Module 2 - Improving current business with your own data - Online

D: DRIVE
How to become Data Driven?
This programme has been funded with
support from the European Commission
Module 2: Improving current
business with own data

Smart Data Smart Region | www.smartdata.how
This programme has been funded with support from the European Commission. The author is
solely responsible for this publication (communication) and the Commission accepts no
responsibility for any use that may be made of the information contained therein.
The objective of this module is to gain an overview how
you can use the data you already have available to
improve your business.
Upon completion of this module you will:
- Learn the tips of how take advantage of the existing
data you already have
- Be able to locate where internal data already lies
within your company
- Recognize the importance of implementing data
enrichment into your big data projects
- See how data can help you to build your brand
Duration of the module: approximately 1 – 2 hours
Module 2: Improving
current business
with own data

1 Advantages of Smart Data
2
Data Enrichment3
This programme has been funded with support from the European Commission. The author is solely responsible for this
publication (communication) and the Commission accepts no responsibility for any use that may be made of the information contained
therein.
– Sources of Data
– Sources of Internal Data
Data Collection
– Why Data Enrichment is a must
– How does it work?
– The Steps of Integration
– The Principles of Data Enrichment
– Why is Smart Data smart?
– How to turn your Data into
Competitve Advantage?
Using Data to build your Brand4
– How can Data help you build your
Business
– The Benefits of using Internal Data in
Marketing

ADVANTAGES OF
SMART DATA
1. Why is Smart Data smart?
2. How to turn your Data into Competitve
Advantage?

It's not important how much data you have,
it's about how well you use it. Big Data could
potentially be just a big a problem. Smart
Data is a solution that changes the game of
marketing, and how we deliver better
solutions for customers from this point
forward.

• Smart in what data to collect, validate and transform
• Smart in how data is stored, managed, operated and used
• Smart in taking actions based on results of data analysis including
organisation structures, roles, devolution and delegation of decision-
making, processes and automation
• Smart in being realistic, pragmatic and even sceptical about what can be
achieved and knowing what value can be derived and how to maximise
value obtained
• Smart in defining achievable, benefits-lead strategy integrated with the
needs business and in its implementation
• Smart in selecting the channels and interactions to include smart data
use cases
More focussed investment in achieving better
business and organisation results
Greater confidence by the business and organisation
in justifying and approving investment and resource
allocation
Quick delivery of results
WHY IS SMART DATA SMART?
SMART DATA MEANS BEING...

The real challenge of Big Data is
not technological: it is "business".
It will necessarily involve human
expertise to enrich the data and
get it to "speak". When turning big
into smart, be aware of next tips!
HOW TO TURN
YOUR DATA
INTO COMPETITIVE
ADVANTAGE
Big Data is a project, not a tool!
Ask the right questions
Start with internal data
Enrich and cross-reference existing data
Enrich the models with external data
Involve business experts
Do not presume what the outcome will be
Remain agnostic
1
2
3
4
5
6
7
8

1. Sources of Data
– Internal
• Sources of Internal Data
– External
DATA COLLECTION

Big Data is not
neccessarily big. The
most „magical“ aspect
of big data is what I
call „Smart Data“.
Philip Brittan

A lot of data is lying in the corners of many
companies or it is possibly not even
currently collected. With a relatively small
effort, that data could be collected,
analysed and put in use to improve
marketing and user experience. As an
example, a website could be personalised
to create better service experience to users
by collecting and using general website
visitor data. Building new processes in a
company always requires investment, for
example skills, appropriate technologies,
and awareness of what to do.

