Data science: Transforming data into value

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Transforming business data into assets, data science helps organisations improve revenue, reduce costs, seize business opportunities, improve customer experience, and more



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4 July 2019 | 0

Data science is a method for gleaning insights from structured and unstructured data using approaches ranging from statistical analysis to machine learning. For most organisations, data science is employed to transform data into value in the form improved revenue, reduced costs, business agility, improved customer experience, the development of new products, and the like.

“The amount of data you can grab, if you want, is immense, but if you’re not doing anything with it, turning it into something interesting, what good is it? Data science is about giving that data a purpose,” says Adam Hunt, chief data scientist at RiskIQ.

“The biggest value a data science team can have is when they are embedded with business teams,” Ted Dunning, MapR Technologies




Data science vs analytics

While closely related, data analytics is a component of data science, used to understand what an organisation’s data looks like. Data science takes the output of analytics to solve problems.

“Data science is coming to conclusions that drive your data forward,” Hunt says. “If you’re not solving a problem with data, if you’re just doing an investigation, that’s just analysis. If you’re actually going to use the outcome to explain something, you’re going from analysis to science. Data science has more to do with the actual problem-solving than looking at, examining, and plotting [data].”

For Hillary Green-Lerman, lead data scientist at US analytics firm Looker, the difference between data analytics and data science is about timescale. Data analytics, she says, describes the current state of reality. Data science uses that data to predict and/or understand the future.

“A lot of folks think of data analysts as just junior data scientists; someone who wants to be a data scientist when they grow up,” Green-Lerman says. “Sometimes it’s true, but I actually find that a really good analyst has a different skillset than a data scientist.”

Data science vs Big Data

Data science and Big Data are often viewed in concert, but data science can be used to extract value from data of all sizes, whether structured, unstructured, or semi-structured. Of course, Big Data is useful to data scientists in many cases, because the more data you have, the more parameters you can include in a given model.

“With Big Data, you’re not necessarily bound to the dimensionality constraints of small data,” Hunt says. “Big data does help in certain aspects, but more isn’t always better. If you take the stock market and try to fit it to a line, it’s not going to work. But maybe, if you only look at it for a day or two, you can set it to a line.”

Business value of data science

The business value of data science depends on organisational needs. Data science could help an organisation build tools to predict hardware failures, allowing the organisation to perform maintenance and prevent unplanned downtime. It could help predict what to put on supermarket shelves, or how popular a product will be based on its attributes.

“The biggest value a data science team can have is when they are embedded with business teams. Almost by definition, a novelty-seeking person, someone who really innovates, is going to find value or leakage of value that is not what people otherwise expected,” says Ted Dunning, chief application architect at MapR Technologies. “Often they’ll surprise the people in the business. The value wasn’t where people thought it was at first.”

Data science teams

Data science is generally a team discipline. Data scientists are the forward-looking core of most data science teams, but moving from data to analysis, and then transforming that analysis into production value requires a range of skills and roles. For example, data analysts should be on board to investigate the data before presenting it to the team and to maintain data models. Data engineers are necessary to build data pipelines to enrich data sets and make the data available to the rest of the company.

Mark Stange-Tregear, vice president of analytics at eBates, warns against seeking a data science “unicorn” – an individual that combines non-linear thinking with advanced mathematics and statistics knowledge and the ability to code.

“Data engineering I don’t think of as a key data scientist trait,” Stange-Tregear explains. “I want someone that actually adds something else. If I can have someone build a model, be able to evaluate the statistics, and communicate the benefits of that model to the business, then I can hire data engineers that are sophisticated enough to take that model and implement it.”

Embedded approach to data science

Some organisations opt to commingle data scientists with other functions. For example, MapR’s Dunning recommends following a DataOps approach, by embedding data scientists in DevOps teams with business line responsibilities. These DataOps teams tend to be cross-functional – cutting across “skill guilds” such as operations, software engineering, architecture, and product management – and can orchestrate data, tools, code, and environments from beginning to end. DataOps teams tend to view analytic pipelines as analogous to manufacturing lines.

“The goal of data science is to construct the means for extracting business-focused insights from data”

“An isolated data science team might want to deploy the most sophisticated model,” Dunning says. “The embedded data scientist is going to look for cheap wins that are maintainable. They’re mercenary, pragmatic, about solutions they pick.”

Goals and deliverables

The goal of data science is to construct the means for extracting business-focused insights from data. This requires an understanding of how value and information flows in a business, and the ability to use that understanding to identify business opportunities. While that may involve one-off projects, more typically data science teams seek to identify key data assets that can be turned into data pipelines that feed maintainable tools and solutions. Examples include credit card fraud monitoring solutions used by banks, or tools used to optimise the placement of wind turbines in wind farms.

Incrementally, presentations that communicate what the team is up to are also important deliverables. “Making sure they’re communicating out results to the rest of the company is incredibly important,” RiskIQ’s Hunt says. “When a data science team goes dark for too long, it starts to get in a little trouble. Product managers take work for granted unless we’re talking about it all the time, selling it internally.”

