issues that prevent basic questions from being answered to suddenly than accept that an algorithm that they don’t understand and that no one can explain would generate valuable insights about strategy or customer behavior. This is as much a shortcoming of the technology and analytics teams as it is of the executive teams. However, executives who do understand what analytics can, and more importantly can’t, do are able to generate and deliver performance far in excess of their peers.
We will spend the time to assess the level of understanding of analytics of the leaders of the potential or acquired investment. There may be valid reasons why analytics have or have not been adopted and through our decades of consulting experience working with C-level executives, we will uncover them.
The #1 reason that analytics projects fail has nothing to do with data, technology, or the availability of talented data scientists. No, the primary reason that they fail is lack of executive vision as to what analytics can truly deliver. We can think of this as the “Moneyball Effect.” It’s extremely difficult for executives who have run businesses for years and have been told time and time again that there are systems and data issues that prevent basic questions from being answered to suddenly than accept that an algorithm that they don’t understand and that no one can explain would generate valuable insights about strategy or customer behavior. This is as much a shortcoming of the technology and analytics teams as it is of the executive teams. However, executives who do understand what analytics can, and more importantly can’t, do are able to generate and deliver performance far in excess of their peers.
We will spend the time to assess the level of understanding of analytics of the leaders of the potential or acquired investment. There may be valid reasons why analytics have or have not been adopted and through our decades of consulting experience working with C-level executives, we will uncover them.
Analytics Strategy
How will you get there?
If there is an executive vision for analytics, has this been converted into a well thought out and achievable strategy? Has a roadmap been constructed that takes into account the market opportunity, data availability, organizational design, level of investment and return on investment? Have specific use cases been identified and executed to test the viability of the strategy? Can analytics be scaled and operationalized?
These are the questions we will be ask along with many others to ascertain the viability of an investment’s analytics strategy.
You can rest assured that if they have done the proper upfront work, they are more likely to succeed in the analytics ventures. However, far too often analytics strategies consist of hiring a number of data scientists, giving them access to data sets and then sitting back expecting some magical insights to emerge. This is not a strategy, this is a fallacy.
If there is an executive vision for analytics, has this been converted into a well thought out and achievable strategy? Has a roadmap been constructed that takes into account the market opportunity, data availability, organizational design, level of investment and return on investment? Have specific use cases been identified and executed to test the viability of the strategy? Can analytics be scaled and operationalized?
These are the questions we will be ask along with many others to ascertain the viability of an investment’s analytics strategy.
You can rest assured that if they have done the proper upfront work, they are more likely to succeed in the analytics ventures. However, far too often analytics strategies consist of hiring a number of data scientists, giving them access to data sets and then sitting back expecting some magical insights to emerge. This is not a strategy, this is a fallacy.
Algorithm Quality
Is it correct?
How would you know if the algorithms developed by an investment target were built correctly? Would you know what questions to ask or even where to start looking? These algorithms are often developed by highly skilled and intelligent mathematicians but they represent the ultimate black box. Only a select few understand how the box works and very few of those individuals can explain it to lay people. In order to understand the black box, you have to either take it apart and examine its inner workings or you can attempt to understand the process in which it was built and more importantly tested. For even if it was built and tested correctly, is it being maintained and modified correctly? Does it deliver the expected results and can you be reasonably confident that it is of high quality?
The added advantage of examining the people and processes is that it provides a level of understanding as to the scalability of analytics within the organization. It will provide insight as to whether analytics is a true core competency or if it was a one hit wonder.
How would you know if the algorithms developed by an investment target were built correctly? Would you know what questions to ask or even where to start looking? These algorithms are often developed by highly skilled and intelligent mathematicians but they represent the ultimate black box. Only a select few understand how the box works and very few of those individuals can explain it to lay people. In order to understand the black box, you have to either take it apart and examine its inner workings or you can attempt to understand the process in which it was built and more importantly tested. For even if it was built and tested correctly, is it being maintained and modified correctly? Does it deliver the expected results and can you be reasonably confident that it is of high quality?
The added advantage of examining the people and processes is that it provides a level of understanding as to the scalability of analytics within the organization. It will provide insight as to whether analytics is a true core competency or if it was a one hit wonder.
