Author: Scottie Todd

Data analysis has become a highly attractive job. If you’re thinking about going in the field, you’re probably wondering what traits make someone a good data analyst. Let’s take a look at four key qualities that define the best professionals in the industry.

Relentless Curiosity

At its core, data analysis is about trying to take a deep dive into information that doesn’t always lend itself to easy answers. You have to want to get to the answers, and you have to be willing to think hard about how to create experiments that will get you there. People in the data analysis world are always wondering:

• Why things happen in the first place
• How processes can be improved
• If we’re really getting the whole picture on a topic
• Where the missed opportunities are
• If there’s a different way to take action to solve a problem
• How deeply an issue can be understood

Drilling through data to get at insights calls for a lot of work. People who succeed as analysts tend to have an abiding discomfort with letting things go. The problem doesn’t go away at the end of the workday, and they’re often the folks who have “ah-ha!” moments in the middle of the night.

Willingness to Question

Curiosity means nothing if it stops when it comes up against a wall. In the early days of the analytics revolution in pro baseball, for example, ridicule was the common response that analysts were met with when they asked the simple question of whether there was a better way to score a run. The revolution persisted because people involved with the sport, especially on the stats side of things, continued to ask a simple question: “Are we even asking the right questions?”

To get to the point you question the very questions other people are asking requires a certain degree of courage. People don’t like to be questioned, and the default response by many analysts has been to just say you should follow the numbers where they take you.

But it takes more than that. Many folks who find themselves in analytics as a profession have somewhat contrarian instincts. They’re not fans of following the herd, and they’re often convinced the herd is going to its doom.

The contrarian worldview has become so pervasive in analytics that the financial world even considers it a distinct form of investing called contrarianism. When the global financial markets collapsed in 2007, for example, the contrarians made billions because they had:

• Asked the questions others weren’t asking
• Gathered the data to figure out what was actually happening
• Followed the numbers to the only logical conclusion
• Made aggressive decisions based on the best data available at the time

It takes a special type to tell decision-makers that they’re making decisions the wrong way.

Commitment to Learning

The data analysis industry depends on evolution in both statistics and computing. Keeping up with the pace of those developments is not an easy task, and the best analysts tend to be folks who read a lot. They want to keep up with new studies, technological innovations and shifting attitudes. If you’re the kind of person who wants to spend a weekend learning a new programming language or reading up on fresh research, a career in analysis might be for you.

Calling an analyst a lifelong learner would be an understatement. They might not all hold master’s degrees or Ph.D.’s, but they all have the same relentless drive to ingest new information and ideas.

Communications Skills

Analysis was once seen as the domain of shut-ins and nerds, but the rapid growth of corporate, government, academic and even public interest in the field has put the best communicators are the front. People like Nate Silver, a data analyst who specializes in elections, sports and news, have emerged as public figures who are now invited onto weekly talk programs to discuss the news of the day. Organizations also have many inward-facing analyst positions where the jobs are fundamentally about explaining what all the data means.

If you’re someone who loves to tell others what you’ve learned, there is a place for you in the analysis world. Those who can understand the math and convert it into actionable insights are increasingly in very high demand.

One of the biggest revolutions in the last 10 years in the field of medicine has been the advent of healthcare analytics. Leveraging large data pools, machine learning and new diagnostic technologies, practitioners have been able to find innovative ways to improve patient outcomes. Patient data analytics incorporates information from a wide range of fields, including pharmacology, genomics, personnel management and even biometrics. In addition to monitoring patients, organizations are now able to monitor staff members in order to optimize their availability and work efficiency. Let’s take a look at some of the factors driving this revolution.

Patient Data Analytics

Among the hardest things for doctors to do is to hold the whole dataset regarding a single patient in their heads. Computerized systems allow doctors access to massive databases about their patients, and they also permit them to make use of pattern-recognition technologies to get out in front of problems that even a trained physician might not readily see. For example, a team of researchers has developed a method for determining whether a patient is likely to develop thyroid cancer using a system that achieves greater than 90% accuracy in predicting whether a growth is or will become malignant.

Dashboards also permit doctors to readily access both the data and analysis. Where a practitioner 20 years ago might have to wait for a few pages of patient information to be emailed or faxed to them, they can now pull up a full medical history on their tablet. If the doctor needs to dig deeper, they can see information about medications the patient is on, how the person was diagnosed during previous visits and even the times of the year when the individual has had the most trouble. This can make it much easier to spot patterns that lead to a diagnosis.

Collecting and Analyzing Big Data

While we tend to think of big data as a 21st Century innovation, the reality is that researchers have been collecting the data to do hard science much longer. Famous ongoing data collections efforts like the Framingham Heart Study have been active since the middle of the previous century. What has changed, though, is that we now have patient data analytics packages that can compress massive amounts of data into a useful work product in a matter of minutes, hours or days.

This sort of work at scale can also provide early warnings. CDC data is used to produce daily maps and charts showing the progress of flu season each year. Rather than trying to make a best guess based on rumors and news reports, hospital administrators can figure out when to begin stockpiling for flu season by referring to hard data.

Organizational Improvements

While it’s easy to think the best way to improve patient outcomes is to focus on direct treatment options, there’s also a lot to be said for using healthcare analytics to revolutionize medical facilities as organizations. A good analytics package can help an administration decide:

• When to ramp up or cut hours
• How to economize inventories of critical drugs and supplies
• What forms of continuing education to encourage staff members to pursue
• How to get patients to heed the advice of doctors and nurses

Except for small clinics and general practices, most healthcare organizations are fairly large operations. They have to deal with the same problems other corporate entities do, such as taking shipments, ordering new materials and scheduling workers. Analytics can even tell a hospital whether the price-to-performance expectation of doing something like adding a new wing will be worthwhile.