The data collection component of research is common to all fields of study including
physical and social sciences, humanities, business, etc. The goal for all data collection is to
capture quality evidence so as to translate into rich data analysis.
DATA COLLECTION
Data collection is the process
of gathering and measuring
information on variables of
interest, in an established
systematic fashion that
enables one to answer stated
research questions and
evaluate outcomes.
Need of Data Collection
• To get information for analysis
• To get idea about real time situation
• To compate between two situations
Factors to be considered before collection of Data
• Scope of the enquiry
• Sources of information
• Quantitive expression
• Techniques of data collection
• Unit of collection

SOURCES OF DATA
DATA
INTERNAL SOURCES EXTERNAL SOURCES
PRIMARY SOURCES
SECONDARY
SOURCES

INTERNAL DATA
Internal Data or Own Data is information created by the
operation of an organization that includes sales, purchase orders,
and transactions in inventory instead of the data being created by
an independent study or database.
Many businesses and departments have information about their
regular functions, for their own internal purpose. In this way,
internal data is the information that the business already has on
hand, has control of and currently owns, including details
contained within the company’s own computer systems and cloud
environments.

Sources of Internal Data
Before decision-makers and data scientists look for external sources, it’s critical to ensure that all of a business’s internal data sources are mined,
analyzed and leveraged for the good of the company. While external data can offer a range of benefits, internal data sources are typically easier to
collect and can be more relevant for the company’s own purposes and insights.
There are a number of impactful, internal places that companies can look to mine data. These include:
TRANSACTIONAL
DATA AND POS
INFORMATION
CUSTOMER
RELATIONSHIP
MANAGEMENET
SYSTEM
INTERNAL
DOCUMENTS
ARCHIVES
OTHER BUSINESS
APPLICATIONS
DEVICE SENSORS

TRANSACTIONAL
DATA AND POS
INFORMATION
One of the most powerful sources of data resides within a firm’s financial and transactional systems.
Here, companies can mine both current and historical data relating to their own business purchases, as
well as information relating to the shopping trends of their customers. From these details, an
organization can glean important insights, including ways to reduce their own spending and remain on
budget, as well as crucial patterns pertaining to their customers’ buying habits and shopping
preferences.

TRANSACTIONAL
DATA AND POS
INFORMATION
In addition to their purchasing and shopping data, businesses can also mine data within their own CRM
systems. Information like clients’ company affiliations, locations and other regional or geographical
details can paint a detailed picture about where customers are located. When combined with their
transactional information, these CRM details become even more powerful.
CUSTOMER
RELATIONSHIP
MANAGEMENET
SYSTEM

TRANSACTIONAL
DATA AND POS
INFORMATION
Especially now within the age of cloud computing, a company’s own internal documents are becoming
more valuable than ever. Digitized copies of internal forms can provide a robust source of information,
particularly when it comes to the business’s activities, policies and processes. Emails, Word documents,
PDF, XML and a range of other internal docs can be mined for big data.
CUSTOMER
RELATIONSHIP
MANAGEMENET
SYSTEM
INTERNAL
DOCUMENTS

TRANSACTIONAL
DATA AND POS
INFORMATION
When it comes to internal information, businesses shouldn’t limit themselves to only the most current
information. Historical data can be very telling as well, which is why it is recommended to look into the
company’s archived documents and data streams as well.
CUSTOMER
RELATIONSHIP
MANAGEMENET
SYSTEM
INTERNAL
DOCUMENTS
ARCHIVES

TRANSACTIONAL
DATA AND POS
INFORMATION
While CRM is one of the most robust internal sources of big data, this doesn’t mean that other internal
applications cannot be mined. Other platforms that employee users leverage, including project
management, marketing, productivity, enterprise resource management, human resources, expense
management as well as automation apps can be incredibly beneficial as well. When mining these
sources, it’s in a company’s best interest to let the nature of their big data initiative drive their decisions
surrounding which sources to utilize. For example, if an organization is looking to gain insight about the
current budget, sources like expense tracking and resource management will be the most helpful.
CUSTOMER
RELATIONSHIP
MANAGEMENET
SYSTEM
INTERNAL
DOCUMENTS
ARCHIVES
OTHER BUSINESS
APPLICATIONS

