Inside Issue 1
Aug 2019
Diving into Data
Article 7 of 11

Sink or Swim: 5 Big Data Challenges

When you hang around long enough in a profession, you pick up familiar nomenclature, general truths, and knowledge. Such is the case with data analysis. Anyone who has worked with data knows there are certain phrases you are bound to start echoing along the way. Today, according to the IDC info brief "The State of Data Science and Analytics,” there are approximately 54M data workers worldwide.

The truth is, depending on your approach to data prepping and blending, how you feel and what you say about your work may look and sound different than other analysts. Analysts stuck in outdated approaches are uttering different phrases than those adopting modern approaches. "7 Things Badass Analysts Say and Do" is all about what analysts using modern approaches have to say about their work.

If you're unsure what category you fall in,  take a look at the five most common data challenge rumblings and grumblings we hear data analysts say when they are stuck deep in old methods. After all, knowing the problem is often the first step to solving it. 

 

1. “If only my boss knew how long data prep really takes.” 

If this sentiment resonates with you, you probably feel like data prep is where time goes to die. However, it's vital to get right. You know what happens when you spend time doing analysis, only to find out your data is suspect. That’s why most analysts spend the bulk of their time wrangling data, leaving little time for producing reports, let alone mining that data for insights. 

 

2. “Data comes from everywhere; I wish it was easier to use all of it.” 

Joining data shouldn’t be a marriage of inconvenience. After all, to arrive at a usable answer from cleansed data, you likely must join multiple sources of data, such as spreadsheets and databases that are formatted in a variety of ways — and we haven’t even talked about unstructured data like e-mail messages, word processing documents, videos, photos, audio files, presentations, and webpages. Most analysts truly desire to combine data from more sources. After all, the possibilities for insight are boundless when you can effectively use any data you need. Still, most are frustrated that it isn’t easier to bring data sources together. 

 

Most analysts truly desire to combine data from more sources. After all, the possibilities for insight are boundless when you can effectively use any data you need.

 

3. “My data loses relevance every minute I wait.” 

Data you don’t have doesn’t help you. Before you can even begin to prep data, you must track it down. It might be locked in the IT department and take a few days to access because IT has many priorities in front of your request. Or, your data might be buried in a spreadsheet that’s shuttled back and forth over email or tucked away in a custom database managed by a single user. These scenarios leave you dependent on the timelines of others. Meanwhile, your own project schedule stutters or stalls completely and you stress about your looming deadline while sending increasingly less polite follow-up emails to your data providers. By the time your stale data arrives, your business stakeholders have moved on to new questions … or worse, devalued the role data insights can play in making business decisions. 

 

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4. “I want to overlay more data sets, but I can’t get there with old solutions.” 

Outdated approaches just weren’t built for advanced capabilities. Once data is prepped, you’ll want to enrich it so you can extract as much value as possible. For example, while it’s good to capture a company’s name and address, it’s better to augment that information with deeper business insights like industry, size, and revenue. Often, you’ll need the help of a specialist and a bit of manual coding to enrich your data — and that takes time you don’t have.

 

Outdated approaches just weren’t built for advanced capabilities. Once data is prepped, you’ll want to enrich it so you can extract as much value as possible.

 

5. “I wish I could do advanced analytics on my own.”  

Daydreaming about performing predictive and prescriptive analytics without actually getting to do it is a real drag.  More than ever, data analysts are expected to present advanced analytics, such as predictive and prescriptive models, including creating decision trees, running A/B tests and logistic regressions, and performing market basket analysis. 

Yet many analysts are still focused on descriptive analytics. Maybe you’d like to move toward more forecasting and informed scenario-building, but aren’t sure how. At one time, predictive and prescriptive models needed to be applied by data scientists, but that’s no longer the case. If your advanced analytics are still dependent on others to implement, it’s important to know you have other options.

 

“These phrases sound all too familiar.” 

If some of these phrases felt too true, check out “7 Things Badass Analysts Say and Do,” and get inspired by your fellow analysts across every industry and department who flipped these challenges into solutions.  

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Discover how analysts have shifted their careers to become data innovators in this short video.

 

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