Process Maturity
Can it be done again?
Is developing analytics a true core competency of the organization? Is their growth and financial success the result of a few talented staff or do they have a repeatable and scalable process on which to build? Talented data scientists really are as unique and rare as purple flying unicorns. They are often astoundingly intelligent, insightful, and gifted. They are able to create insights out of landfills of data. They can be a tremendous asset to a firm. But they can also be a single point of failure.
That is unless the organization was proactive and developed a set of repeatable processes around the analytics efforts that enable the flexibility and creativity necessary for the solutions to be developed while simultaneously ensuring the proper rigor exists in analysis, testing, and deployment. Achieving this level of process maturity is not easy as all too often many of these tasks reside with one or a few individuals. However, once an organization has achieved the appropriate level of maturity, the team is able to deploy its capabilities throughout the enterprise.
Our proprietary Data Monetization Model focuses specifically on the level of process maturity that exists within a firm to understand how well it is constructed and how repeatable and scalable it is.
Is developing analytics a true core competency of the organization? Is their growth and financial success the result of a few talented staff or do they have a repeatable and scalable process on which to build? Talented data scientists really are as unique and rare as purple flying unicorns. They are often astoundingly intelligent, insightful, and gifted. They are able to create insights out of landfills of data. They can be a tremendous asset to a firm. But they can also be a single point of failure.
That is unless the organization was proactive and developed a set of repeatable processes around the analytics efforts that enable the flexibility and creativity necessary for the solutions to be developed while simultaneously ensuring the proper rigor exists in analysis, testing, and deployment. Achieving this level of process maturity is not easy as all too often many of these tasks reside with one or a few individuals. However, once an organization has achieved the appropriate level of maturity, the team is able to deploy its capabilities throughout the enterprise.
Our proprietary Data Monetization Model focuses specifically on the level of process maturity that exists within a firm to understand how well it is constructed and how repeatable and scalable it is.
Data Cleansing
Its dirty work, but it needs to be done
Before the self driving car can hit the road, before an artificial intelligent powered chat bot can replace your customer service teams, indeed even before a sophisticated dashboard is built, the data that powers all of these solutions must be clean. The vast majority of companies do not have clean data and this has become a significant hurdle for the adoption of even basic intelligent analytics.
This is one of the reasons we exist. We’ve spent over 20 years working with many of the leading firms and we came to the realization that data quality issues existed almost every time and simultaneously that it was a very difficult problem to solve. There was no technology that could simply be thrown at the problem to fix it. It would take a combination of technical skill, domain knowledge, and creative thinking to solve this messy problem and that’s what we did. Our proprietary process profiles your data, identifies the issues and then enables the remediation of the issue at the lowest possible level and then identifies the root cause of the issue so that it too can be rectified so that the problem is solved permanently.
We don’t try to boil the ocean as we know that this is why projects fail or never get off the ground. We will work with your team to craft and execute a carefully constructed plan that has a high likelihood of success.
We know that data cleansing is not a fun or glamorous task. And frankly, we don’t care. Because what we do know is that without high quality, clean data, the true power and potential of analytics will never be realized.
Before the self driving car can hit the road, before an artificial intelligent powered chat bot can replace your customer service teams, indeed even before a sophisticated dashboard is built, the data that powers all of these solutions must be clean. The vast majority of companies do not have clean data and this has become a significant hurdle for the adoption of even basic intelligent analytics.
This is one of the reasons we exist. We’ve spent over 20 years working with many of the leading firms and we came to the realization that data quality issues existed almost every time and simultaneously that it was a very difficult problem to solve. There was no technology that could simply be thrown at the problem to fix it. It would take a combination of technical skill, domain knowledge, and creative thinking to solve this messy problem and that’s what we did. Our proprietary process profiles your data, identifies the issues and then enables the remediation of the issue at the lowest possible level and then identifies the root cause of the issue so that it too can be rectified so that the problem is solved permanently.
We don’t try to boil the ocean as we know that this is why projects fail or never get off the ground. We will work with your team to craft and execute a carefully constructed plan that has a high likelihood of success.
We know that data cleansing is not a fun or glamorous task. And frankly, we don’t care. Because what we do know is that without high quality, clean data, the true power and potential of analytics will never be realized.