asset has different characteristics from those of the other assets both in the way of guarding it, and in the way of valuing it and ensuring its proper use. The greatest value of this
asset is in the use give it; this is how its management is not only oriented to
the use of tools and technology that allows its registration, consultation, and
processing, but it is essential to design strategies to ensure its reliability,
security, and privacy to obtain the most precious: the achievement of business
initiatives from the accurate use of data.
For this purpose, it is
necessary to design a plan to follow that initially allows having the clarity
of the business initiatives that will be driven by data and what will be the
way in which data can drive these initiatives. Having the above clear, you can
identify the data that is required to manage and analyse to comply with each of
the identified initiatives. According to Marr (2020), after being clear about the needs of the business, the data that will
support these needs, how the data will be analysed, how it will be visualized
and what technological tools are required, it is necessary to define an action
plan that allows to carry out the data strategy designed. Likewise, it is
important to have a solid business case to present to the organization in which
the advantages of using data can be clearly identified and how these advantages
are related to the business objectives. Some companies have defined
data strategies seeking to make these the main input for decision-making, others
have sought to improve operability and processes, and others have created new
services or products from the data that are generated by themselves Varshney and Allen (2020). This does not mean that you can only take advantage of the data that
is generated within companies. In some cases, strategies have been designed in
which it is decided to acquire data from suppliers or capture the data through
new technologies; for example, the capture of data from social networks to
understand customer behaviour, the use of sensors on the Internet of Things,
prediction, or segmentation data from data science, among others. The most
important thing is to be clear about the use that will be given to the
information and the benefit that will be obtained from it. Today, it can be evidenced
how some large companies through a correct management of their data, have
managed to increase their revenues, either by reducing costs or improving the value
offer through new products and services designed to suit their customers. In
turn, other companies have used their data to increase their productivity,
redefine their processes looking for greater efficiency, improve their
security, define business strategies, or define new products and services among
others. One of the success stories of companies that manage their data as an asset is that of the BBVA bank, a recognized company in the financial sector worldwide, which began to focus on ensuring the quality, veracity and storage of the data that allows to support the decisions that are made in the business. The data becomes the source for the construction and definition of financial products and services to the measurement of customers. In one of the internal events held by the bank to show the status of the company's significant projects, a web platform was presented that works with transactional data and converts anonymous data from purchases made with debit or credit cards into useful information for its customers' businesses. In the same way, the renewal of the home insurance system that is used in the bank's offices was shown. In this system, an improvement was implemented from the management of external data, with which they managed to streamline the process; the customer only gives his full address, and the system retrieves the rest of the data from the official cadastre. Tena (2018) In the case of research, a company from the financial sector of Colombia was selected that as aspires to be the financial advisory and services company of preference for clients, assuming the challenge of making their financial projects a reality. For this, from the different business units have set goals and objectives that are aligned with the company's strategy and that seek to be achieved through initiatives that will improve and strengthen the tools and professional teams that manage the products in which customers can invest. Since its foundation, the
company has had an important growth in Latin America, positioning itself as one
of the market leaders in the financial sector and in Colombia, being the
leading company among stockbrokers. This leadership has brought with it
critical challenges in all areas of the organization, all of which are related
to data. One of the biggest challenges is to have a transversal vision of the
company in each country and from now on, a regional vision on the IT projects
that leverage the initiatives and business objectives. Similarly, the organization
has had a significant growth in the value of managed assets, the number of
collaborators, the number of applications that support the operation, and in
the volume of data that is generated throughout the different processes, also.
