Data Is a Corporate Asset that Powers Digital Transformation

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The power of digital transformation does not reside in specific software, hardware or even business mission. Data is the power, the heart and the blood of digital transformation.

Recently, I was talking to a friend who had just changed jobs. Previously, he was working for a specialized high-volume online retailer, looking after their eCommerce implementation and customer-experience systems.

His new job was with Carfax. Carfax offers a vehicle history report to prospective car buyers. Users just enter the VIN number, and Carfax tells them all about the history of the car they are thinking about buying. For pre-owned vehicle buyers, Carfax is essential.

The first thing my friend said about Carfax was, “We’re a data company.”  Although this was perhaps a slight understatement, it was absolutely accurate. Raw vehicle-history data is their raw material and inventory, and vehicle history reports are their product—data in, data out!

Today, data has emerged as the essential element within nearly every enterprise in terms of decision-making. Former IBMer and Columbia College business professor, Dr. Kennedy Amofa, states, “Business intelligence and data analytics have become the core competencies for business decisions and operations.”

Companies that sat out the whole digital-data discussion, which is now over 10 years old, risk being disrupted into irrelevance. For large enterprises, this inaction may be potentially more devastating than for the smaller company.

Ray Wang, of Constellation Research, explains that early adopters in digital-transformation efforts have built a huge advantage for themselves over their less-innovative competitors. This could prove to be very costly in the not-too-distant future.

Data is a Corporate Asset

Data is nearly ubiquitous within the organization. It is, indeed, a corporate asset; it does not belong to one application, one manager, one department or one division. The enterprise needs to adopt a “data culture.”

Finance cannot claim “their” receivables data is for their use only, and Sales cannot claim “their” contact files belong to them alone. Data belongs to the organization just like the buildings, furniture and computers.

The great power of data is found in its sharing. The sharing of data must not only be based on need; it also must be based on free movement across functional silos and lines of business.

As processes and functions are converted from manual execution to digital or digitally powered functionality, they will employ assorted applications at the process level. These applications will interact with other applications, data-collecting sensors and data networks, both internal and external. These applications will both consume and create data.

All of that data must be securely managed, stored and protected.

What does this look like in real practice?

Automating the Sales Process: Selling Involves Four Elements

Selling starts with four elements:

  1. salespeople
  2. prospective customers
  3. pain points or needs
  4. solutions or products

Years ago, selling was a numbers game. If you could call 100 people per day, you might actually talk to 20 people. Of those 20, perhaps five would have some interest in your product, and you would expect to close a sale with one of those five people. No one has the time or patience for that kind of selling anymore.

However, that process is littered with data. Just for starters, it includes contact names, telephone numbers, answered/didn’t answer, time of day and date the call was placed, and time of day and date the call was answered. Additionally, the content of the actual discussions contained useful, collectible data such as gender, age and other demographic data.

Sadly, sales reps had no incentive, nor means, to collect or store most of this data. Companies may have even scoffed at the idea of wasting time with it.

Today, eCommerce solutions offer useful, high-value information related to unknown prospects. This might include information about pain point mitigation, tribal knowledge and detailed technical data related to products, application of products to needs and other educational resources. Couple this with tracking online behaviors—essentially who looks at what and for how long—and companies can identify potential prospects.

Marketing automation solutions oversee this process, pull in data from web access tracking and use it to build visitor profiles. As visits become more frequent and the value of the specific content desired increases, visitors trade useful information about themselves in the form of title, location, company and perhaps even contact data. After certain required criteria are matched, a visitor may be identified as a marketing qualified lead.

Configure Products and Solutions that Meet Customers’ Needs

Sales will engage these leads and discuss the prospect’s interests and requirements with the aid of a guided selling interview facility built into their configure-price-quote (CPQ) systems. As these discussions become more detailed and specific, CPQ will begin putting together possible product solutions that are applicable to the needs of that customer.

This process will include CPQ interacting across functional silos, pulling in data related to supply and part availability, product scheduling, credit worthiness, order management and other specialized functions. Product experts have supplied data related to specific types of usage in specific environments and physical settings.

When the prospect buys, CPQ pushes data back to all of those sources. This initiates the order-entry, billing and supply-chain notifications related to part or supply purchase, scheduling production cycles for the order, itself, and scheduling logistical resources to effect delivery of the purchased product. Marketing converts the prospect into a customer, and Customer Support records them as a supportable customer.

Data Powers Process Automation During Digital Transformation

This illustration shows the interconnectivity of processes and the data that powers those processes. When the data does not move across functional lines, the process cannot be completed throughout the organization. This back-and-forth, free movement of data is essential to automating almost any function within an enterprise.

IT and functional department managers must facilitate the legitimate free flow of data throughout the enterprise.

Companies that are successful at learning to identify, exploit and effectively share data are well-equipped to compete in this digital marketplace.


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