DataScience CEO Ian Swanson discusses how any business team, from Sales to Customer Support, can see powerful results through applying data science.

1. DATA SCIENCE FOR
EVERY BUSINESS TEAM:
A Conversation with Ian Swanson

2. Tell us a bit about your company.
Swanson: We’re a data science company. We deliver what customers need to see in their data –
actionable insights, not more dashboard reports.
. . . . . . . . .
What is data science, and why has it become such a big deal?
Swanson: In the simplest terms, data science is the extraction of knowledge from data. Why now?
There’s an explosion of data from a vast number of sources, including but certainly not limited to
the Internet of Things. Everybody has data, and companies must extract value from data to stay
competitive.
. . . . . . . . .
How new is this discipline? Businesses have relied on analytics and business intelligence tools for a
long time. How is this different?
Swanson: Yes, business intelligence and analytics tools have been around for years. Thousands
of companies specialize in those approaches. The difference is that data science is not just about
dashboards or KPIs, key performance indicators. An analytics software solution might grab your
attention and tell you that revenue is dipping in a given part of your customer base. However, they
won’t tell you why it’s happening. You need a data science team to answer the “Why?” In order to do
that, organizations can’t rely solely on machine-only software solutions. They need human beings who
are able to connect the dots and dive deeper than just a BI solution or analytics dashboard.
. . . . . . . . .
Have we been overvaluing these tools because they’re all that has been available? Or have we
outgrown them because the amount of data has exceeded their ability to deal?
Swanson: I believe there’s value in these tools. To be very clear, businesses need something that adds
more value than analytics and/or BI alone. Just to stay competitive, companies need to extract value
from data. They understand they need more than just a KPI. They need to be able to dive deep into
data and extract very clear insights that can help them win in their market. In order to do that, they
need to have a data science team and data science solution. No dashboard tool is able to go into
that depth, and able to mine that gold or put that gold in the product. Businesses need to have a data
science team.
. . . . . . . . .
So it’s not that there’s anything wrong with dashboards per se – it’s just that they’re limited.
Swanson: Dashboards can do the jobs they’re currently being asked to do, but now, companies and
their business teams need more value from their data. That’s where dashboards are falling short.
That’s where you need the machine-plus-human element of data science. With that, you can dive
deeper and find gold in large complex data sets.
. . . . . . . . .
Talk a little more about this “gold” in the business. Can companies not get to this through an
analytics dashboard?
Swanson: That’s best illustrated through an example. Lets say you’re a Product Marketing Manager
of an e-commerce website. You have a dashboard that reports activity on the site and you notice
that there’s a 30 percent overall drop-off in users between signing up and adding a photo. The value
of the dashboard stops there -- they won’t tell you why the drop-off is happening or what you should
do about it. Data Scientists, by contrast, would tell you to encourage users age 35-50 to add photos,
since there’s is a significantly disproportionate drop-off with that segment, and they account for 32
percent of your revenue. They could also tell you that people who add photos are three times as likely
to purchase products on your site. While you, as a Product Marketing Manager, would have had do
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3. much more analysis to arrive at that conclusion, a data scientist could explain the “why” and make
recommendations about a response. Although dashboards have clear uses for certain tasks, data
science can provide much more depth and value.
. . . . . . . . .
As young a discipline as data science is, all kinds of misconceptions and myths surround it. What are
observers missing?
Swanson: A common misconception is that a company needs to find a data scientist -- someone who
is multi-disciplined -- in order to do data science work. We share the point of view of Dr. DJ Patil, U.S
Chief Data Scientist, who described data science as “a team sport.” Data science employs techniques
and theories from many different fields -- statistics, data mining, business analysis, and so on. For a
company to be effective at data science, it must look at it from this team sport perspective and bring
together people who are specialists in various disciplines. When you do that, you wind up with a very
strong unit, one able to perform the task of data science. So the misconception is that you need to
find a unicorn -- a data scientist. The reality is that data science really is a team sport.
. . . . . . . . .
Businesses that want to make sense of their data and find those insights are of course focused on
the core business – they may not totally grasp data science. How can a business determine its data
science needs?
Swanson: By first understanding the problem and the opportunity it presents. Bottom line, companies
are experiencing an explosion of data, truly big data. Data is multiplying every year in quantity and by
type. Whether you make garage doors or coffee makers or anything else, the market is drowning in
data. Then, the key thing to understand in order to stay competitive is the need to extract value from
that data. Any company that wants to lead its market, or keep market share, needs to get value from
its data. You can’t just sit on it. Why? Because your competition is likewise trying to get value from its
data. As a result, we’re seeing a relative arms race in data science.
. . . . . . . . .
Companies are of various sizes and capabilities, and they have at their disposal varying amounts of
resources. If this is in fact an arms race, it would seem that mid-size companies need an equalizer of
sorts. They may not be able to go on a hiring binge.
Swanson: Yes, that’s true. Look at the market. Right now, more than 6,000 companies are hiring data
scientists. According to many reports, only 20 percent will be able to fill their open positions. So the
arms race is on, and the stakes are high. Companies need data science or they’re at risk of losing
market share. There is, however, a solution. We’re offering one that can help immediately. Companies
of any size, midsize or large enterprises, can experience a 100 percent gain in value from applying
data science.
