{"id":1674523,"date":"2018-10-17T14:44:33","date_gmt":"2018-10-17T21:44:33","guid":{"rendered":"https:\/\/www.retailpro.com\/News\/blog\/?p=1674523"},"modified":"2018-10-17T14:30:44","modified_gmt":"2018-10-17T21:30:44","slug":"which-data-is-most-valuable-for-analysis-in-retail","status":"publish","type":"post","link":"https:\/\/www.retailpro.com\/News\/blog\/index.php\/2018\/10\/17\/which-data-is-most-valuable-for-analysis-in-retail\/","title":{"rendered":"Which data is most valuable for analysis in retail?"},"content":{"rendered":"

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For all the good that data can do for retail businesses, there is one major issue it presents: information overload.<\/p>\n

Many companies try to focus on too many different metrics without focusing on those that truly matter.<\/p>\n

They have an ocean of data inside the company from every type of software supporting their business processes.<\/p>\n

In our experience, marketing departments typically have three to five different systems and manufacturing departments have even more.<\/p>\n

As the big data tsunami overflowed the media and blogs over the past years, many companies try to grasp what to do with all that external data that is becoming available.<\/p>\n

Some companies have started to collect external data but are not yet effectively utilizing it.<\/p>\n

According to Gartner, the confusion around big data is so apparent that they indicate companies will delay spending on analytics and Business Intelligence in general until they figure out how to handle data better.<\/p>\n

Actually, most big data investments made to date have been big data services, such as consultancy projects to clarify what they should be examining.<\/p>\n

And only 8% of respondents in a recent Gartner survey of CIOs say they have deployed big data investments.<\/p>\n

But big data — or any data for that matter — doesn\u2019t have to be a daunting task if you start from within.<\/p>\n

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Determine your end goal<\/strong><\/p>\n

As with anything else in life, the way to successfully move forward, even with developing KPIs and identifying leading indicators, is to first determine your end goal.<\/p>\n

That can be anything from increasing revenue to reducing costs.<\/p>\n

Next, consider what in your internal and external environment can truly impact whether you are capable of reaching those objectives.<\/p>\n

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Internal Data<\/strong><\/p>\n

Internal data has to do with the internal processes of the business (product design, corporate vision, etc.).<\/p>\n

You most likely own this information inside your company solutions already.<\/p>\n

Otherwise, it is time you start tracking and registering.<\/p>\n

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External Data<\/strong><\/p>\n

External data has to do with economic factors outside of the company that still have an effect (the needs of customers, competitor actions, etc.).<\/p>\n

The data needed to understand the external indicators is what is traditionally referred to as big data or external data sources.<\/p>\n

Don\u2019t worry too much about whether it\u2019s big or small data; worry about what impacts your company.<\/p>\n

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Get Whitepaper<\/span><\/h4>\n

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This blog is an excerpt from the Retail Pro Decisions whitepaper, From KPIs to Profit: Understanding Your Leading Indicators for Better Retail Results<\/em><\/a>. Get this whitepaper today to read more.<\/p>\n

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Get whitepaper<\/a><\/p>\n

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  For all the good that data can do for retail businesses, there is one major issue it presents: information overload. Many companies try to focus on too many different metrics without focusing on those that truly matter. They have … Continue reading →<\/span><\/a><\/p>\n","protected":false},"author":7,"featured_media":0,"comment_status":"open","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[49,44],"tags":[],"class_list":["post-1674523","post","type-post","status-publish","format-standard","hentry","category-retail-customer-business-intelligence","category-specialty-retail-management-and-operations"],"aioseo_notices":[],"aioseo_head":"\n\t\t\n\t\n\t\n\t\n\t\n\t\n\t\n\t\n\t\t\n\t\t\n\t\t\n\t\t\n\t\t\n\t\t\n\t\t\n\t\t\n\t\t\n\t\t\n\t\t\n\t\t