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Connected Business Insight
Quantifying the value of Internet of Things data
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January 2017
mm1 – Die Beratung für Connected Business
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2
IoT
Data
Internet of Things data is growing in volume & value, but still
underutilized by businesses, who often think in internal data silos only.
To accelerate the development of IoT data driven business models,
companies need to start aggregating IoT data over more than one
corporate silo and adapt their business case evaluation processes.
mm1 – Die Beratung für Connected Business
Raw Data
Data Market PlaceData
Supply
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101001001
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Data markets are essential for data business models, but
not possible without the ability to define the value of data
3
IoT data driven business models often require data from different sources (i.e. car data + weather
data + petrol price data) or data of a minimum critical mass (i.e. 5% of all car data). Companies
therefore need to start exchanging and trading data, i.e. in a data market place. But to work, any
data market place needs to first be able to value data on both the supply and demand side.
Essential Service
Enables data trading
Enables data aggregation
Payment
Rich data
and Use Cases*
Payment
Data
Demand
Data Valuation
Definition of value of data
Setting of dynamic pricing for
supply and demand
mm1 – Die Beratung für Connected Business
Defining the value of data is also crucial for corporate data
driven IoT business model development processes
4
Unlike data centric companies, old-economy companies typically need to first be able to define the
value of IoT data in order to develop investment ready business cases. The ability to define this
data value is therefore crucial for corporate business model development.
Requires a method to
define the value of data
in order to calculate data
driven revenue
Data-centric companies
Collect
Data
Get Upfront
Investment
Identify
Business
Model
Old-economy companies
Get
Investment
Business
Strategy &
-Case
Collect
Data
Allows for data value
definition at a later
stage and by
experimentation
The approach to IoT business model development differs significantly between the majority of old-
economy and data centric companies.
New Method
Required
mm1 – Die Beratung für Connected Business
Different methods for IoT data valuation exist, the ‘optimal’
method depends on the situation and can change with time
5
There are different methods available to calculate the value of data, the optimal methods increases
in complexity with & depends on the maturity of the market.
Data Valuation Methods
Non Monetary MethodsValue (Importance)
Monetary Methods
Price (€)
Intrinsic
Value
accuracy,
completeness,
accessibility, scarcity
Business
Value
relevance, accuracy,
completeness,
timeliness
Performance
Value
Influence of data on
one or more KPIs
Cost
Value
Expenses for data
purchase &
gathering
Economic
Value
Difference in e.g.
sales with and
without data
Market
Value
Price of data in
market
User
Value
How much is
user willing
to pay
Prudent
Value
Determined
by stock
market
Lifetime
Value
Calculated
by market
forecast
Trade
Value
Determined
by market-
place
Business
Initiative
Value
Schmarzo
Method
Technical
Value
Takes into
account data
quality
Demand
Value
Takes into
account No.
of data calls
mm1 – Die Beratung für Connected Business
The accuracy and value of data valuation increases with the
level of information available about market mechanisms
6
Costs. Evaluates
consumption of material
and immaterial goods
required to collect and
manipulate the data
Value. Reflects the
importance of the good –
for example in form of the
number pull requests for a
data set
Price. Most difficult to
generate level of
information: how much are
certain customer groups
willing to pay for the data or
service generated from it,
in €?
Cost Value Demand Price
Demand. Qualitative
measure of how high the
demand for a good is, in a
discrete range.
hard to obtain informationeasy to obtain information
The more information is available about the specific market mechanisms related to the data, the
higher and more accurate it can be valued. Lacking market information, cost based pricing is the
only available option to value data. The desired option is value based pricing.
potential valuation of data increases with access to information and market maturity
mm1 – Die Beratung für Connected Business
Calculation of the internal value of IoT data based on a
supply & demand model according to mm1
7
The data valuation scheme balances supply & demand and takes into account data quality and the
number of use cases to value data. The method is ideally suited for IoT data market places.
Data Supply Side Data Demand SideData Valuation Tool
Dynamic* Data Valuation
Internal Value(according to mm1)
VVC = ∑1-U (WP x PUC x
WQ x QUC x WS x SUC x WI
x IUC) – CWeight
Factor
Quality of data (Q) Completeness (Qc)
Accuracy (Qa)
Timeliness (Qt)
Frequency (Qf)
Consistency (Qco)
Validity (Qv)
Uniqueness (Qu)
Importance for use case (I)How essential is the data for the UC?