Paying large consulting services like Deloitte or SAP is very difficult for many midsize companies.
We solve this problem through making data science scalable and accessible to any business team.
We give companies the power of data science at an affordable price point. Indeed, our monthly price
point is in the thousands, compared to multi-million dollar engagements from other companies in the
data science market. We’re able to produce amazing results for any business team, from marketing to
customer support.
. . . . . . . . .
Do organizations below the level of the Fortune 500 or 1000 have an opportunity to use data science?
Swanson: Yes, one hundred percent of them do. Data science is available right now for every
company. And every company should be taking advantage of their data and extracting value. They
might not be able to put together teams of 100-plus people. But there is an opportunity for them to
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4. partner with a company like ours that can go in at an affordable price, apply the art of data science
and provide remarkable results on an ongoing basis.
. . . . . . . . .
Are most data scientists primarily doing so-called janitorial work – data cleanup, in other words? Are
data scientists being used optimally or is there still a mismatch?
Swanson: There are several challenges in the data science space. The process of using data and
extracting value is both cumbersome and challenging. Part of the challenge is cleaning up data, called
data munging. That can take up to 80 percent of a data science team’s time. We offer a solution that
produces results in part because we developed real technology that eliminates the “data janitor” work.
We’re able to maximize the efficiency of our team as well as build a real platform around analysis
capabilities.
. . . . . . . . .
So you’re not only enabling data scientists to be more efficient and effective, you’re eliminating that
mismatch of (mundane) tasks to (expensive) people. They need to be doing things other things than
janitorial chores…
Swanson: Correct. Our offering is more efficient at a better price point because there’s real machine
technology backing up our people, our data scientists. When we work with customers in identifying
actionable insights within data, we’re able to produce a package that adds enormous value in the
midsize to large enterprise market.
. . . . . . . . .
How are companies benefitting from data science today? It’s still a young discipline.
Swanson: Typically, data science has supported the engineering or product teams, reporting into the
technical part of business. Companies need to understand that all functions within the company can
benefit from data science. We’re interacting with various business teams directly and providing them
an avenue to dive deep into their data, extract value, and really transform how they operate. We work
with customers to help them reduce churn within the business… to increase basket sales… to drive
engagement… to reduce operational costs. All the answers are in their data. It really takes machines
and humans, working together, to produce real and quantifiable results that were buried in data.
. . . . . . . . .
What’s the right mix of human and machine for data science, and how do you achieve that?
Swanson: There’s no right formula for every company. It depends on the company. The focus needs to
be on the power of machine and human. That’s a departure – it’s no longer just a matter of software.
In their book Zero to One, Peter Thiel and Blake Masters talk about machine and human intelligence
in relation to business challenges. They discuss how companies have this huge appetite for data. And
we tend to think that just because there’s a lot of data, there’s also a ton of value. But the reality is,
data is of no value unless humans provide a helping hand.
Thiel and Masters specifically say that computers can find patterns that may elude humans. But
at this point, if you’re trying to drive action from data, action can only come from humans. Humans
connect the dots. Right now, whether they’re called analytics or dashboard solutions, that’s primarily
what companies are using -- software only, machine-driven solutions. We’re driving change within this
ecosystem. The change is, “here is a solution, a company that you can partner with.” We’re offering
machine plus human, and it’s incredibly efficient. We give customers the right blend of machine and
human that delivers what they need to see in data: actionable, and often huge insights. The kind of
insights that make it possible to transform a business or to keep it as a market leader.
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5. So you can work with organizations that already enlist data scientists as well as those that haven’t yet?
Swanson: In terms of where we fit, there are two ways to look at it. If a company doesn’t have a data
science team, absolutely use us. If you do have data science team, we add enormous value. We’re
able to field all the questions a business team might have about their data, and put that on our plate.
Let us answer those questions; let us mine for gold and find it. Once we find it, we bring it to the
surface. Then we work hand in hand with the internal data science team to integrate that gold into the
company’s product or technology. What you typically see is that the business teams are completely
underserved as it relates to these solutions, to be able to dive deep in data and answer questions.
That is precisely our sweet spot.
. . . . . . . . .
About DataScience
Based in Culver City, Calif., DataScience, Inc. (www.datascience.com) delivers a first-to-market
“assembly line” for the data science process. Driven by its mission to help businesses spend less time
analyzing and more time implementing, DataScience delivers what business teams need from their
data -- actionable insights, not more dashboard reports. Customers plug raw data into DataScience’s
proprietary technologies and receive actionable insights from an on-demand team of data experts.
The company’s solution can be applied to any business team, from marketing to customer support,
and can be leveraged across any industry, from connected hardware (e.g., the Internet of Things) to
e-commerce. Founded by a team of accomplished entrepreneurs with a background in data science
and big data, and experience in such Fortune 500 mainstays as American Express, AOL and Sprint,
DataScience traces its lineage to Sometrics, which was acquired by American Express in 2011.
DataScience is backed by Pelion Venture Partners, Crosscut Ventures, and TenOneTen.
MEDIA CONTACT
Ken Greenberg
Edge Communications, Inc.
(323) 469-3397
ken@edgecommunicationsinc.com
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