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4 Steps in the Art of Data Analysis

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Guest blogging for the Aurora EDI Alliance today is Nathan Camp of Liaison Technologies.  Photo appears courtesy of Stephen Bowler.

Have you ever heard about the game Minecraft by Mojang (www.minecraft.net)?  The website describes it as, “…a game about breaking and placing blocks.  At first, people built structures to protect against nocturnal monsters, but as the game grew players worked together to create wonderful, imaginative things.”  No, I don’t play, but I’m fascinated by the grip it has on my son’s imagination to build something from the raw elements.

Minecraft is a massive world.  According to “The Scale of Things,” each world is 130 quadrillion blocks. Each block is sitting there, holding potential to be used and reshaped into something useful, something necessary, something as beautiful as 8 bit art can get.  The same can be said of data and data analysis.

Yes, I want to talk about data.  Data transformed.  Data made available.  Data made useful.  Data made actionable.  Data shared.

More people within organizations are now becoming data customers and analysts. Decisions are being made based on real-time, observable, quantitative metrics.  Marketing managers test content changes with A/B and multivariate testing.  SEO analytics drive behavior and modify marketing plans and go-to market strategies.  Data analysis is so important, a recent survey of more than 700 marketing professionals conducted by Infogroup Targeting Solutions and Yesmail Interactive revealed that, “…45% of of marketers identified analyzing or applying customer information as the biggest data-related challenge they will face this year.”  Infogroup.com reports that, “…the big data trend will significantly impact corporate budgets in 2013 as marketers place a growing emphasis on analytics, real-time data and integrated multichannel marketing.”

When marketing is working, that leads to more data analysis across the company.  Sales key performance indicators (KPI) for such things like quota achievement, pipeline and close ratios, are now becoming transparent across entire organizations.  Supply chain managers now can have instantaneous and collaborative access to inventories of raw goods, work in process, finished goods and goods in transit.  This can be overlaid against demand forecasts, with minor corrections made sooner.  This speed and access to useful data is how companies are driving inventory costs down and increasing customer satisfaction through real-time progress updates.  Progress updates are not just related to tangible goods any longer.  Service companies must make sure that project managers are keeping their clients apprised to project status as well.  Budgetary tolerance demands this.

Here are four practical starting points in your review of your data:

(1) Perform a data inventory – record every source of data creation/receipt/generation.  This could be information coming from B2B data exchanges, created from manual entry points in your CRM system, from internal applications, or from legacy systems.

(2) Connect the data points – document your information entry points as well as where data goes.  Verify if useful information is accessible across the enterprise, or if it is sequestered due to its sensitive nature.

(3) Measure the value of the data – publish your findings in steps 1 and 2, and have discussions about this data in cross-department meetings.  Often, a piece of data will have little relevance until it is properly polished with further data elements and context. Sometimes it can also be a case of one man’s trash is another’s treasure.

(4) Make the data available – this can be the hard part where data from disparate systems needs to be normalized into a common framework (database, Excel spreadsheet, XML, JSON) and published into accessible data locations.  If you have determined you have sensitive data that needs special treatment or handling, for example customer records with PII (personally identifiable information) or credit cards (PCI), then you may want to employ an obfuscation or tokenization strategy to render the data as harmless.

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EDI and the Internet of Things (IoT)
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