Number of use cases (U) Use cases (UC) data is used in
Share of data for UC (S)Which % of data is used in UC
Price for use case (P)How much demand side pays
Cost of Data (C) Related to availability/quantity
of data. Includes operations.
supply demand
market
equilibrium
* Dynamic here refers to the fact that the value of data changes dynamically depending on time, context etc.
mm1 – Die Beratung für Connected Business 8
Value of sensor data in Berlin Value of sensor data in Rome
VVC = ∑1-U [PUC x (WQc x QcUC x WQa QaUC x WQt x QtUC x WQf QfUC x WQco
QcoUC x WQv QvUC x WQu QuUC) x WS SUC x WI IUC] - C
Highest timeliness
valuation
VVC = ∑1-U [PUC x (WQc x QcUC x WQa QaUC x WQt x QtUC x WQf QfUC x WQco
QcoUC x WQv QvUC x WQu QuUC) x WS SUC x WI IUC] - C
Low insurance margins in Germany, customer pays less
Reduced
timeliness
valuation
U = 7
High margins in Italy, customer pays more
Sensor data available to insurer within only 10 seconds Sensor data available to insurer only after 24h
Data can be sold for 6 further use cases Data can only be sold for 5 further use cases
Higher insurance premiums result in
higher price data can be sold for
Lower insurance premium
margins result in lower price
data can be sold for
-+ -+ -
+
Value of data in Berlin Value of data in Rome>
Example of internal data value calculation for a usage
based insurance use case (Pay How You Drive, PHYD)
Main criteria for valuation in this hypothetical example are timeliness, use cases & price. Valuation
heavily influenced by insurance margins: the higher the margins, the higher the valuation.
U = 6
mm1 – Die Beratung für Connected Business 9
VVC = ∑1-U [PUC x (WQc x QcUC x WQa QaUC x WQt x QtUC x WQf QfUC x WQco
QcoUC x WQv QvUC x WQu QuUC) x WS SUC x WI IUC] - C
Highest timeliness
valuation
VVC = ∑1-U [PUC x (WQc x QcUC x WQa QaUC x WQt x QtUC x WQf QfUC x WQco
QcoUC x WQv QvUC x WQu QuUC) x WS SUC x WI IUC] - C
High chance of black ice
Reduced
timeliness
valuation
Black ice use case almost never relevant
Sensor data available early, 1 minute after onset of black ice Sensor data available late, 30 minutes after black ice onset
Low relevance results in low price data
can be soldHigh relevance results in high
price data can be sold
-+ -+
Value of data in Innsbruck Value of data in Miami>
Example of internal data value calculation for black-ice
warning use case: the value of data in Innsbruck & Miami
Main criteria for valuation in this hypothetical example are price, timeliness & usage. Valuation
heavily influenced by usage: the more additional use cases, the higher the valuation.
Value of sensor data in Innsbruck Value of sensor data in Miami
Data can be sold for 4 further use cases Data can only be sold for 2 further use cases+ -
U = 5 U = 3
mm1 – Die Beratung für Connected Business 10
Outlook: How companies can conquer the IoT data market
using different data valuation methodologies
Depending on the IoT data readiness level of the company and its positioning within the IoT
market, different methods can be used to accelerate data driven IoT business development.
A company is in the process of
discovering where novel
applications for its IoT data for
internal use can be found.
Suitable Methods include:
• Intrinsic Data Value
• Technical Data Value
• Performance Data Value
A company already utilizes IoT
data for internal applications and
is exploring opportunities for
novel IoT business models.
Suitable Methods include:
• mm1 Data Valuation Method
• Business Data Value
• Demand Data Value
• Schmarzo Method
A company is cooperating with,
setting up or operating a IoT data
market place.
i
Suitable Methods include:
• mm1 Data Valuation Method
• Market Data Value
• Lifetime Data Value
Discovery phase1Business Model
Development Phase2
Market Place Development
& Operations3
Increasing utilization of IoT Data Value
To increase the utilization of their IoT data, companies need to rapidly progress to phase 3 of the
IoT data readiness level & start exchanging IoT data across corporate silos to develop improved IoT
data driven business models. The mm1 Data Valuation Method enables them to achieve this goal.
mm1 – Die Beratung für Connected Business
mm1 is the Consultancy for Connected Business
11
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