Data Value Chains: Making Data Dance for Your Business
We all deal with massive amounts of data in our day-to-day lives. Sometimes it may feel like we’re sinking under the weight of that avalanche. Meanwhile there is so much talk about Big Data, analytics and the Internet of Things, one wonders if there is more volume, velocity and variety in talking about Big Data than actual data!
In the next decade and perhaps this century, data will be one of the most critical assets that businesses must harvest to create value. Unlike other assets, data doesn’t show up on your balance sheets or income statements, yet this asset flows like lifeblood across your organization as well as outside it.
The real challenge is how we use this intangible asset to derive insights and take action to create sustainable value. In this post, let’s look at a framework to do this.
Creating Value from Data: The Dance of the Decade
Technology is at such an exciting stage where we can meet that challenge of creating value from data using a systematic approach. How we do that effectively will be the dance of the decade for IT and broader industries.
This dance will involve how we gather and connect data, breaking down the steps between all that data and the actions that create value. In order to master this art of converting data to action to create business value, let’s look at:
- how value is created in the physical world of goods and services
- how value can be created from data
- how marrying the data value chain to a physical value chain digitizes and enriches the physical value chain to create more value.
Physical Value Chains and Data Value Chains
We know businesses produce goods by sourcing materials, building their product and using channels of distribution to get to the end customer, who consumes the product and derives value from it. Every actor in this value chain enriches the goods or services for the end user.
For example, for a product like milk, here is a simplified view of the physical value chain:
Now let’s think of data as part of a value chain – a data value chain that enriches data at every stage it’s processed through the actors in the chain, in each location the data traverses.
To build a data value chain, you need capabilities that will help you access data from everywhere, analyze it anywhere, orchestrate the processes and entities that help move relevant data between applications, and finally use the data in context to engage your customers and employees and prompt them to take action in real-time. We’ll discuss the technological capabilities needed to realize this data value chain in a later post.
Data Value Chain: Access Data from Everywhere, Analyze and Act Anywhere
As the business environments de-centralize, we will have data originating outside the data center in new centers of data that are closer to where events happen and where actions need to be taken (for example, in a field office or a remote operations location).
So when we think of implementing a data value chain, we have to think of accessing and analyzing data and orchestrating processes where it makes the most sense: where the event occurs and where the action is needed.
Consequently, instances of the data value chain need to run anywhere we have connectivity and anywhere we can process data. Pervasive network connectivity and distributed computing on the network enable us to do that efficiently, balancing the costs and responsiveness needs of the business.
A Matter of Time
Time is a critical factor for the value of data. For example, when a customer walks into a store, you have a narrow window of time to capture their attention and engage them to buy. This data needs action, such as sending a relevant coupon to their mobile device. Without quick action, the value of the data reduces exponentially with time.
In other cases, data aggregated over a period of time may be equally valuable. A customer might visit a store five times in a month and look for specific departments. That is valuable trend information to act on, such as sending the customer promotional coupons for categories they have historically shown interest.
To benefit from the time value of data, data value chains need to operate across the business environment all the way from the cloud to the edge and any combinations therein.
Fresh Milk and Happy Customers
Going back to the milk value chain, you risk spoiled milk unless you continuously monitor the milk temperature and other data – during distribution, transportation and merchandizing – and take relevant action. We can digitize a mundane milk value chain and reduce waste by applying a data value chain approach:
- Access the parameters about the conditions around milk
- Analyze milk temperature readings and compare with other ambient data
- Orchestrate the exchange of relevant information among apps (If-This-Then-That) to take automatic action
- Engage people for action: Alert workers if automated actions such as temperature control fail, or in a retail setting, engage customers with promotions or shopping assistance.
This is just one example of how the dance of the decade is beginning to play out, and how a value chain approach to data can bring transformative value. We see this happening across many industries such as manufacturing, hospitality, retail and government, and we’ll discuss more on that in my next post.
Consider how you are getting insights and action from the data in your organization, and how a data value chain approach can help you milk your data for all it’s worth.
Learn more about data value chains by listening to my recorded session from Cisco Live Berlin.
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