|By Emily Burns||
|November 30, 2012 02:00 PM EST||
Efficiency may be the most commonly used term in enterprise software marketing - that or "ensure." And not without reason - efficiency is one of the key value propositions of most enterprise software, from collaboration tools, to productivity tools, to integration tools and beyond. At a certain point though, the gains to be achieved from efficiency become smaller and smaller and of lesser and lesser business significance.
This is resulting in a shift in focus from efficiency to effectiveness. At times, these goals are twin, but in many cases, they are not - the most effective allocation of resources may not be the most efficient - at least in the short-term. Managing an organization with an eye toward effectiveness can be a challenge, because business metrics are often tied to processes and other types of "discrete" pieces of work, and how quickly/efficiently they are completed. As a result, when an organization makes the shift to managing for effectiveness rather than efficiency, the metrics used to evaluate success typically have to be "leveled-up," that is, taken up to the level that really matters to the business. An example of this leveling up occurred several years back when customer service organizations changed their focus from shortening call times to increasing the rate of first call resolution. Resolving a customer issue on the first call may result in increasing the length of the call, but over the long term it is a more effective approach, because it may result in a shorter overall expenditure of the Customer Service Representatives' aggregated time, and will certainly result in more satisfied customers.
Operationalizing this "leveling-up" is not an easy task. Most of the greatest challenges associated with doing so relate to data. First, organizations must have an idea that their current efficiency-based metrics are not serving them well. The only way to know that your current practices are ill-serving you is to capture data to make that point. In the CSR example above, that means being able to find out that a customer has called multiple times. But the way that calls are typically handled, a case is created for each one, meaning that the data doesn't tell a story of a customer calling multiple times and taking the time of many different CSRs; instead, the data tells of ten individual calls, each of which lasted three minutes. The complexity of the problem is actually greater than this, because what happens more often than not in such cases is that a customer will try to resolve the problem by contacting the organization through multiple different channels - phone, Web, email, chat. Because the data is so often fragmented, organizations will typically find out about such broken practices through a series of irate letters and phone calls, or in the worst case scenario, in a drop-off in customers. Whatever the means of notification, at some point it becomes clear to the organization that they not only have a problem of misaligned incentives, but also a data problem. They then turn to the data to understand what has been going on in their organization and how to manage more effectively.
The story likely can be pieced together from the data, but the organization must still make sure they are asking the right questions - if "number of cold calls made" is not the right metric, what is? Once the right questions have been identified, then it's time to turn to the data. Because in most organizations the data to be captured was not set up with these higher-level goals in mind, getting the right answer from the data requires some work. The data across these various systems must be integrated and federated - all of the necessary data must be extracted from the various systems inside and out of the organization and loosely coupled so that the data is telling the whole story. It also requires cleansing the data and rationalizing it such that data about the same thing being captured in different systems is in sync.
It may be that even after having all of the data rationalized and accessible, the crucial data needed to manage the business more effectively is not currently being captured. This is a relatively small problem, with practically everything digitized and virtualized, there is very likely a way to capture the data an organization seeks. A common scenario is that the data is being captured, but in an off-premise cloud-based application or in a partner's application or it may be that the data is embedded in the activities carried out on social networks. In all of these cases, new technology makes the data accessible and manageable. As a result, so, too, are the answers to the real business questions of how to manage the business more effectively.
Data integration tools make it possible to integrate and federate data from cloud-based applications with on-premise systems, to incorporated data from third parties. The ability to use Hadoop MapReduce to take in and manage unprecedented volumes of data from social networks and other non-traditional sources makes it possible to truly have, manage and analyze all of your data. New social MDM technology means that you can tap into the data embedded in social interactions on social networks and use this to create an even more fully fleshed-out golden record for your customers.
In truth, it is the gains we have made in efficiency, in finding ever-more efficient ways to access, store and analyze data that make this turn towards effectiveness possible. Without being able to do all of the above in a time- and cost-efficient manner, it is not possible to use the data to manage more effectively.
In many ways, this is what the hype about Big Data is all about. The unarticulated and implicit excitement about Big Data is really about being able to take advantage of the data in which we are all awash and use it to manage our organizations more effectively than ever before. Managing for effectiveness looks different in every industry. In retail, managing for effectiveness is understanding customers - catering to them when, where, how and with what they want. In pharma, managing for effectiveness is limiting physician wash out, getting more clinical trial data more quickly, and being able to complete or pull the plug on trials faster based on the results of that data. In every industry, managing for effectiveness means using the power of data to make the best business decisions possible, getting a true return on data.
The essence of data analysis involves setting up data pipelines that consist of several operations that are chained together – starting from data collection, data quality checks, data integration, data analysis and data visualization (including the setting up of interaction paths in that visualization). In our opinion, the challenges stem from the technology diversity at each stage of the data pipeline as well as the lack of process around the analysis.
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