Manual data-sitting shortchanges you and your company

Too often, the analytics professionals you hired to provide insights and analysis end up spending their days plugging up holes and cleaning up spills. They came on board ready and willing to realize their potential and contribute to the success of the venture, and instead they’re doing work that manages to swing back and forth between tedious and frantic, and is anything but fulfilling. If they could spare a few minutes to look up from all the codes, they might wonder how they got stuck.
At the same time, executives could rightly feel that they are the ones who have been shortchanged. They have been told that web analytics will lead them into the promised land, that all this data will work wonders, and soon they’ll be awash in milk and honey. They can see what the enterprise is costing them, but not what it’s getting them. What’s more, the analytics professionals who are supposed to work the wonders tend always to be the bearers of bad news: “this gap you see in the table here, well, we’ll have that fixed soon, and next month we’ll have better numbers.” Neither analysts nor executives are getting what they thought they signed up for. Analysts have got capacity and insights that are not being tapped because they’re too busy putting out fires. Business leaders are hip-deep in data but can’t extract meaning from it that could help them direct business activities in productive paths.
While there might be several angles from which to approach these problems, the most potent is to rewrite the job description of the web analysts. Babysitting the data is far below their pay grade, and frankly a waste of their time and skills. Analysts shouldn’t have to do it: they’re both over- and under-qualified. Though the tasks are generally simple, there are so many of them that even the most detail-oriented perfectionist ends up with crossed eyes when trying to keep everything straight. By its very nature it’s a job that humans can’t do without introducing the occasional error.
Far better to hand the tending of the data to the computer, the one player ideally suited to the task. Think of what automating the system means: it’s not just that all those little fires that have kept people scurrying around with buckets could be put out automatically. It’s that those fires never start–they don’t even become a warm spot in the circuitry. With the buckets standing idle in the hall closet, the analysts are free to look at the data, the entirely reliable data, in new ways, and to help others in the organization use it to drive better business decisions.
Until the data reliability problem is addressed, web analysts won’t be able to provide insights into the types of questions that business leaders need answers to, nor to mold a system that can provide those answers. They won’t be able to make the sorts of meaningful contributions that the organization has been expecting from them.
Until they have data they can trust, business leaders are operating on assumptions and hunches, unsure about just what all the numbers are really telling them, tantalized by the promise of insights that remain out of reach.
Lori Forsyth is the Director of Content Programs at Claravine

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