Data science processes and methodologies

Production engineering teams work on sprint cycles, with projected timelines. That is often difficult for data science teams to do, Hunt says, because a lot of time upfront can be spent just determining whether a project is feasible.

“A lot of times, the first week, or even first month, is research – collecting the data, cleaning it,” Hunt says. “Can we even answer the question? Can we do it efficiently? We spend a ton of time doing design and investigation, much more than a standard engineering team would perform.”

For Hunt, data science should follow the scientific method, though he notes that it is not always the case, or even feasible.

“You’re trying to extract some insight out of data. In order to do that repeatedly and confidently, and to make sure you’re not just blowing smoke, you have to use the scientific method to accurately prove your hypothesis,” Hunt says. “But I don’t think many data scientists actually use any science whatsoever.”

Real science takes time, Hunt says. You spend a little bit of time confirming your hypothesis and then a lot of time trying to disprove yourself.

“With data science, you’re almost always in a for-profit company that doesn’t want to take the time to dive deeply enough into the data to validate these hypotheses,” Hunt says. “A lot of the questions we’re trying to answer are short-lived. In security, for instance, we’re trying to find the threat actor tomorrow, not next year – tomorrow, before he can release his threat to the wild.”

As a result, data science can often mean going with the ‘good enough’ answer rather than the best answer, Hunt says. The danger, though, is results can fall victim to confirmation bias or overfitting.

“If it’s not actually science, meaning you’re using scientific method to confirm a hypothesis, then what you’re doing is just throwing data at some algorithms to confirm your own assumptions.”

Data science tools

Data science teams make use of a wide range of tools, including SQL, Python, R, Java, and a cornucopia of open source projects such as Hive, oozie, and TensorFlow. These tools are used for a variety of data-related tasks, ranging from extracting and cleaning data, to subjecting data to algorithmic analysis via statistical methods or machine learning.

“You need good visualisation tools. Programming tools – Python is an odds-on favourite at this point. You need the tools that will actually build interesting models. You can’t survive with just one,” MapR’s Dunning says.

When MapR surveyed its customer data teams, Dunning says, the smallest number of modelling tools used by a team was five, and that didn’t even get into visualisation tools.

“Things are becoming more polyglot because people are more suspicious. Will this other modelling technique produce a better model?” Dunning says. 

Data science skills

While the number of data science degree programs are increasing at a rapid clip, they are not necessarily what organisations look for when seeking data scientists. eBates’ Stange-Tregear says he looks for candidates that have a statistics background, so they know whether they are looking at real results; domain knowledge to put results in context; and communication skills that allow them to convey results to business users.

“If I’ve got a data scientist that can do all of those things, then I’ll worry about getting that implemented through the data engineering team,” he says.

RiskIQ’s Hunt is attracted to candidates with PhDs. “I’m biased toward people who have PhDs, but I wouldn’t pass up someone who has a lot of experience,” Hunt says. “What a PhD tells me is you’re capable of doing very deep research on a topic, and you’re able to disseminate that information to others. But having a solid background or personal project is incredibly interesting.”

Hunt says he particularly looks for PhDs in physics, math, computer science, economics, or even social science. He would not turn his nose up at applicants with degrees in data science or analytics, but he does have reservations. “My personal experience is I find they’re very useful, but they focus too much on the operations of the models and not the mindset,” he says.

MapR’s Dunning cares less about the letters behind an applicant’s name than their ability to show him something new. “What I interviewed for first and foremost [when hiring data scientists] was: Did the interviewee teach me something? I did not want to find people that knew how to do what I knew how to do,” Dunning says. “I desperately wanted to find people that could do stuff that I couldn’t do, or that could teach the team stuff.”

Dunning notes that some of the best data scientists or leaders in data science groups have non-traditional backgrounds, noting that some of the best he has worked with include someone who spent six years working as a gardener before going to college, a person with a background in fine arts, another with a French literature degree, and yet another who was a journalism student and very little formal computer training.

“You want to test people in terms of data perception, not knowing formulas,” Dunning says. “You want the ability to look at things and understand them.”

Data science certifications

Organisations need data scientists and analysts with expertise in techniques for analysing data. They also need big data architects to translate requirements into systems, data engineers to build and maintain data pipelines, developers who know their way around Hadoop clusters and other technologies, and system administrators and managers to tie everything together. Certifications are one way for candidates to show they have the right skillset.

Some of the top Big Data and data analytics certifications include:

  • Certification of
    Professional Achievement in Data Sciences
  • Certified Analytics
  • Cloudera Certified
    Associate (CCA) Data Analyst
  • EMC Proven
    Professional Data Scientist Associate (EMCDSA)
  • MapR Certified Data
  • Microsoft Certified
    Solutions Expert (MCSE): Data Management and Analytics
  • SAS Certified Data
    Scientist Using SAS 9

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