The objectives of the business unit are focused on having a constant growth in
both clients and managed assets, which brings with it a continuous evolutionary
change in the information systems that support the products offered by the
firm. That is why, from Asset Management and each of the business units,
projects are defined to improve or evolve the applications that need to be
modified to support the new products of the business or the modifications that
were defined for the existing products. Similarly, at the regulatory level,
requirements are defined with which the business logic that has been
implemented in the company's information systems is impacted, which have a compliance
date for their implementation. The management and execution
of these projects generate data that must be managed in an organized and timely
manner by TI team, so that they can be used later in the generation of
information. This information allows identifying the progress, impacts,
priorities, costs, capabilities of the relationship area and the software
factories, which manage and implement the requirements specified by each
business unit. Likewise, this information becomes an input that supports
decision-making related to IT projects. Additionally, the company is
currently implementing a regional model that allows it to have standardized
processes despite the particularities that each country in the region has in
its organizational structure and at the regulatory level. Due to this, at the
transversal level of the company it is necessary to have an overview of the
requirements that are in progress and of the possible projects that in the
short term will begin their implementation process in each country of the
region. IT area of the company, there
is a business relationship management (BRM) which is responsible for managing
the initiatives that involve some type of technological implementation and that
are defined by the different business units. Currently, the relationship management has few standards on the processes of collection, storage, analysis, and use of the data generated in the execution of its tasks. Likewise, some structured data and most unstructured data are not being analysed in their entirety. For this reason, there are opportunities for improvement in the process with which you can obtain greater confidence in the data and a better use of it. As well, it is possible to have improvements in operational efficiency, since, on some occasions, the necessary data have not been available and in a timely manner so that the sponsor or interested parties of the initiative can make-decision on their requirements. The lack of standards and good practices in data management has generated delays in the decisions that must be made on the development of initiatives. Also, there have been impacts of delay in meeting the expectations that the business has on the execution of these. These delays directly impact on the business objectives because they prevent the contractual compliance established with the clients to manage the different investment assets and put at risk the reputation of the company if any regulatory requirement is not met. It is worth highlighting the importance of clearly knowing what are needs of the organization are in terms of business, remembering that the data will not make any sense if from them you cannot obtain the greatest value that allows the organization to meet its objectives. Data requirements are derived directly from business requirements as well as it is necessary to ensure that processes and technology allow the creation, transformation, storage, assurance, and use of data according to business needs. Likewise, it is important to highlight the importance of data for any organization and with this understand that there is a need for them to be managed correctly; "Within and between organizations, data, and information are essential to running business" Dama (2020). It is vitally important for companies that their decisions are backed by data and not only by the experience of business executives. With the above, the need to design an effective data strategy is identified and the relationship management of a company in the financial sector is taken as a case study, in order to identify the necessary data to support the decision-making of the organization. In the same way, the strategy defines the necessary guidelines of a data management and governance program that allow to meet the objectives of the designed strategy. This allows the company's business units to have higher quality information and make more accurate and timely decisions, managing to respond to the needs of the market, customers and the different external changes that may affect the business. Similarly, in relationship management you can improve your information management processes, looking for them to be more efficient and of higher quality. Data management is one of the
most important contemporary tools to facilitate processes within organizations
and thus enable decision-making. For this reason, a bibliographic survey is
carried out to different projects and research experiences in different
productive sectors at the international and national level in relation to
information management. This bibliographic search exercise was done through
tools such as: the repository of degree works that the Colombian School of
Engineering has, electronic databases such as Scopus, Academic Search Ultimate,
and Google Academic. First, reference is made to
the research carried out in the electric vehicles sector by Jichao Hong, Zhenpo
Wang and Peng Liu, in Beijing (China) in 2018 which was based on the forecast
of thermal failures of the battery system for electric vehicles based on the
use of data. A thermal management system in batteries is necessary and
essential because high temperatures affect driving performance and safety in
electric vehicles. This research presents a
method of real-time diagnosis and forecasting of thermal failures in batteries
caused by thermal leaks, through monitoring of battery temperature during
vehicular operations. Much of the monitoring of this voltage is done in real
time. The data is derived from the National Center for Electric Vehicle Service
and Management (NSMC-EV) in Beijing which has the function of monitoring and
capturing data from electric vehicles that are in circulation such as the
voltage and temperature of the battery system. In addition, a thermal safety
management strategy for thermal leaks is presented under the Z-score approach.
The results illustrated that the proposed method can accurately forecast both
the time and location of the temperature fault within the battery packs of
electric vehicles. The feasibility, reliability, and stability of the
prognostic capacity of the proposed method were also discussed and verified by analysing
extensive monitoring data. It could be concluded that
the proposed method is flexible and applicable to several systems in which
abnormal fluctuations occur, regardless of data types and fields of
application, thus having a potential for widespread application not only in the
electric vehicle sector, but also in other areas with complex environments that
fluctuate abnormally Mei et al. (2018). Secondly, Daniel K.