TRANSACTIONAL
DATA AND POS
INFORMATION
The Internet of Things is growing every day, and providing additional and increasingly unique data for
analysis. Companies that utilize devices that are equipped with sensors and network connectivity can
leverage these for data as well. These include IoT items that the business uses in its own office, or those
that it provides for its customers. For instance, car sensors on an enterprise’s fleet of vehicles can offer a
wealth of data about usage, mileage, gas and traveling expenses. Companies that offer fitness or other
health sensors can gather, anonymize and analyze these sources as well.
CUSTOMER
RELATIONSHIP
MANAGEMENET
SYSTEM
INTERNAL
DOCUMENTS
ARCHIVES
OTHER BUSINESS
APPLICATIONS
DEVICE SENSORS

Overall, internal sources of big data can offer
numerous advantages for today’s
businesses. Not only are these sources
incredibly telling and relevant, but they’re
free of cost to the company, as this is
information that the organization already
owns. In this way, enterprises can launch an
array of big data initiatives without ever
looking beyond their own walls.

EXTERNAL DATA
External data is information that is not currently owned by the
company, and can include unstructured, public data as well as
information gathered by other organizations.
More about external data and how use it to benefit your business
you can read in Module 3: Improving your business with external
data.

DATA ENRICHMENT
1. Why Data Enrichment is a must
2. How does it work?
3. The Steps of Integration
4. The Principles of Data Enrichment

The data integration process is traditionally thought of in three steps: extract, transform, and
load (ETL). Putting aside the often-discussed order of their execution, "extract" is pulling data out
of a source system, "transform" means validating the source data and converting it to the desired
standard (e.g. yards to meters), and load means storing the data at the destination.
An additional step, data "enrichment", has emerged, offering significant improvement in business
value of integrated data. Applying it effectively requires a foundation of sound data management
practices.
DATA ENRICHMENT
Data enrichment refers to
processes used to enhance, refine
or otherwise improve raw data.
This idea and other similar
concepts contribute to making
data a valuable asset for almost
any modern business or
enterprise.
WHY DATA ENRICHMENTS IS A MUST
25%
74%
88%
36%
0
10
20
30
40
50
60
70
80
90
100
of the average
B2B marketer's
database contains
critical data errors
of companies do
not have a
sophisticated
approach to data
quality
of records
analyzed lack
firmographic data
of marketers say
that data quality is
the biggest
obstacle to
marketing
automation
success

HOW DOES IT WORK?
Data integrators traditionally bring data from source
to target unchanged. It's as if ETL developers were
movers who prided themselves on putting your
furniture in the new place unbroken. Businesses
today are asking the movers to repair and improve
the furniture before landing it in the new house.
The most obvious enrichment example is address
correction. When you enter your address on some e-
commerce sites, the site corrects it by standardizing
street, city, and state fields, and adding the last four
digits of the zip code. ETL vendors tout many
possibilities beyond address correction.
The types of information that can be added, or
"augmented„ to a demographics database:
GEOGRAPHIC
• Such as post code, county name, longitude and latitude, and political district
BEHAVIORAL
• Including purchases, credit risk and preferred communication channels
DEMOGRAPHIC
• Such as income, marital status, education, age and number of children
PSYCHOGRAPHIC
• Ranging from hobbies and interests to political affiliation
CENSUS
•Household and community data

Data enrichment or data enhancement adds more info from other internal or external data sources to information already used in the
organization. This process increases the analytic value to the existing information. One example of the data enrichment process is to associate
the current customer records in the current database with buying behaviors and demographical information from other sources.
For customers targeting purpose, income classification is used to
assign the income level to the customers.
First Last Income
John Smith 32,000 $
Henry White 88,000 $
Andy Brown 120,000 $
Steve Brook 54,000 $
Income L Income U Target
20000 39999 A
40000 59999 B
60000 79999 C
80000 99999 D
100000 119999 E
120000 139999 F
First Last Income Target
John Smith 32,000 $ A
Henry White 88,000 $ D
Andy Brown 120,000 $ F
Steve Brook 54,000 $ B
Example

