Tuesday, January 7, 2020

Analysis And Analysis Big Companies - 972 Words

Many companies around the world have the false idea that having a strong Analytical tool is the highest power they need to produce good analytical results. In fact, good analytical results are better obtained with intelligent resources (Analysts). The 10/90 Rule focuses on peoples’ skills as the most important resource in analytical businesses. Avinash Kaushik based his conclusion for the 10/90 rule on four different facts: Website complexity, Market trends, Technology evolution, and diversity. Big companies are seriously impacted by the use of the 10/90 rule, and failure to consider this rule can lead companies to have more data and less accurate analyses. Avinash Kaushik (2007) states that, â€Å" Websites are massively complex, and although tools can capture all the data, they don’t actually tell you what to do† (P.46). I believe that all the data collection should lead to its analysis, and the analysis should produce recommendations. In order to produce the best recommendations, each company needs skilled people with the right analytical intelligence to interpret the data available and produce information that will bring changes to the company. These expected changes can either be translated into revenue, or increase the company’s competitive capacity in the web-based or E-commerce business. What can we do with all this data collected? Where do we go from here? What lessons can we learn from these trends? â€Å"Most Web analytics tools in the market, even today, simply spew data.Show MoreRelatedAnalysis Of Richard Stantons The Big E Commerce Company 2406 Words   |  10 Pageswill come to her with a super report, or an interesting bit of news, which she will then use as a foundation for a bigger analytical piece. A specific example she gave was, â€Å"if a PR comes to me with important news about, say, fundraising at a big e-commerce company, I can use that as a news hook to write a bigger piece about phone penetration and or online shopping in Africa† (Whitehead, 2016). 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