Papiernik, Dhruv Nanda, Robert O. Cassada and William H. Morris in 2000,
presented a case study based on the implementation of a Data Warehouse in the
Virginia Department of Transportation (DTV), with which they sought to become
the most effective public agency in the United States and with a one hundred percent
customer-oriented approach. Likewise, this implementation was part of the
company's technological investment strategy and aimed to store business data
that was in legacy systems or external sources, to have a set of services based
on data that can acquire, integrate, prepare, and manage them to allow access, visualization,
and analytical interpretation and thus support decision making. This initiative
was also driven by the intermodal transport efficiency law, which sought to
integrate transport systems, data standardization and accessibility to them. It
was concluded that the implementation of the Data Warehouse allowed to have
integrated data and access to the end user, improving reports, queries,
processing capacity and analysis. From this implementation, the best business
practices in data management within DTV have been institutionalized through the
area of data management Papiernik
et al. (2000). Every day there are more
companies that want to take advantage of data, generating information that
allows to ensure knowledge and thus obtain a competitive advantage in the
market, be more efficient and support the achievement of their objectives. This
is evident in the research reviewed, as well as in the various marketing
strategies that have been deployed through social networks, which have clarity
about our tastes and interests, among others. Currently, there are many
ways to capture information, today many sensors are used that are embedded in
different parts in the manufacturing processes, in cases such as the one
narrated by Bernard Marr in his book "Data Strategy" Marr (2017). It about the Rolls-Royce company which has assembled sensors in the
aircraft engines it manufactures, to transmit in real time, the operation of
the engine to the monitoring stations and with this to be able to detect
possible failures to also investigate and prevent some type of disaster. A key
aspect that Bernard Marr indicates and that must be considered with respect to
the information that is captured, is to be clear that the data that is
collected must address some business needs and must generate some type of value
for the organization and with this help the company to achieve its strategic
objectives. There are other very interesting cases such as the use of data in smart cities, where through cutting-edge technologies a lot of data is generated that supports different work fronts such as education, health, and mobility with the purpose of improving the quality of life. However, one can misinterpret the use of these technologies and use them for "technological fashions" hoping that, by implementing Big Data, Business Intelligence, or data science, among others, the problems will be solved on their own, without understanding the real purpose of the implementation of such technologies. The goal is not to collect data for the sake of collecting it, it must start from a clear purpose in which both the data necessary to support business initiatives and the way in which said data will be used to effectively achieve the indicated support planned. For Gallant and Fleet (2018) data strategy is defined in how an organization improves specific
business objectives by strategically using its data as assets. A strategy lies
between business strategy and data management or data governance strategy. It's
about how your organization will maximize its leverage on data to generate the
greatest business impact Gallant and Fleet (2018). Therefore, the beginning of
the design of the strategy could be considered with the definition of its
mission, vision, and objectives, highlighting which objectives of the strategy
will be focused on generating value for the company and being fully aligned
with the strategic objectives of the organization. Based on the understanding
of these components and the knowledge of the organization, its data, and its
capabilities in data management, it will be possible to define the activities
that will allow meeting the established objectives. 2. METHODOLOGY This research is framed
within the qualitative approach, since it is responsible for studying the phenomena
within their real and natural context, allows investigating not only about the
practice itself, but also about the role played by each of the participants and
other aspects that have relevance within the research process, as stated by Blasco and Perez (2007): "Qualitative research studies reality in its natural context and
how it happens, drawing and interpreting phenomena according to the people
involved" Blasco and Perez (2007). For Sampieri, qualitative
research provides depth to the data, dispersion, interpretive richness,
contextualization of the environment or environment, details, and unique
experiences. It also brings a "fresh, natural and holistic" point of
view of phenomena, as well as flexibility Sampieri (2018). In this sense, the qualitative approach offers a diversity of
instruments for the collection, collection, and classification of information,
essential aspects within research because they condense the data collected
through structured, semi-structured interviews and surveys. Data collection
occurs in the natural and everyday environments of the participants or units of
analysis. These instruments allow the
researcher to have a direct contact with the reality in which the investigated
phenomenon occurs, which at this point is not defined in its entirety, on the
contrary, it is once the exploration and description phase is finished, where
the aspects that are evidenced as transformation needs are problematized. In addition, the qualitative
approach allows tracking, investigate, identify, and describe the environments
in which the relevant aspects of the investigated phenomenon are developed, to
recognize the needs, relational situations, and dynamics of the process that in
no way constitute absolute truths. Therefore, it is sought from the qualitative, to account for the answer to the problem question How does the data of the relationship management support business initiatives? At this point it should be noted that, as this research arises and develops within the same dynamics of relationship management, it adapts to qualitative research, as it is developed in the real context of a company in the financial sector in Colombia. In qualitative research, the information or data that interests to be collected are concepts, perceptions, mental images, beliefs, interactions, thoughts, experiences, processes, and experiences manifested in the language of the participants, either individually, group or collectively. They are collected to analyse and understand them, and thus answer research questions and generate knowledge. The information collection instruments used in this project were semi-structured interviews, surveys, and documentary analysis (Requirements Formats, Test Formats, Heatmaps, Changos System). Semi-structured interviews:
The qualitative interview is more flexible and open. This is defined as a
