The Age of the Badass Analyst

  • Seize the day with the badass data analyst
  • Explore modern approaches to data analytics that unleash your brilliance.
  • Five common data challenges

As a data analyst, you provide one of the most needed services in business today, triggering important decisions with the insights you provide while grappling with impossible deadlines, high expectations, and IT bottlenecks. From healthcare to government to higher education, organizations in every sector need data analysts.

Regardless of which industry you work in, collecting data and compiling your findings into reports that will help the business is a part of the job.

Regardless of which industry you work in, collecting data and compiling your findings into reports that will help the business is a part of the job.

SECTION 1 LET’S DIVE IN

DATA ANALYSTS: BADASSES WHO PROPEL CHANGE

Your organization counts on you to answer the questions they pose with pinpoint accuracy. It’s vital to get the answers right, because decision-makers use those answers to make important choices. In your pressure-cooker role, it can be  a challenge just to survive, much less thrive. Do it successfully, and you’ll achieve badass status.

SECTION 2 FIVE COMMON DATA CHALLENGES

1. DATA PREP IS WHERE TIME GOES TO DIE

Even though data prep and blend is only the first step in an analytic process, it’s vital to get right, so that when it’s time to generate insight, data is accurate. That’s why most analysts spend the bulk of their time wrangling data using manual processes, leaving little time for producing reports.

The data you work with exists across multiple file types—SQL databases, CSV, XML, Excel (XLSX) formats, and more. You need to bring together each piece of the data puzzle, but manual processes are not efficient for generating insights. Cleansing data from these formats requires a lot of copy/paste, formulas, or macros unless you happen to know Python, R, Pandas, or Jupyter.

If you do, you know that scripting languages are time-intensive and can be error-prone. If you don’t, some degree of manual reconciliation is your only option, and manual data work is mind-numbingly slow, inflexible, and error-prone.

Even if you are familiar with these or other tools, prepping data that can be “dirty” in a number of ways is still slow-going using traditional data-cleansing methods. Cleansing data isn’t limited to ensuring naming and abbreviation consistency, of course. You’re bound to encounter other prep and blend hurdles, like:

  • Unit conversions, such as pounds to kilos or feet to yards
  • Currency conversions
  • Empty spaces
  • Unrecognizable characters imported from non-English alphabets True/false records that need to become yes/no records (and vice versa)
  • Null values that send your advanced analytics into a tailspin
  • Records that contain unwanted characters like %, &, and other symbols or punctuation
  • Non-identical duplicates, like “Maria Seelos” and “M. Seelos”

2. JOINING DATA SHOULDN’T BE A MARRIAGE OF INCONVENIENCE

To arrive at a usable answer from cleansed data, you likely have to join multiple sources of data, such as multiple spreadsheets and databases that are formatted in
a variety of ways.

Reports often require multiple programming languages and approaches to achieve your goal. From R to Python to SQL, from dplyr to sqldf to data.table, exploring and applying these solutions eats up time. SQL, R, and Python approaches can limit your flexibility when you want to use a single solution to:

  • Join data that results in more than one or two outputs
  • See the fall-out data that flows from all three outputs
  • Group records based on two input keys from your data stream
  • Produce a dataset that contains every combination of two or more tables

3. DATA YOU DON’T HAVE DOESN’T HELP YOU

Before you can even begin to prep data, you have to track it down.

According to a recent survey, nearly two-thirds of data going through  traditional prep and blend was at least five days old by the time it reached  an analytics database—and therefore, no longer able to meet the guidelines  for fast-paced analytics.

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, while your own project schedule stutters or stalls completely. Deadlines can be missed as a result.  In any case, working with aging data is not ideal.

When internal processes are slow, and reports take days to generate,  you’re not able to deliver your best work. According to a recent survey, nearly two-thirds of data going through  traditional prep and blend was at least five days old by the time it reached  an analytics database—and therefore, no longer able to meet the guidelines  for fast-paced analytics.

4. ANTIQUATED APPROACHES AREN’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.

The same holds true for geospatial data. With this kind of data, you can pinpoint where your target customers are located to design better marketing campaigns, find new retail locations or optimize your supply chain logistics. When it comes to customers and prospects, it’s important to know details like age and income.

It’s even better to gain deeper knowledge, including the types of technology,  food, and household products they purchase, to increase your understanding  and inspire new segmentation approaches.

But you can’t get very far with outdated methods like spreadsheets. 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.

5. STILL WAITING FOR PREDICTIVE AND PRESCRIPTIVE ?

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.

Many analysts are still focused on descriptive analytics that explain what already happened. They would 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.

Now, analysts can be empowered by modern technology. Modern, easy-to-use solutions help analysts sharpen their analytics skills without having to learn code.

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