Enrichment isn't limited
only to demographics. Data
quality tools allow
definition of rules that
integrate into the ETL
stream for any data source.
MATCHING
incoming records
with existing data,
like
identifying to which
insured member a
claim applies
CORRECTING
invalid data based
on other data in
the record, like
correcting an out-
of-bounds hand-
entered
measurements
based on an
independent
automated data
feed
INTERPOLATING
missing values
based on other
available data. So
while loading a
pregnancy related
claim the system
might fill in a missing
value for gender.
THE STEPS OF INTEGRATION

THE PRINCIPLES OF DATA ENRICHMENT
Operations that automatically match, correct, or interpolate data values operate with some "confidence" level, meaning that sometimes they are wrong. That means
that hundreds of thousands of matches may have been incorrect - not necessarily an issue for the particular application involved, but something for those implementing
enrichment to consider.
By following these three guiding principles, organizations can ensure that they deploy enrichment processes that enhance business value of integrated data while
minimizing risk and maximizing flexibility as requirements evolve.
The business should drive and manage enrichment definition: Data stewards who understand the incoming data and the intended
use must be the key drivers of what data is enriched, how it is done, and test of the enrichment outcomes.
Enriched data must be identifiable and audit-able in the target database: Any integration target database should feature
complete lineage metadata: where is this data element from, when was it loaded, and what happened to it along the way. This is even more true for data
added by interpolating from, augmenting, matching, or correcting source data. Analysts must know which data came directly from the source, which was
generated, and the confidence level of the latter.
Data replaced by enrichment must be available alongside the enriched data: Enrichment processes must store modified or
added data in such a way that analysts have access to the "raw" source data. Analysts should be able to independently test enrichment processes and
suggest improvements if needed. If, for whatever reason, enrichment doesn't meet specific analysis needs, then they should be able to fall back to the
original source data.
1
3
2

USING DATA TO
BUILD YOUR BRAND
1. How can Data help you build your
Business
2. The Benefits of using Internal Data in
Marketing

While the average small business has less
self-generated data than big players like
Google or Facebook, this doesn’t mean big
data is off limits. In fact, in many ways, big
data is more suited to small businesses
because they’re generally more agile and
able to act more quickly on data-driven
insights. Let’s look at some of the ways small
businesses can make use of big data.
HOW CAN DATA HELP
YOU BUILD YOUR
BUSINESS

Understanding what makes your customers tick
Thanks to big data, small businesses can get a fuller picture of their customers – what makes
them tick, why they buy, how they prefer to shop, why they switch, what they’ll buy next, and
what factors lead them to recommend a company to others. Companies can also better
interact and engage with customers by analyzing customer feedback in order to improve a
product or service. Useful data sources include traditional in-house data (like sales data and
customer service logs), social media, browser logs, text analytics, and large, public data sets
(such as census data).
Social media has become a particularly valuable source of data, making activities such as
identifying niche markets and analyzing customer feedback much easier and cheaper. Twitter
– where almost all conversations are effectively held in public – is easier to mine than most
platforms.
1

Identifying trends
Spotting and monitoring behaviors and patterns allows us to take a stab at predicting where
things are heading, how demand for our products or services will change over time, and what
will prompt that change. Until recently, trend analysis and prediction often came down to ‘gut
instinct’. Now, big data is taking a lot of the guesswork out of that process.
Trending topics flash across Facebook and Twitter every day, making it easier than ever before
to work out what people want. Services such as Trendera and Trend Hunter collate trend data
and use it to answer specific questions for businesses. In retail, online and offline customer
behavior can be measured to microscopic detail – even down to how someone moves around
the physical and online store. That data can be compared with external data, such as the time
of the year, economic conditions and even the weather, to build up a detailed picture of what
people are likely to buy, and when.
2

Checking out the competition
In the past, understanding your competition was limited to industry gossip or looking around
rivals’ websites or shops. Some might go as far as pretending to be customers in order to find
out more about a competitor’s service or product. These days though, you hardly need to
leave your desk to find out what the competition is up to; financial data is readily available,
Google Trends can offer insights on the popularity of a brand or product, and social media
analysis can illustrate popularity (i.e., how often a company is mentioned) and show what
customers are saying. Again, Twitter is a particularly transparent place to start. All the
information you gather can be compared with your own brand; for example, does your
competitor get more mentions on Twitter? How do their Twitter conversations with
customers compare with yours? Keep in mind that it’s also easy for your competitors to glean
more information on your business than ever before. There’s no way around this, but you can
stay one step ahead by keeping up-to-date on the latest big data technologies and uses.
3