meeting to discuss and exchange information between one person (the
interviewer) and another (the interviewee) or others (interviewees). In the
interview, through the questions and answers, communication, and the joint
construction of meanings regarding a topic are achieved. Interviews are divided
into structured, semi-structured or unstructured, or open. Semi-structured
interviews are based on a guide to issues or questions and the interviewer is
free to introduce additional questions to specify concepts or obtain more
information about the desired topics (i.e., not all questions are
predetermined) Sampieri (2018). To obtain the information
related to the objectives, goals and business initiatives, an interview was
designed, which was applied to the product manager of the Asset Management
business unit to understand the business needs, and likewise, identify the data
that is required to support decision-making related to the management of the
initiatives that drive the achievement of organizational objectives. On the other hand, another
interview was designed, which focuses on collecting information related to the
decision making that is made from the business units with the advice and
information managed by the relationship management. This interview was
addressed to the relationship manager leader, managers, and analysts. Within the process of
analysing the interviews, the questions asked were organized and the answers
given in each interview were transcribed in a matrix way, to analyse each of
the answers and thus be able to group them by topics. Likewise, answers were identified
that did not answer the question asked, but that in a certain way complemented
another question asked in the interview, in these cases that answer was
reclassified in the respective topic. Surveys are an information
collection tool where their answers allow data to be obtained to be later
analysed. Qualitative surveys are exploratory. Its main goal is to understand
the way a group thinks, their opinions, and their attitudes about a particular
topic. For this reason, a survey was designed to evaluate the level of maturity
of relationship management, in different areas that comprise information
management. To determine the level of maturity, a rating scale was implemented
for the answers to each question, which is in a range of one to four, where one
is Strongly Disagree and four Strongly Agree. To calculate the grade of each
question, the sum of the different answers is made and multiplied by five,
which is the maximum rating that a question can have. This value is divided by
the maximum rating that each question could have if all its answers were four,
this value depends on the number of answers given. Once the grade of each
question is obtained, the average is calculated between the grades of the
questions that make up each area evaluated, with this the level of maturity by
area is obtained. To obtain the level of general maturity, the average is
calculated between the grades of each area evaluated. Documentary analysis plays a
fundamental role in the project since it allows us to understand the way in
which the different processes and information within the company have been
built and documented throughout its history. It is essential to verify the
authenticity of documents and formats and the relationship they have in the
different processes. On the other hand, it is necessary to question: how is the
material or element linked to the problem statement? In the present investigation,
the following corporate documents such as requirements formats, test formats
and Heatmaps were considered. 3. FINDINGS To analyse the information
collected, the categories shown in Table 1 were defined, so that they respond to the objectives of this research.
Likewise, these categories were defined to identify the needs of the business
through its goals, objectives, and initiatives, since this knowledge is the
foundation of the data strategy. In the same way, it seeks to have clarity on
the decisions that are made from the business unit and from the relationship
management, which support those identified business initiatives. On the other
hand, the data category seeks to identify those that are required to make
decisions that support the fulfilment of the initiatives and likewise, identify
the uses that will be given to them.
In this category there are three subcategories related to the needs of the business are presented which are: Goals, Objectives, and Initiatives. In that sense, the vision of the company is presented below, as well as the goals and objectives that are in the Asset Management business unit, which was selected for the research work. Likewise, the business initiatives that will be supported by the data identified in the data strategy design process will be described. It should be noted that this business unit generates a large part of the company's revenue and is also managing assets valued at kind of ten billion dollars (figure as of June 2020). Through the interview with
the asset management product manager, it was possible to identify that in this
business unit, it seeks to position itself as a brand and become the most
representative Latin American investment manager in the region. Similarly, this
business unit wants to be the most important placement agent in Latin America;
a “placement agent” is that agent who is responsible for placing products from
other fund houses in institutional investors. Likewise, the interview
allowed to identify the objectives of the unit which are focused on the growth
at the level of assets managed in Colombia which is expected to reach 9%. As
well, it is sought that the flagship funds of Luxembourg, reach the figure of 1
trillion dollars. Another objective of the unit is to continue developing real
estate, infrastructure, and private debt practices, it wants to close another
infrastructure fund that allows financing 5G roads in Colombia, and we have the
first efforts in private debt that what we seek is to have a fund oriented to
the economic reactivation of the country. This is difficult to measure, but
what is wanted is to continue consolidating these practices. That is, to
continue growing our funds and even take out new ones. On the other hand, it was
also possible to identify the initiatives that are led by Asset Management,
which will help achieve these proposed objectives. The product manager of the
unit affirms that they are working on initiatives with which they seek to have
collective investment funds that have the best standards in investment
administration and management. Nowadays, solutions are needed at the
application level, at the system level that minimize the number of operational
errors that occur in the operations log. Likewise, solutions are needed that
help control and monitor different policies or limits that occur from the construction
of a regulation and the definition of an investment policy to the execution of
investments on a day-to-day basis. Likewise, it is required to have specialized
software that allows access to the entire universe of investible assets, today
the platform viewed in an objective way allows valuation of fixed income
securities, equity securities and what has been done through time is to
accommodate certain types of assets that resemble fixed income or equities.
