Technology is a utility. Regardless of your industry – telco, biotech, retail or manufacturing – it’s important to classify it this way. Why does it matter? Because to gain efficiencies through automation and digital transformation, or to create new value to customers, we have to listen to the data that is coming from this utility. Listening to and understanding the data created by machines is critical for long-term viability. Amazon, Google, Facebook, Microsoft, Apple, and Netflix, to name a few, have created trillions of dollars in value by doing this.
That’s why I believe the role of the CIO is more crucial than ever.
I am a huge fan of financial planning and analysis, but most companies are primarily using historical transaction data, combined with experience and reference data, to guide their business. The fact is, the CIO is armed with more and better data they can use to understand the health of the company – and the behaviors of the company’s customers and internal resource utilization – than the CFO is.
How much more efficient would the accounts receivables team be if they knew which customers were actively engaged with your product or services? What if they focused on those products and services first, using the most talented resources available? For any connected device, the CIO has access to that information in web logs, application logs, firewalls, router logs and technical infrastructure.
Think about how much more effective your marketing team would be if they knew exactly which features a power user was engaged with, and they could then help other customers get there. Some say marketers can’t over-communicate with customers, but it’s risky to be too noisy. So why waste the “eye ball” views in an email campaign that’s trying to cross-sell a feature to a user who is already using that feature? It’s a wasted opportunity, one the CIO has the data to prevent.
CIOs know the value of “sleepy” customers
The CIO’s understanding of operational data can also extend to product and service pricing. Pricing is a strategic weapon for building a business, but it can also damage your relationships with customers and suppliers. One way to avoid self-inflicted wounds with pricing changes is to use operational data.
For example, everyone in the C Suite wants to avoid churn, but it can be hard to control if you’re all using different analytics systems to track customer data. Predicting churn triggered by pricing changes is a special challenge, but if you combine pricing data with the recency, frequency and duration of customer interaction with your product or service (behavioral analytics), you are better equipped to avoid mistakes like waking up a “sleepy” customer.
Here’s an example: I have been getting a $10 a month bill from Microsoft for my son’s Xbox for as long as I can remember, but I’m not sure he even uses the features I’m paying for. For $10 a month, I’m willing to let the charge ride.
If Microsoft knows my son isn’t active, they are much better off continuing to bill me $10 a month than they would be raising the price to $20. A $20 charge might “wake me up” and make me debate whether I should keep the service. Again, the CIO has this data. (Mr. Nadella surely knows this.)
Another example is the services business, an area of the economy that’s booming with the gig worker trend. Services companies that contract to deliver flowers or provide 3rd party customer phone support charge companies for these services. Often, however, the goals and business interests of the services companies and the contracting companies are not in alignment, and to add complexity, each of these companies has different access to different data. Interactions drive lots of information that can be analyzed for insight, if only you have visibility into the data streams. Think about the possibilities: ordering flowers from a website, the time the flowers were signed for, the length of a telephone call to customer support, the number used to call for flower delivery or customer service, the number of times a call originated from a certain number, the unique identifier of a router or set-top box – all this, and much more data, about subscribers and customers exists within multiple log files and machine data, in different applications and analytics systems. Yet all this data sits under the control of the CIO, not the CFO.
Clues about business viability exist in unexpected places
In high tech, where about 70% of our expenses are related to employees, it’s common to have hiring plans where a company will double the number of people one year, reduce twenty percent another year, and then double again. This makes planning things like real estate commitments – which can be five to seven years out – really difficult. Something as simple as putting in a Google Lens, which has integrated Edge-based machine learning, will allow you to collect data, such as how many people badge into a building, and then how movement in the office happens. Are some conference rooms never used in the afternoon? Adjusting the thermostat would save money. Are offices or cubes assigned to people who work mostly from home? Having visibility into this data lets the CIO help other managers understand how their resources are being used, and where investment – or trimming – is warranted. And there’s a new wrinkle: recently-enacted regulations in the United States require employers to ensure that exempt workers don’t put in more than 40 hours without being paid overtime. The CFO is responsible for keeping track of this, but the CIO has the data.
The net is, if you run a line of business or department for your company, you should ask yourself the question: “If I had detailed, accurate data about X, Y or Z, how could I make that actionable to make or save the company money?”
If you can clearly articulate the question to IT, the odds are they have data, but it’s very possible no one has ever asked for it. Pulling this kind of data was hard to accomplish just a few years ago. Today, with Devo, it’s easy to ingest trillions of events per day and make that information available to IT, and to business users, marketing and analysts, all with the aim of driving operational analytics – and meaningful action – into every facet of the company.
