M. Eugenia Pérez-Pons1*, Alfonso González-Briones1,2, and Juan M. Corchado1,2,3,4
1BISITE Research Group, University of Salamanca. Edificio I+D+i, Calle Espejo 2, 37007, Salamanca, Spain.
2Air Institute, IoT Digital Innovation Hub, Carbajosa de la Sagrada, 37188, Salamanca, Spain.
3Department of Electronics, Information and Communication, Faculty of Engineering, Osaka Institute of Technology, 535-8585 Osaka, Japan.
4Pusat Komputeran dan Informatik, Universiti Malaysia Kelantan, Karung Berkunci 36, Pengkaan Chepa, 16100 Kota Bharu, Kelantan, Malaysia.
Corresponding Author’s Email: eugenia.perez@usal.es
DOI : http://dx.doi.org/10.13005/ojcst12.02.01
Article Publishing History
Article Received on : 17 June 2019
Article Accepted on : 09 July 2019
Article Published : 10 Jul 2019
Article Metrics
ABSTRACT:
The following work presents a methodology of determining the economic value of the data owned by a company in a given time period. The ability to determine the value of data at any point of its lifecycle, would make it possible to study the added value that data gives to a company in the long term. Not only external data should be considered but also the impact that the internal data can have on company revenues. The project focuses on data-driven companies, which are different to the data-oriented ones, as explained below. Since some studies affirm that data-driven companies are more profitable, the indirect costs of using those data must be allocated somewhere to understand their financial value14 and to present a possible alternative for measuring the financial impact of data on the revenue of companies.
KEYWORDS:
Case-Based Reasoning; Data-Driven Companies; Financial Valuation; Recommendation Systems
Copy the following to cite this article:
Pérez-Pons M. E, González-Briones A, Corchado J. M. Towards financial valuation in data-driven companies. Orient. J. Comp. Sci. and Technol; 12(2).
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Pérez-Pons M. E, González-Briones A, Corchado J. M. Towards financial valuation in data-driven companies. Orient. J. Comp. Sci. and Technol; 12(2). Available from: https://bit.ly/2XYNgzC
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Introduction
Nowadays, data-driven companies are
widespread in all business contexts.19, 17, 4 How-ever, the real
implications of being a data-driven company are huge and not all the companies
can easily transform into one.5 A data-driven approach is one in
which companies organize and analyze their data carefully in order to improve
their customer service and predict future product demands. One of the most
essential elements of data-driven companies are big data architectures.6
When we say Big Data we refer to large volumes of data, both structured and
unstructured, that are generated and stored on a day-to-day basis. Although
what is really important is not the amount of data a company has, but how it
uses the data it collects and the knowledge it is capable of extracting, as well
as how this knowledge helps the company improve and grow.
In the process of collecting and organizing
the information, many big data projects fail because companies develop rigid
architectures which do not have the flexibility to adapt to possible future
changes.3 Many companies generate lots of
data which they manage through business intelligence applications or
dashboards, such companies can be called data-oriented but they are not
data-driven because they only analyse a part of all the possible data that
could be collected. Data-oriented companies collect real-time and historical
data for monitoring, however, unlike data-driven companies, they do not look
for the causes, nor do they make recommendations or decisions on the basis of
this information.16
Most of the analyses of data-driven
organizations are oriented at the future, they use predictive models to
optimize spending or to respond to an element in the supply chain that is at a
point of failure.18, 10, 8 They help prevent
the loss of clients to competition through different strategies such as the
collection of external data or by offering a small discount to customers to
persuade them not to leave. Certain tools and skills are necessary in order to perform these activities; However, above all, there must be a
business culture that promotes the use of data as a basis for decision-making.
It has not been demonstrated yet that data-driven companies benefit more from
being oriented both to customers and to the internal organization, nevertheless
it seems quite obvious that the decisions that are based on data will always
have a better result. Therefore is suggested a CBR architecture to propose an
alternative.
Presumably, customer- or user-related data
have a direct impact on the company sales and revenues. The data related to the
main activities of a company, such as feedback from the users on its products,
is important in order to be able to calculate the costs and profits of a
company. The aim of this work is to propose an alternative methodology for the
measurement of financial impact that the collection, processing and storage of
data generated by users has on a company. In the last decade, news reports have
stressed the effectiveness of data-driven companies, however a real financial
demonstrative or conclusive valuation method has not been proposed yet.
However, there are some methodologies in the state of the art that make it
possible to value the assets of a company, such as the Data – Information –
Knowledge – Wisdom theory1 which analyzes the value of data at a
certain period in time (since it’s raw Data to Wisdom) and valuing the data of
a company as an asset,15 are some of the most known and
representative methodologies. The actions carried out at each stage of the CBR
architecture are detailed below.
Background
The costs that data capture, storage and
processing entail are never the same for all companies. The amounts of data
collected nowadays are big, however, with the growth of IoT and new
technologies, those volumes will increase dramatically and all companies should
be able to see where the break-even point is for their data. As mentioned
before, since it is very hard to identify the point at which data has a certain
value, when considering data as an asset, it can be evaluated with the DIKW
methodology or by valuating the company as an asset.
That DIKW methodology is capable of
calculating the value of the data at any point in its lifecycle. Also, the data
have to be in the life-cycle like other organisational assets, information has
a cost (collection, storage and maintenance) and a value (how it contributes to
the revenue of a company). However, this is where the similarity ends.