This allows the further development of the practice of Alternative Latin
American Assets, among which are real estate and infrastructure practices. The different initiatives
identified in the interview with the product manager of the business unit
require some type of implementation or technological improvement. In this
sense, the data strategy defined in this research exercise is focused on the
decision-making that is made from the business unit to implement in a way
timely the initiatives that will allow achieving the proposed objectives. For
making these decisions, the main input is the information that is generated and
managed on the relationship management team. Therefore, in this research
it was decided to ask the interviewees about the decisions that need to be made
and the questions that need to be answered, to execute in an effective and
timely manner the initiatives proposed by the business unit. In relation to the category
of decision-making, two subcategories of analysis are presented, which are:
Business Unit and Relationship Management. The initiatives defined by the
business unit must be managed in the IT area by the team of relationship
managers throughout the life cycle of their implementation. Throughout the
management process that is carried out between the business unit and the relationship
managers, there is a joint interaction, which is focused on making the
necessary decisions to implement the initiatives that were defined in the
required time. Therefore, it was considered necessary to know what are those
decisions that are made from the unit that are related to the initiatives that
have a technological impact. For the Product Manager of
Asset Management, it is necessary that their initiatives have a correct
planning of the necessary capabilities for their implementation and that one of
these is always being advanced. Likewise, it is necessary to identify how each
of the initiatives developed improve the internal and external user experience,
and in turn, how much impact it has on customer satisfaction. At the same time,
it is necessary to continue learning to determine which initiatives are
important, which are urgent, which are those that have regulatory compliance,
and which have great impacts on the experience of users who interact with Asset
Management. On the other hand, from the business unit it is necessary to
identify the capabilities of the software factories that implement their
initiatives to know where there is greater capacity or availability of these
resources and thus have a better planning. Regarding the perspective of
the business relationship management, questions were identified that are asked
from the business units and those that originate throughout the demand
management process, also. The analysis carried out on the information collected
in the interviews shows that management needs to make decisions related to the
prioritization of requirements, the capabilities of factory resources,
managers, administrators, and users. In the same way, questions are generated
that allow decisions to be made that add value to the business, likewise, that
support decision-making on aspects such as response time and compliance with
the delivery dates of the requirements, the availability of users for processes
of information gathering and certification of tests, the traceability of the
requirements, capabilities of the
different resources, what are the costs and number of hours of effort for the
implementation of a requirement and what is the total time of the effort made
for the implementation of each of these. As for the category of data,
two subcategories related to these are presented, which are: identification and
use. In the analysis of this category, the data related to the business
initiative selected for the research exercise were identified, and in turn, the
data that is required to support decision-making related to the management of
business initiatives. The interviewees answered the questions related to this
category and evidenced the data described below as necessary. In reference to
the selected initiative, it aims to access the universe of investable assets
which allows the further development of real estate, infrastructure, and
private debt practices that strengthen the positioning of the asset management
brand. An alternative asset type can be real estate or infrastructure. The
types of real estate assets seek to obtain profit through any type of contract
that can be generated on a property. They can also participate in projects of
construction of business or commercial buildings and purchase of lots. On the
other hand, infrastructure asset types seek to make a profit from financing
works such as 5G roads in Colombia. The different types of assets require
defining an operational management model that allows you to record all the
operations that are executed daily, and likewise, define management models that
facilitate you to execute all the tasks they require for the fund to operate
correctly. Investments in these types of alternative assets are made through a
collective investment fund that in its investment policy has defined to buy
assets of this type. The alternative funds in turn have defined an investment
committee that is responsible for the control and good management over the investments
that are made from these. The second area of interest
was defined as “business”. In this area are concepts related to the selected
initiative such as “product” which represents the different investment
mechanisms that a client of the “business unit” of Asset Management has when
constituting an investment. These products can be of different “types” among
which are traditional and alternative collective investment funds, voluntary
pension funds, private equity funds and portfolios of investment solutions that
fit the needs of clients. In addition, the business unit has many initiatives
that allow it to potentiate its products to improve the value offer and achieve
