How do we make the right decisions in the digital economy? A recent conversation with my children made me aware of how using the data that is available now can help improve overall business operations.
My boys’ science homework topic this term is taxonomy and the classification of animals and plants. This has generated a number of interesting discussions, including whether a tomato is a fruit or a vegetable. Whilst it was amusing to hear the perspective of 8- and 9-year-olds, it led me to discuss with them the well-known phrase: “Knowledge is knowing a tomato is a fruit; wisdom is knowing not to put it in a fruit salad”.
I recently spent time with the DXC Technology team in India looking at how they’re transforming the operations we run for commercial clients. During one of the meetings about Bionix, our digital-generation services delivery model, I was drawn back to the tomato quote and the relevance of the “knowledge vs. wisdom” expression.
We are surrounded by data providing knowledge of how our operation is running, what we do for our clients, how we spend our time, etc. This “knowledge” is collected in a myriad of systems, from client-owned service management tools to DXC systems of record. Each team accesses its data and sees that its silo has a great deal of knowledge about a topic. However, it is only when you take this knowledge and contextualise it with other data that real wisdom and insight are formed.
To know that a tomato doesn’t make a good addition to your fruit salad, it’s necessary to know more data about the tomato, how it tastes, how it reacts when mixed with other fruits. A whole range of other issues may or may not be known to the person making the fruit salad, and to someone without the experience it’s not going to seem too obvious. Delivery teams are often faced with this situation. They have fact-based knowledge about their piece of the puzzle, but through no fault of their own they’re acting without seeing the full picture or thinking about the effects of their decisions.
For example, a team is solving issues the service desk cannot fix. The team might be fixing issues in 2 days when they actually have 5 days under the SLA provision. The data showing this will indicate that the team is “over-producing”, a key factor we look at when identifying lean optimization initiatives in Bionix. As a result, the team starts to leave incidents in the system for 4 or 5 days, allowing them to reduce the required employee-hours to meet the SLA. All good, right?
In another part of the business, that decision doesn’t seem quite as palatable. The user waiting for a solution is frustrated, so he or she calls the service desk again. Every time the user calls, the ticket is passed to the team that can fix it, and this can happen several times now that it has moved from a 2- to a 5-day resolution period. The service desk team has to vary its staffing levels based on the volume of calls and handle calls accordingly, to stay true to the SLA. The costs go up, and client satisfaction goes down. The decision doesn’t seem so good now, does it?
Being able to link desk calls from the telephone platform, chase calls in the service management system, and record the time taken by agents to escalate the chase — this process gives a different picture of the required optimisation actions needed to make the process more “lean”. Operational data mining, using data analytics, enables us to seamlessly move from knowledge to insight, bringing all the data points from the disparate systems together and collating them into the full picture. Seeing the full picture, based on system-wide data insight, means the company can spend considerably less money running the service whilst providing a demonstrably better service. It also enables a data-driven conversation with the client.
Making decisions around this sort of action might sound obvious once you have the end-to-end data. But with global siloed teams, complex systems and varying contractual provisions, linking all the pieces to give an actionable insight can still be very difficult. Helping decision makers move from facts to insights is why data analytics are essential today. The key question should be: “Am I making my decision based on knowledge (data) or on wisdom (data insight)?”
It’s not enough in today’s digital economy to make one-dimensional decisions. Using big data techniques and analytics, we can put the correct real-time information in the hands of those making the decisions to drive better overall outcomes for our stakeholders.
Knowing a tomato is a fruit is simply not enough.
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