What we need is greater clarity of communication in explaining exactly how and why data capabilities deliver business value in language that can be understood by data engineers, data architects, data analysts, business analysts, and business leaders alike.
Forbes
Randy Bean
April 14, 2022
This is a republication of the article above, visually edited by the Editor of this site. Joaquim Cardoso MSc @ The Health Revolution Strategy
I hope we can all agree that investments in data capabilities and solutions must deliver business value.
Maybe this is stating the obvious, but it is not always clear to me that major corporations have clearly articulated their business goals and expected outcomes as it relates to investments in data capabilities before they make these investments.
How many times have I met with data and technology leaders and listened to enthusiastic endorsements of the data architectures and platforms they have implemented, …
… only to meet with business leaders who profess that they just don’t trust the data they are seeing …
— the quality of the data is questionable, the timeliness of the data is inadequate, the relevance of the data to the business questions that are most critical is questionable?
This may seem like a caricature of the current state of data in the business world today, but from my experience, exaggerated or not, there is truth to be found in this dynamic.
Most organizations continue to struggle to manifest the holy grail of becoming data-driven — just 26.5% of leading companies identify as being data-driven organizations.
… just 26.5% of leading companies identify as being data-driven organizations.
There is no question that efforts to capture, sanitize, organize, and analyze data require committed effort and expertise.
Companies have been working hard at this for decades, with evidently mixed results — even though data is an asset that flows through any business from production to consumption, just 39.7% of companies say they are managing data as a business asset.
… just 39.7% of companies say they are managing data as a business asset.
So, it is against this backdrop of an ongoing struggle to gain value from data, and to ensure that investments in data solutions, tools, and capabilities translate into some form of measurable business value, that …
… we witness ongoing perplexity as it relates to how to select the right tools and technologies in ways that deliver plain old-fashioned business value.
I recall the story from many years ago of a data executive who met with the corporate CEO to request a multi-million-dollar investment “to build a corporate MDM capability.”
The response of the CEO was to deny the investment, lecturing the data executive that until they could articulate the need in terms of business value and benefits, rather than “technology jargon”, no investment would be forthcoming.
When the data executive related this story to me, I asked with a straight face whether MDM meant master data management or metadata management.
The data executive looked at me like I had two heads. I asked because I wasn’t really sure.
The response of the CEO was to deny the investment, lecturing the data executive that until they could articulate the need in terms of business value and benefits, rather than “technology jargon”, no investment would be forthcoming.
This brings me to the state of data tools and technologies today.
We all know that data continues to proliferate, in both quantity and form.
We also know that the evolution of computing power has enabled companies to organize and analyze data to an extent that might have been unimaginable just a couple decades ago.
There are so many wonderful new data tools and capabilities available today, bringing value to data leaders and enabling the emergence of “citizen data analysts”.
In spite of this hard-won progress, the proliferation of technical data jargon continues to frustrate and sometimes antagonize business leaders, as well as sow confusion among data leaders.
Presumably, data literacy and data democratization must be good. How could they not be?
Clearly, we want data to be healthy, and it would be very helpful if we could understand what data it is that we have in our possession for purposes of analysis by having an accurate data catalog.
It would make sense that we have rules, policies, standards, practices, and procedures to manage data, so it would be helpful to have some form of data governance, though presumably not data fascism.
It shouldn’t come as a surprise to any thoughtful data leader that the growing lexicon of data tools and technologies might be off-putting to some business leaders, who want to cut through the clutter and cut to the chase —
… how will investments in data capabilities enable us to serve our customers better, expand into new markets, introduce new products and services, and use data as a competitive differentiator?
Isn’t this what it’s all about? Isn’t that why we are here?
As if we hadn’t enough data jargon, enter the new next big data things — data meshes and data fabrics.
I have no doubt that data meshes and data fabrics deliver wonderful features and advances over previous generations of data solutions.
However, why has the business of data become the business of branding and rebranding, rather than the delivery of clear business value?
Haven’t data meshes and data fabrics existed in previous forms and varieties for years, if not decades, known by a different terminology at the time.
When did data warehouses become data lakes? Why did DSS become EIS become BI become self-service analytics or whatever the successor solution is today? (You can look these acronyms up if you don’t know what they mean).
The names change, but hasn’t the fundamental value proposition remained the same — how can we make data more accessible to business analysts and business decision makers to make better and more informed business decisions?
When did data warehouses become data lakes? Why did DSS become EIS become BI become self-service analytics or whatever the successor solution is today?