Information does not follow the same laws of economics as other assets do,
because it has some unique properties which must be comprehended if their real
value is to be measured correctly. (Glazer, 1993)9 State that data
can be shared between multiple business areas and the cost will be the same as
if a single party had exclusive use of the information. Another argument
against valuing data as an asset is that we will miss the fact that duplicating
data entails indirect costs as well as storage costs, these costs are not very
noticeable nowadays but will be when the volume of data will increase.15
Due to those research gaps, the main objective of this work is to propose a
methodology for analyzing the financial impact that data processing has on
companies. The external data collected from users is going to be used in the
development of a method for real conclusive economic assessment or prediction,
that is, for the measurement of financial impact (ie: greater profit, lower
costs,…).
It is sometimes argued that recommendation
systems do not help consumers discover new services or products and only
reinforce the popularity of the already popular ones.7 However, what
is clear is that they are usually beneficial for the companies that use them.2,
13 Therefore, recommendation systems can serve as a tool for the
measurement of financial impact on company benefits.
Proposed
CBR Architecture
This work presents a hypothetical CBR
architecture for the prediction of the financial impact that recommendation
systems have on a company. The methodology will be based on a set of variables
that are considered key to understanding the performance and the evolution of a
company in a given sector. We have chosen several Key Performance Indicators as
our variables which will make it possible to extract company performance
patterns and compare them with different companies. Those variables will
indicate the financial evolution of the company, the human capital and market
concentration. Companies that use recommendation systems and base their company
model on it, need to collect large volumes of information to train the
artificial intelligence models which help offer products or services adapted to
the client’s profile.
 |
Figure 1: CBR cycle for the prediction of the financial impact of introducing a recommendation system
Click here to View figure
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Some business models view the real-time
data generated by customers as the added value of companies. Nowadays, we are
capable of extracting value from all types of data as they all contribute to
improve business. Nevertheless, it is true that the collection of customer data
and customer flow is essential, without those elements companies would not be
able to become as competitive. For instance, any social network based on a
localization service requires constant information updates and algorithms that
process information in real time.
Everyday more data is created and stored
around the world, i.e: Google processes over 40,000 search queries every second
on average, which is 3.5 billion searches per day and 1.2 trillion searches per
year worldwide. As Google’s director of research Peter Norvig, puts it: “We
don’t have better algorithms. We just have more data”. The storage,
processing and analysis of data entail costs for the companies that collect
them. A small mistake in data mining can have serious implications, leading,
for example, to the poor performance of the algorithms that are going to be
trained with incorrect data.
To measure the financial impact that
recommender-system-facilitated data processing has on enterprises, it is first
necessary to study the variables that allow for the development of cases in the
knowledge base of the proposed architecture. The following set of variables
must be used in the description of cases: (i) Company Sales, (ii) Company Earnings,
(iii) Staff Costs, (iv) Cash Flows, (v) Company Size, (vi) Company Sector, (vii)
EBITDA and (viii) Company Location .
Figure 1 shows
the flow of the proposed theoretical architecture which makes it possible to
obtain a solution on the basis of information contained in past cases.The
proposed architecture must be capable of obtaining case solutions for various
companies. In this respect, it is necessary that the CBR architecture can be
suitable for any company, regardless of the sector in which it develops its
economic activity.
To best adapt the solutions of the
architecture to the input data, the system’s case memory separates the
information of each company into sectors. In addition, to calculate the
financial impact, the architecture must have an ANN for each sector, which must
be trained with the input variables (the ones listed above).
In the Retrieve step, the system recovers
the neural network which has been trained with data from companies in the same
sector. Having obtained the cases for a concrete sector, data is pre-processed
to eliminate the information that does not provide relevant cases for study.
In the Reuse step, the neural network will
estimate the economic impact of a recommen-dation system on a company. Once the
objective solution has been reached and the impact of using a recommendation
system is known, the ANN will be trained at regular intervals automatically as
the amount of input data increases through the addition of new cases to the
database. To validate whether the training of the database has been successful,
the ANN will train the database with 70% of its data and will use the remaining
30% to test its functionality.12, 11
The learning process of the CBR
architecture is performed in the Revision step. This review process must be
carried out by a human in order to determine if the obtained economic impact
corresponds to the company’s input data (This process will be carried out
according to personal criteria and experience, nevertheless different
adjustments can be made). Otherwise, the case is discarded and the CBR cycle
ends.
In the Retain step, regardless of whether
the case had to be modified or not, if in the end it has been accepted it is
stored in the Knowledge Database as a possible solution to a future problem.
Conclusions
and Future work
The conclusion drawn from our research is
that we still don’t have a real valuation method and it is something that
companies will need shortly as the amount of data being stored by companies
will double. It is necessary for them, therefore, to have the tools necessary
to filter the data or to be able to measure their real cost at any level.
A real database will be used to test the CBR methodology on Spanish companies that do not operate in the stock market. The financial information of the companies will be anonymized and then used in the CBR to train the system and find possible patterns that will demonstrate the economic profit that comes from having a recommender system or correct data processing in a company. In the first step, the data of Spanish companies is going to be used, however, at a later stage it would be preferable to use the information of different European companies to identify tendencies in different countries and sectors.
Acknowledgments
This work has been developed thanks to the project “Virtual-Ledgers-Tecnologías DLT/Blockchain y Cripto-IOT sobre organizaciones virtuales de agentes ligeros y su aplicación en la eficiencia en el transporte de última milla” ( ID SA267P18 ), financed by Junta Castilla y León, Consejería de Educación and FEDER funds.
Conflict of Interest
The authors declare no conflict of interest.
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