{"id":2245,"date":"2012-10-25T20:44:29","date_gmt":"2012-10-26T01:44:29","guid":{"rendered":"http:\/\/steinvox.com\/blog\/?p=2245"},"modified":"2012-10-30T18:09:58","modified_gmt":"2012-10-30T23:09:58","slug":"big-data-and-analytics-other-perspectives-worth-a-look-part-4","status":"publish","type":"post","link":"https:\/\/steinvox.com\/blog\/big-data-and-analytics-other-perspectives-worth-a-look-part-4\/","title":{"rendered":"Big Data And Analytics, Other Perspectives Worth A Look – Part 4"},"content":{"rendered":"

\"ClockPerspective\"<\/a>Big Data and Analytics is a vast and growing field whose concept and context boundaries continue to expand. In this Part 4, we explore alternative perspectives other views to expand our own perspective.<\/p>\n[Part 1<\/a>] [Part 2<\/a>] [Part 3<\/a>] [Part 4<\/a>]\n

There is significant room for other perspectives on Big Data and Analytics. Through alternative lenses with diverse perspective, those who are newly exposed and just starting to explore Big Data and Analytics will gain depth and breadth to their understanding.<\/p>\n

Big Data and Analytics – Other Views<\/h3>\n

There are three contextual perspectives that I have found timely, valuable, and worth sharing. Each in its own way supports my vision and mission for a common analytics platform that allows people to focus on extracting value, not building one-off systems. The systems that today claim to be platforms require way too much effort to deploy. The true democratized value of Big Data and Analytics must come in a system as simple to use and apply as Survey Monkey is to use, or Google Analytics is to apply.\u00a0 Configuration, customization, and frankly \u2013 the software, must get out of the user\u2019s way, and allow them to focus on achieving their Big Data and Analytics objective. Too much software is focused on forcing users to learn and know the software \u2013 which is off point.<\/p>\n

The following perspectives will help to shape this new platform yet to come \u2013 as they look at the problem to solve in context of what the user, analyst or decision maker are trying to achieve in terms of outcome.<\/p>\n

QuadDirect – Data Value Chain<\/h3>\n

QuadDirect<\/a> points out that you can have all the data in the world, but owning or accessing this asset alone will not bring success. They have identified five elements in a value chain to move from owning data to taking action. In their success story on Demand Insight<\/a> QuadDirect makes a good point that precise measurement is useful, only if you know why, where to look, and how much to analyze. In terms of getting to an outcome from Big Data and Analytics, their five element data value chain has these elements:<\/p>\n

Data<\/strong> \u2013 the raw numeric or alphanumeric values associated with specific measured events.
\nInformation<\/strong> \u2013 the data collected into tables or other organized areas so that they can be used in a meaningful way.
\nAnalytics<\/strong> \u2013 information that has been sorted through, using a range of algorithms and programs, so that aggregated trends or results are visible.
\nInsights<\/strong> \u2013 key learning from the analytics, identified in terms of meaningful business conclusions that can be drawn.
\nActions<\/strong> \u2013 based on insights, business actions are taken to correct or exploit the results of all the work done in the data\/information\/analytics\/insights value chain so far.<\/p><\/blockquote>\n

Accenture – Analytics Value Chain<\/h3>\n

Accenture<\/a> points out that using quantitative data to form decisions and drive outcomes is a critical competitive advantage. Accenture concluded that a proper solution to extract insight included formal methods to capture knowledge and create value out of available streams of social content, audio, image and video data. They point out that tools and technology advances are critical to this process as data is proliferating at an exponential rate in both type and volume. The Accenture data value chain<\/a> uses process to connect tools to people. And, it describes analysis through statistical science as \u201cmethods\u201d which extracts \u201cinsight\u201d that is applicable throughout decision-making through final outcomes. Accenture further points out that most technology focuses attention on the early part of the value chain, leaving much opportunity for downstream solution development.<\/p>\n

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\"Big<\/a><\/p>\n<\/blockquote>\n

Gartner \u2013 Analytics Capabilities Chain<\/h3>\n

At their Symposium ITXPO<\/sup> in Orlando Florida October 2012, Gartner made several presentations that emphasized new developments Big Data and Analytics have in terms of Information Technology for a variety of industries.\u00a0 They shared an interesting value chain concept that is less linear and more organic where data, content and hybrid combinations serve a chain of potential outcomes. They group these outcomes in terms of understanding the past (descriptive and diagnostic), and exploring the future (predictive and prescriptive).<\/p>\n

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\"Big<\/a><\/p>\n<\/blockquote>\n

In terms questions to ask when looking into history through the data record, Gartner points out a series of questions. And likewise, in terms of using real-time content, combined with structured data, Gartner suggests that a new set of questions can be answered through statistical computations. That creates the following outline hierarchy of questions from Gartner<\/p>\n

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