Improving operations
Big data is also increasingly used to optimize business processes and everyday operations.
With any business process that generates data, you can use that data to make improvements
and generate efficiencies.
For manufacturing or industrial companies, machines, vehicles and tools can be made ‘smart’,
which means they can be connected, data-enabled, and constantly reporting their status to
each other. By analyzing this data, organizations can gain real-time visibility into their
operations and look for ways to increase efficiency. Retail companies are able to optimize
their stock keeping based on predictions generated from social media data, web search
trends, and weather forecasts. This allows stores to stock up on the most popular items,
ensuring they don’t miss out on sales and reducing the amount of unwanted stock lying
around. Supply chain or delivery route optimization is another business process that is
benefitting heavily from big data analytics. Here, GPS and sensors are used to track goods or
delivery vehicles and optimize routes by integrating live traffic data, and so on.
4

Recruiting and managing talent
Data can help you find the most successful candidates, identify the best recruitment
channels, and help to better engage existing employees. Most businesses already generate a
wealth of HR-related data: absenteeism figures, productivity data, personal development
reviews, and staff satisfaction data. As well as this, companies can now access so much more
data that wasn’t available before: data from recruitment sites, information from sensors in ID
badges, social media data, etc. All this information can be analyzed to gain insights that were
never available before.
5

Tweaking your business model
Data can even become a part of your business model, leading to exciting new ways to
generate revenue. Facebook, for example, is free to users but has historically generated
income from advertising. Now the company is capitalizing on the huge amount of data it has
on its users, by making certain data available to businesses. Some of this data is available for
free but some of it you have to pay for, creating a new income stream for Facebook. There are
many opportunities now for small businesses to monetize the data they are generating by
providing value added services or selling data to customers or third parties.
6

Unlike external data, which is available to
the public, internal data is exclusive to a
brand. It’s information that is not available
anywhere else. This is just one of the
reasons why this data is so perfect for
brand storytelling. Content based on
original data attracts attention and comes
with several other benefits.
THE BENEFITS OF
USING INTERNAL DATA
IN MARKETING

Rather than regurgitating what the
rest of your industry is saying in their
content, sharing your company data
allows you to present something
unique. By opening the kimono and
sharing your internal data, you’re
offering something that can’t be found
anywhere else which is extremly
attractive to your consumers.
1
EXAMPLE: SPOTIFY
Spotify, a streaming music service app, collects data all day every day. As listeners select
songs, playlists, and artists, the music app collects information on the user’s musical choices
as well as their location and demographics.
Spotify has unique access to listeners’ habits and traits; this allows it to create original, unique
content that isn’t available anywhere else. The company shares the information and trends it
uncovers in its data on the Spotify Insights blog.ORIGINALITY
It even created a
world “Musical
Map” that shows
the listening habits
and preferences of
people in cities
around the world.
By tapping into its
resource of
internal data that
no other brand or
organization has
access to, Spotify
creates original
stories and insights
that can’t be found
anywhere else.

CONSUMER VALUE
Audiences enjoy educational content
that shows them something they
haven’t see before. By using internal
data, you can create valuable
content that educates, introduces
new ideas, and/or provides support
for existing ideas.
2
EXAMPLE: INDEED
With thousands upon thousands of job listings posted on its website, Indeed.com can collect
data on job trends across the world. Instead of keeping this data to itself, Indeed shares the
data on its Job Trends page.
The Job Trends page shares information on a job’s number of postings over time. It also
shares postings per capita, job market competition across the country, and growth and
decline rates of major employment industries.
The graphs and charts provide
valuable information for both
job seekers and employees. By
providing useful insights,
Indeed can help its audience
while attracting users to the
platform and growing its
brand recognition.

