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Packaging Analytics is Good for You and Your Customers

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Packaging Analytics is Good for You and Your Customers

Analytic data products are like Kale for your company, so value-rich that all you have to do is cook it and serve it correctly and your company can immediately enjoy the organizational health benefits of publishing analytics. With these vitamin-fortified data products, the sniffles from neglected partner relationships disappear, their antioxidant properties help keep customers happy for longer periods of time. And the high fiber content can help streamline the business and impact the bottom line. So you see, your “analytic kale” can add value—once you remove the jaw-breaking chewiness of the raw data and then serve it in balsamic lemon juice to cut the bitter, earthy taste.

You know that finding value in your data will be good for you, like Kale, but how do you figure that out? The first challenge is quantifying the nutritional value of your organically grown data. The next trick is knowing how to prepare it – that takes a data chef – and then finally, finding the right appetites to consume your data meals, and serving these tasty data treats with the right presentation and on a regular basis. Great food for thought, right?

Let's Talk About this for a Moment

To resolve these challenges, I’ve been looking to the Information Management analysts at Gartner Inc., in-particular Doug Laney, along with Olive Huang, Ted Friedman, Jamie Popkin, Jim Hare, Mario Faria and others. Two years ago they wrote about, “InfoNomics,” and the process by which an enterprise should recognize, and determine ROI from, its information as a corporate financial asset. Back then information and analytics were simply cost centers, and their payback was in the form of insight for analysts, IT and data scientists. Gartner estimated that by now, 30% of organizations would be treating their data as a financial asset, yet most of them would not know how to quantify or measure its value, which makes determining your return on investment very difficult.

Last year, to help with quantification, they gave us multiple formulas from which to determine both the internal and the external value of those assets—one helps manage risk, the other may offer revenue opportunity. What we have noticed is that this method of measurement also reflects on the personality and risk tolerance of the data stewards and IT organization. Those that are exclusively focused on data governance—making sure the data is perfectly correct, secured and controlled—tend not to have clarified the potential value of their assets. To move these organizations along, Gartner more recently crafted six economic formulas to help determine the value of your Information Management initiatives. We like the ones that focus on the Market Value and Economic Value of Information, because this is what GoodData customers are doing. Interestingly, we still run across organizations who do not want to talk about the monetary value of their data, nor do they see themselves as sellers of an analytic offering. For them we simply help improve relationships within their business.

Now that we have been helping organizations build data products for some time, we’ve come to recognize where challenges exist that Garter has yet to address. We meet visionary organizations—those that want to share analytics with customers and partners as a means to improve competitiveness, but unless they are ISVs themselves, they have no practical experience in defining and building their data products. So we help them. We’re helping these enterprises unleash the value of their information assets through secured, packaged, personalized analytics. We get them through the problems of creation, distribution and engagement, which are areas that Gartner is only starting to understand. We act as surrogate product managers to define data products. We use our platform to securely distribute these packages at scale, and then we provide both tools and a model in which to evaluate whether the application is indeed successful.

Written by GoodData Author  | 

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