The inclusion of data and knowledge in the strategic architecture reflects the existence of two classes of entity — the “material” factors we have dealt with as tangible resources, and “informational” factors that also can be collected, lost, stored and used. The simplest informational factor is just data—specific pieces of information about something of importance. This can be illustrated by extending the call-center example from Briefing 27. At that point, we looked at the staff skills required to serve customers well. Now we will look at the issue of the data they need.
In Figure 1, the call center started with 200 000 customers making on average one call per month. The 80 staff could each take 125 calls per day (2 500/month) if fully trained, so could just handle all the calls received. But to answer those enquiries, these staff need information about customers and their activity – that is, data. Some of that data is long-lived, such as name, address and other personal details, whilst other data is transitory, such as the customer’s recent transactions or enquiries.
Figure 1: Adding systems to capture data allows growth in call center activity. (Click image to view larger)
Assume that 20 items of data are required in order to answer every enquiry customers might raise, but only 60% of that data is actually available – enough to deal with nearly 90 % of customer enquiries. The data gaps increase the time staff have to spend on each enquiry, so each person can only answer 99 calls per month instead of 125. The call center therefore has too few staff, simply because of limited data. Over a quarter of calls go unanswered, causing 5 % of customers to be lost each month.
Each month, more data becomes obsolete — old transactions become irrelevant to current enquiries, customers’ details change without being updated, and so on. Even spending some $20 000 per month updating and maintaining the data is not sufficient to keep even the limited data up to date, so this fraction of adequate data continues to decline, reducing staff productivity still more.
By month 12, the system comes into balance, but only through the same kind of bad mechanism we saw in Briefing 75 for the computer service firm — losing customers brings pressure back to a level that the center’s capacity can deal with. This is helped by the continuing increase in staff numbers, so although each person can by now only respond to 92 calls per day, there are by this time enough staff to manage all the incoming calls.
Realizing from the start that the lack of data is damaging customer service and holding back staff productivity, the call center decides to invest in improved systems for data collection. This takes the first 12 months to complete, but when those systems come online, the flow of data items captured increases sharply and the database starts to fill. Staff productivity rises strongly over months 12–18, so the center’s capacity is easily able to handle the larger number of calls from the increasing customer base. Note, incidentally, that the total quantity of data to be captured is itself rising in the later months, due to the growing number of customers.
Until next time…
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Information Systems investment programs
New systems can do more than merely improve the availability of data, of course. They can automate business processes, and thus directly raise productivity, and create further business benefits. Unfortunately, there are often complex interdependencies between such initiatives. In this case, there is clearly a benefit from investing in data capture systems. But there may also be a benefit from investing in the call support systems. However, the benefit of both initiatives in combination is not a simple addition of the two. The investment in improved call-handling will be devalued if not accompanied by efforts to improve the availability of data, and more data will be of limited help if it still has to be used with slow, manual processes. Which should happen first?
Fortunately, the strategy dynamics approach can be used to answer exactly this question, not just in the case of two complementary systems in one function, but across an entire portfolio of information-systems projects. Typically, each project does something to improve one or more resource-flows, and each also places demands on the scarce and costly resource of the IS professionals who need to do the work. An IS project portfolio therefore has its own ‘strategic architecture’, which can be specified, quantified and simulated in order to discover better strategies in terms of the business value that the program will deliver.
This briefing summarises material from chapter 9 of Strategic Management Dynamics, pages 602-606.
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