Patient Satisfaction

Assessing whether you’re satisfying patients can be a major challenge. Unsatisfied patients may never complain directly to you, instead taking their business to other facilities and expressing their anger on social media. People don’t return questionnaires in great numbers, making it difficult to be proactive even when you strongly want to do so.

Data mining and analytics allow modern organizations to monitor many sources of information regarding both quality control and patient satisfaction. Social media mentions, for example, can be broken down into positive, negative and neutral sentiments. An organization can monitor mentions of its name and the names of its staff members. Machine learning is now so advanced that error correction can be used to identify social media mentions that get the spelling of a doctor’s name wrong.

Information can be compared and contrasted, too. A hospital might compare its analytics regarding staffing against social media sentiment. This can help administrators draw a line between what does and doesn’t drive patient satisfaction. Solutions can then be devised based on what has worked best to make patients happy, and it can all be done without the difficulties associated with satisfaction surveys.

Adopting and applying healthcare analytics, though, demands a cultural change. In particular, an organization must forge a culture of following the data where it leads. If patient satisfaction metrics indicate that a hospital is falling short in key areas, there still has to be a responsive culture present within the administration. With time, however, an organization can develop both the infrastructure and culture required to make better decisions for its patients based on hard data.

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Advances in modern computing have made it possible to analyze more data than ever before. Data analysis automation has the potential to accelerate digital transformations for companies across a wide range of industries. Before you take the first steps toward that future, though, it’s wise to understand what uses cases data analysis automation is best suited for and which ones may present challenges. It’s also a good idea to understand the digital transformation process. Let’s take a look at how your company can benefit from both.

Where Data Analysis Automation Excels

Automating the analysis of data does not provide uniform results. Automation generally works best in situations where data lends itself to many iterations of analysis. This is one of the main reasons that preparing data is a critical part of the process.

If the information going into the system presents problems, the odds of getting useful analysis drop significantly. While many new approaches, such as the use of neural networks to conduct unsupervised machine learning, can iron out some of the uglier bits, providing clean inputs remains the easiest way to not have trouble. This means ensuring that data fields are prepped to be uniform, scrubbing data for things like unreadable characters and performing initial analysis to remove anomalies and outliers.

Highly prepared data is the best sort to use in an automated process. For example, consistently formatted datasets from a trusted government source, such as weather data, tend to require less preprocessing.

The amount of available data also matters. If you’re trying to glean information from a couple of thousand data points, the data set may be too paltry for use. That’s especially the case once data points have been scrubbed from the set. More robust data sets, such as Amazon’s studies of billions of searches for products, also tend to lend themselves better to data analysis automation.

Developing a Culture That Values Data

Digital transformation calls for more than just hiring a few programmers, engineers and data scientists. At its core, the transformation of a company calls for a shift in its culture. This process does not occur overnight, and it requires on-boarding many employees and decision-makers. Hiring practices have to incorporate a data-centric worldview as being just as important as hiring “self-starters” and “team players.”

As painful as it may be to do so, companies also have to confront off-boarding folks who refuse to come along with the transformation. While an honest effort should be made to onboard all personnel, there will come a point where providing severance packages to those who struggle with the change will be necessary.

Choosing a Direction

A company must make an overt effort to pick a direction when it adopts data analysis automation. While it might seem easier to leave this up to the data people, that risks creating a hodgepodge of semi-compatible systems and cultures within your organization. Planning needs to be done to produce documents that outline what the organization’s philosophies are and the kinds of hardware and software it’ll use to accomplish its goals.

There are many major frameworks on the market today. Plenty of them are free and open-source, and those frameworks are often robust enough to do the heavy lifting a company requires. For example, the combination of Tensorflow and Python is wildly popular. These two are often used in conjunction with nVidia’s CUDA Toolkit for GPU acceleration.

Each choice will have its pros and cons. A software stack built on Linux, Python, Tensorflow and CUDA, for example, will call for engineers with specific job skills. Likewise, maintaining and updating software and hardware will require close attention to the requirements of the environment. A new version of CUDA, for example, might open up opportunities for machine vision analysis to be exploited, but it may also call for recompiling existing software and code to operate properly. Diving into such changes willy-nilly can cause the entire software stack to collapse.

Good planning documents should provide fair consideration to the available options. A reasonable comparison might be made between the virtues of OpenCL versus CUDA. There should be a good reason for the final choice you arrive at, such as the relative costs of doing GPU acceleration on nVidida versus AMD hardware.

Compliance

A disconnect between data analysis automation and the real-world things data points represent is a major risk. In an increasingly strict regulatory environment, failures of compliance come with major costs. It’s prudent for an organization to not only think about how it will acquire and process data, but how it will protect the privacy and well-being of people who may be represented by data points. Each company should also consider its own values during the digital transformation process.

Conclusion

The hardest part of automating analysis is ramping up capabilities. Your company will have to plan for a variety of challenges, such as how it will store and archive the data, what will be done with analysis and how it will report its work.

Analysis, though, lends itself to many business cases. Once your company is generating usable work products, you can also begin to develop new business models. For some companies, the focus will be on improving efficiency and processes. Others will discover that selling analysis to other parties is a viable business model in its own right. In time, you will find the returns from building out automated analytics capabilities tend to compound very quickly once you get rolling.

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