Data-driven or insights inspired?
As the phenomenon of digital transformation continues to unfold, we need to be wary of certain absolutes that will work their way into our language, our thinking and our consciousness. It's common, especially in the early stages of any emerging trend for new ideas and new thinking to take on more meaning than is intended, and eventually to become accepted facts rather than the conjectures or ideas that they really are. Later, the concepts or theories unfold, these ideas or concepts are demonstrated to be what they always were - early attempts to define or to provide context that provided some insight, but were not complete or holistic. Such is the case with digital transformation today.
Data Driven
No phrase worries me more than what I hear repeated regularly as a mantra by our customers and prospects. Everyone wants to be "data driven", which on the surface is an excellent idea. If by "data driven" they mean making decisions based on data rather than conjecture or opinion, that's good. If they mean allowing machines and processes to make decisions based on rules driven by high quality data, that's also good. If they mean managing end to end processes that are highly connected and integrated and most of the work is done based on good data, rules and systems, so much the better.
But here's the thing: data is for systems. Data is not for people. People need insights or knowledge derived from the data. So if your business is going to be data driven, you are talking about a layer or two below the human element - stuff you can run automatically or in the dark.
Insights Inspired
I'd prefer to focus on being inspired by the insights from the data. This is where humans add real value, in interpreting the data and obtaining insights or making discoveries based on information from the data. We can be informed by these insights and even inspired to create or imagine new products, services and business models from insights gleaned from the data. Our goal as business leaders should be that what can be automated and data driven should be, and we should turn our attention to interpreting the insights that come from the data and building new offerings, solving unmet needs and creating new channels, relationships and business models based on inspiration from what we can glean from the data.
The problem with data
Being data driven at an operational level will become a necessity, because most companies will want to eliminate inefficiencies and cut costs. Replacing people with machines or automated processes will make a lot of sense if and when the data is right. However, almost anyone involved in artificial intelligence and machine learning will tell you that the biggest challenges to using these tools effectively are: 1) enough data 2) of high enough quality and 3) with enough access to the data.
In other words, simply having data can't and won't make your organization a "data driven" company. You need to focus on improving the data to provide higher quality data with greater consistency. You need to have enough data for the machines to draw logical conclusions and define consistent rules. You need to aggregate and provide access to data in order to make it usable.
Having a lot of data doesn't necessarily make it easier to be data driven, and the more sources and the larger the number of "types" of data make it even more difficult to normalize the data. In fact it may be easier to become an insights inspired company rather than a data driven company, especially if you operate in a large legacy organization with a large number of IT systems which don't share data effectively.
The real point of this blog
So, violating all the rules of blog writing, I've left the "bottom line" statement till the end. The real point of this blog is that being data driven is difficult but not impossible, but only a short term target and not really all that interesting. Data driven companies will solve operational challenges - they'll optimize systems and cut costs through automation if their data is good and systems are integrated. What we should be striving for is making better use of data by converting it into information which provides insights, which inspires companies to create new products, services and business models. We ought to hope for, to aim for, using data in such a way that it impacts our strategic thinking, that the data inspires people to create new offers and new products. Anything less is yet another attempt to improve efficiency and cut costs, and misses a huge opportunity for growth and differentiation.
Data Driven
No phrase worries me more than what I hear repeated regularly as a mantra by our customers and prospects. Everyone wants to be "data driven", which on the surface is an excellent idea. If by "data driven" they mean making decisions based on data rather than conjecture or opinion, that's good. If they mean allowing machines and processes to make decisions based on rules driven by high quality data, that's also good. If they mean managing end to end processes that are highly connected and integrated and most of the work is done based on good data, rules and systems, so much the better.
But here's the thing: data is for systems. Data is not for people. People need insights or knowledge derived from the data. So if your business is going to be data driven, you are talking about a layer or two below the human element - stuff you can run automatically or in the dark.
Insights Inspired
I'd prefer to focus on being inspired by the insights from the data. This is where humans add real value, in interpreting the data and obtaining insights or making discoveries based on information from the data. We can be informed by these insights and even inspired to create or imagine new products, services and business models from insights gleaned from the data. Our goal as business leaders should be that what can be automated and data driven should be, and we should turn our attention to interpreting the insights that come from the data and building new offerings, solving unmet needs and creating new channels, relationships and business models based on inspiration from what we can glean from the data.
The problem with data
Being data driven at an operational level will become a necessity, because most companies will want to eliminate inefficiencies and cut costs. Replacing people with machines or automated processes will make a lot of sense if and when the data is right. However, almost anyone involved in artificial intelligence and machine learning will tell you that the biggest challenges to using these tools effectively are: 1) enough data 2) of high enough quality and 3) with enough access to the data.
In other words, simply having data can't and won't make your organization a "data driven" company. You need to focus on improving the data to provide higher quality data with greater consistency. You need to have enough data for the machines to draw logical conclusions and define consistent rules. You need to aggregate and provide access to data in order to make it usable.
Having a lot of data doesn't necessarily make it easier to be data driven, and the more sources and the larger the number of "types" of data make it even more difficult to normalize the data. In fact it may be easier to become an insights inspired company rather than a data driven company, especially if you operate in a large legacy organization with a large number of IT systems which don't share data effectively.
The real point of this blog
So, violating all the rules of blog writing, I've left the "bottom line" statement till the end. The real point of this blog is that being data driven is difficult but not impossible, but only a short term target and not really all that interesting. Data driven companies will solve operational challenges - they'll optimize systems and cut costs through automation if their data is good and systems are integrated. What we should be striving for is making better use of data by converting it into information which provides insights, which inspires companies to create new products, services and business models. We ought to hope for, to aim for, using data in such a way that it impacts our strategic thinking, that the data inspires people to create new offers and new products. Anything less is yet another attempt to improve efficiency and cut costs, and misses a huge opportunity for growth and differentiation.
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