Data technologies and solutions naturally experience short lifetimes. It should come as no surprise that capabilities evolve rapidly given the dynamic of data — ongoing proliferation of data and ever-increasing computing power.
Data should be a tool for transformation and business change.
No wonder then that in just the past two decades, we have seen organizations migrate from relational databases to solutions like Netezza, and then migrate from Netezza to Hadoop, and now migrate from Hadoop to Snowflake, or whatever.
No wonder then that in just the past two decades, we have seen organizations migrate from relational databases to solutions like Netezza, …
and then migrate from Netezza to Hadoop, and now migrate from Hadoop to Snowflake, or whatever.
I expect that I will hear from the data technology experts, many of whom are my friends and readers, who will weigh in to point out the distinctions and nuances that I have obscured. I hope they do.
I hope to learn something.
My point is not to minimize advances in data technologies and solutions.
My point is that, when all is said and done, data leaders must do a much better job of articulating the value and benefits of these data capabilities and solutions in plain business language, in terms of business value and clear business benefits, if they ultimately want to be successful in building data-driven companies.
My point is that, when all is said and done, data leaders must do a much better job of articulating the value and benefits of these data capabilities and solutions in plain business language, …
… in terms of business value and clear business benefits, if they ultimately want to be successful in building data-driven companies.
We don’t need more data jargon.
What we need is greater clarity of communication in explaining exactly how and why data capabilities deliver business value in language that can be understood by data engineers, data architects, data analysts, business analysts, and business leaders alike.
Only then will we overcome the gap between data capabilities and delivery of meaningful business value that translates into outcomes such as improved customer service and successful entry into new markets.
Only then will business leaders no longer feel compelled to say, “we don’t understand what it is your talking about”, or “not another data project!”
Such a pedestrian ambition — clearly and simply articulating the business benefit in simple terms — may not satisfy the VC community or command the highest market valuations, but wouldn’t it be nice if we could articulate the value and benefits of data solutions and capabilities in plain language that any business executive could understand.
Perhaps then we might see more companies identify as data-driven, and more companies justifiably claim that they are leveraging their data as a corporate business asset. Is that such a radical notion?
Originally published at https://www.forbes.com.
We don’t need more data jargon.
What we need is greater clarity of communication in explaining exactly how and why data capabilities deliver business value in language that can be understood by data engineers, data architects, data analysts, business analysts, and business leaders alike.
Forbes
Randy Bean
April 14, 2022
This is a republication of the article above, visually edited by the Editor of this site. Joaquim Cardoso MSc @ The Health Revolution Strategy
I hope we can all agree that investments in data capabilities and solutions must deliver business value.
Maybe this is stating the obvious, but it is not always clear to me that major corporations have clearly articulated their business goals and expected outcomes as it relates to investments in data capabilities before they make these investments.
How many times have I met with data and technology leaders and listened to enthusiastic endorsements of the data architectures and platforms they have implemented, …
… only to meet with business leaders who profess that they just don’t trust the data they are seeing …
— the quality of the data is questionable, the timeliness of the data is inadequate, the relevance of the data to the business questions that are most critical is questionable?
This may seem like a caricature of the current state of data in the business world today, but from my experience, exaggerated or not, there is truth to be found in this dynamic.
Most organizations continue to struggle to manifest the holy grail of becoming data-driven — just 26.5% of leading companies identify as being data-driven organizations.
… just 26.5% of leading companies identify as being data-driven organizations.
There is no question that efforts to capture, sanitize, organize, and analyze data require committed effort and expertise.
Companies have been working hard at this for decades, with evidently mixed results — even though data is an asset that flows through any business from production to consumption, just 39.7% of companies say they are managing data as a business asset.
… just 39.7% of companies say they are managing data as a business asset.
So, it is against this backdrop of an ongoing struggle to gain value from data, and to ensure that investments in data solutions, tools, and capabilities translate into some form of measurable business value, that …
… we witness ongoing perplexity as it relates to how to select the right tools and technologies in ways that deliver plain old-fashioned business value.
I recall the story from many years ago of a data executive who met with the corporate CEO to request a multi-million-dollar investment “to build a corporate MDM capability.”
The response of the CEO was to deny the investment, lecturing the data executive that until they could articulate the need in terms of business value and benefits, rather than “technology jargon”, no investment would be forthcoming.
When the data executive related this story to me, I asked with a straight face whether MDM meant master data management or metadata management.