OPERATIONAL
TRANSPARENCY
More and more customers crave
transparency from the brands they
love. Opening up your organization
and sharing insider information is a
way to give customers an inside
look into your brand.
3
EXAMPLE: GROOVE
Groove is a help-desk software solution for small businesses. Groove’s clients rely on it for
providing vital support to their business. So to display to its clients how it helps, Groove uses
data and transparency to show how it supports its own business.
On the Groove blog, team members regularly share statistics and data from its operations. In
the “Startup Journey” section of the blog, they reveal “everything on our journey to $500k in
monthly revenue.” In the post, “How We Measure and Optimize Customer Success Metrics in
Our Saas Startup,” they dig deep into its internal metrics and share onboarding, conversion,
churn, and active user data.
By peeling back the layers of its
business and opening it up for the
world to see, Groove is using data
to be transparent, show its
authority, and build trust with an
audience who it hopes will turn
into customers.

CONSUMER TRUST
Customers crave transparency
because it helps them trust brands.
When you share internal data and
an inside look at your business or
operations, it shows that your
company values openness. This lets
your customers know you have
nothing to hide, which builds trust
and lasting consumer-brand
relationships.
4
EXAMPLE: HUBSPOT
HubSpot is a leading inbound marketing software that also acts as a leading inbound
marketing resource. The company provides both software and educational content to help its
clients achieve success.
HubSpot shows that it is a reliable resource in its industry by using content to build trust. It
shares free content marketing information through dozens of ebooks, white papers, blog
posts, and its annual State of Inbound Report.
The State of Inbound is an in-depth resource that includes results from HubSpot’s annual
survey of marketers. The report shows that HubSpot knows what it is talking about and that it
has access to leading information in the industry.
By sharing a deep
resource of marketing
knowledge and insights,
HubSpot shows
customers why they
should turn to it and trust
its products and
knowledge when they
have content marketing
needs.

BRAND RECOGNITION
In a content-filled world, getting
brand exposure is difficult. It’s hard to
stand out. But original, internal data
allows you to cut through the noise
and get your brand in front of a larger
audience. Audiences are introduced
to your brand and left with an
impression that will allow them to
recognize and remember your brand.
5
EXAMPLE: JAWBONE
Jawbone is a wearable technology company that builds “products and software platforms
powered by data science.” As its mission statement expresses, Jawbone is a company rooted
in data. The company uses data to improve its products, help its customers, and spread
awareness about its brand.
The Jawbone blog regularly features stories with trends in data collected by Jawbone’s
wearable devices. By using information that is exclusive to its database, Jawbone can gain
brand exposure by sharing content that is relevant to a large audience.
By pulling interesting and
unique stories from its data,
Jawbone can catch the
attention of online audiences,
spread awareness about its
brand, and build brand
recognition with each data-
focused blog post.

COMPANY WORTH
Internal data is also useful for promoting
your brand and showing its value. You can
use your internal data to show how your
products or services benefit your
customers. The data can show how you
differ and stand out from competitors,
putting your brand in a position to
resonate with potential customers.
6
EXAMPLE: KASPERSKY
With more than 400 million users across the world, Kaspersky is a large antivirus and internet
security software provider with access to tons of information on cyber threats.
Kaspersky doesn’t keep this deep pool of data to itself. Instead, it shares the data to help its
clients and show the value of its products. By highlighting stats on cybersecurity risks, it
shares an insider look at its industry while educating customers on why they need antivirus
software.
The antivirus company has a blog, Kaspersky Labs, that is filled with resources on digital
security tips, studies, products, and trends. It also created the Cyberthreat Real-Time Map, an
interactive website that allows audiences to see what type of digital threats are happening
around the world.
By sharing findings from its
database on cyber security,
Kaspersky can expose the
problem that its brand solves.
This shows its value to its
customers while educating
them on an interesting topic.

www.smartdata.howwww.facebook.com/smartdatasr

Module 2 - Improving current business with your own data - Online

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Module 2 - Improving current business with your own data - Online