The data executive looked at me like I had two heads. I asked because I wasn’t really sure.
The response of the CEO was to deny the investment, lecturing the data executive that until they could articulate the need in terms of business value and benefits, rather than “technology jargon”, no investment would be forthcoming.
This brings me to the state of data tools and technologies today.
We all know that data continues to proliferate, in both quantity and form.
We also know that the evolution of computing power has enabled companies to organize and analyze data to an extent that might have been unimaginable just a couple decades ago.
There are so many wonderful new data tools and capabilities available today, bringing value to data leaders and enabling the emergence of “citizen data analysts”.
In spite of this hard-won progress, the proliferation of technical data jargon continues to frustrate and sometimes antagonize business leaders, as well as sow confusion among data leaders.
Presumably, data literacy and data democratization must be good. How could they not be?
Clearly, we want data to be healthy, and it would be very helpful if we could understand what data it is that we have in our possession for purposes of analysis by having an accurate data catalog.
It would make sense that we have rules, policies, standards, practices, and procedures to manage data, so it would be helpful to have some form of data governance, though presumably not data fascism.
It shouldn’t come as a surprise to any thoughtful data leader that the growing lexicon of data tools and technologies might be off-putting to some business leaders, who want to cut through the clutter and cut to the chase —
… how will investments in data capabilities enable us to serve our customers better, expand into new markets, introduce new products and services, and use data as a competitive differentiator?
Isn’t this what it’s all about? Isn’t that why we are here?
As if we hadn’t enough data jargon, enter the new next big data things — data meshes and data fabrics.
I have no doubt that data meshes and data fabrics deliver wonderful features and advances over previous generations of data solutions.
However, why has the business of data become the business of branding and rebranding, rather than the delivery of clear business value?
Haven’t data meshes and data fabrics existed in previous forms and varieties for years, if not decades, known by a different terminology at the time.
When did data warehouses become data lakes? Why did DSS become EIS become BI become self-service analytics or whatever the successor solution is today? (You can look these acronyms up if you don’t know what they mean).
The names change, but hasn’t the fundamental value proposition remained the same — how can we make data more accessible to business analysts and business decision makers to make better and more informed business decisions?
When did data warehouses become data lakes? Why did DSS become EIS become BI become self-service analytics or whatever the successor solution is today?
Data technologies and solutions naturally experience short lifetimes. It should come as no surprise that capabilities evolve rapidly given the dynamic of data — ongoing proliferation of data and ever-increasing computing power.
Data should be a tool for transformation and business change.
No wonder then that in just the past two decades, we have seen organizations migrate from relational databases to solutions like Netezza, and then migrate from Netezza to Hadoop, and now migrate from Hadoop to Snowflake, or whatever.
No wonder then that in just the past two decades, we have seen organizations migrate from relational databases to solutions like Netezza, …
and then migrate from Netezza to Hadoop, and now migrate from Hadoop to Snowflake, or whatever.
I expect that I will hear from the data technology experts, many of whom are my friends and readers, who will weigh in to point out the distinctions and nuances that I have obscured. I hope they do.
I hope to learn something.
My point is not to minimize advances in data technologies and solutions.
My point is that, when all is said and done, data leaders must do a much better job of articulating the value and benefits of these data capabilities and solutions in plain business language, in terms of business value and clear business benefits, if they ultimately want to be successful in building data-driven companies.
My point is that, when all is said and done, data leaders must do a much better job of articulating the value and benefits of these data capabilities and solutions in plain business language, …
… in terms of business value and clear business benefits, if they ultimately want to be successful in building data-driven companies.
We don’t need more data jargon.
What we need is greater clarity of communication in explaining exactly how and why data capabilities deliver business value in language that can be understood by data engineers, data architects, data analysts, business analysts, and business leaders alike.
Only then will we overcome the gap between data capabilities and delivery of meaningful business value that translates into outcomes such as improved customer service and successful entry into new markets.
Only then will business leaders no longer feel compelled to say, “we don’t understand what it is your talking about”, or “not another data project!”
Such a pedestrian ambition — clearly and simply articulating the business benefit in simple terms — may not satisfy the VC community or command the highest market valuations, but wouldn’t it be nice if we could articulate the value and benefits of data solutions and capabilities in plain language that any business executive could understand.
Perhaps then we might see more companies identify as data-driven, and more companies justifiably claim that they are leveraging their data as a corporate business asset. Is that such a radical notion?
Originally published at https://www.forbes.com.