the objectives that are taken as a unit. These initiatives can be of different
types and have a main motivator for their implementation which can of
technological renewal, operational improvement, process improvement, version
update, regulatory compliance, improved service availability, experience
improvement, operational efficiency, increase in income or a new product. As
well, an initiative seeks to generate an impact on the product by improving its
management standards, in such a way that they allow improving the value offer
to the client, through the investments made in the universe of assets that
exist in the market. Among the data identified are
data from the other areas of IT and user areas to be able to make a much
clearer analysis on the availability, impact, time required of resources,
capabilities in test environments, technical dependencies between requirements,
technical impacts, costs, deviations, start and end date of a requirement in
its lifting stages, development, testing
and production, implementation times, number of requirements, number of
projects, management indicators in tests and development, quality delivery
environments, number of hours invested per manager, per factory per business
unit, level of satisfaction of human resources and users. The interviewees were asked
about the missing data to support decision-making, from the answers given data
were obtained such as the availability of each area to designate projects and
requirements, capacities of the areas for test execution, hours available per
week and date availability, road map of the areas, a work plan involving all company projects,
end-user time consumption, relationship managers, application administrators,
developer capabilities, factory, administrator, factory planning, developer
performance, state of factory resources (disabilities, vacations, etc.),
requirements delays, change of priorities and response times for
incidents, errors, developments, and
estimates. Likewise, one of the things
that was sought to identify in the process of collecting information, were the
possible uses that can be given to the data that were defined by the
respondents, among which some uses related to decision-making were described
through descriptive and predictive analysis on capabilities, resources required
for a project, efficiency and improvement in processes, prioritization of requirements
and an identification of opportunities for improvement. Likewise, topics such
as the visualization of information through heat maps were found to identify
the capacity of the areas involved in the different stages of the process of
implementing requirements and control boards that allow to have a 360 vision of
the company in terms of the initiatives that are planned to be able to make a
timely follow-up on each of the company's initiatives. On the other hand, uses
related to operational improvements were proposed, among which it is sought to
identify and improve the performance of the team, based on the way in which
they collect the data and prepare the reports that are presented to the
business units, thus allowing to improve the opportunity to deliver the
information and have a more accurate measurement of the indicators. Regarding regulatory
compliance, it was identified that the necessary data is available to meet
audit requirements, but that this process requires an important effort because the
information is scattered, disordered, and cannot be traced; a significant
effort is required on the part of the managers to obtain this information. The maturity assessment in
data is a tool that allows to identify the level of evolution that is in each
of the areas of knowledge framed within the data management. Today, there are
different models focused on processes and good practices that must be
implemented on each front that comprises data management. In this research, we
worked based on the DCAM model defined by the Enterprise Data Management
Council, to define a survey to collect information to identify the level of
maturity that is in the relationship management with respect to the information
management process. For this, it was defined to evaluate five of the eight
categories defined by the model and one of culture in data that is not part of
the DCAM model, among the areas that were evaluated are the data strategy, data
management program, data governance, data design and modelling, data quality,
architecture, and culture in data. These areas of interest were selected as
they were the most relevant points to analyse in the company related to
information management and the objective of this research, likewise the
criteria and knowledge of the researcher to choose the areas of knowledge that
were evaluated was very relevant.
Figure 1 shows the results of the evaluation carried out on each defined area of interest. Although at the business level there is strategic planning, at the data level there are several opportunities for improvement related to the scope, objectives, and monitoring that is done on the data management program so that it generates the desired impact. Currently, in the company there are identified business cases that can be driven by data, but there is not clearly defined and documented data strategy in which those data that are necessary to meet the business objectives are identified. Likewise, there is no roadmap for its implementation. In the company there are policies for the use and access to data and in some cases those responsible for these are defined, however, the role of owner of the data has not been defined, and the interested parties do not have participation in the definition of policies and standards. Now there is no clear evidence of the evolution of the data governance program. As for this area of knowledge, the company has several opportunities for improvement related to the graphical representation of data models that allow to have knowledge about the different data domains and all their components, as well as the concepts and terms that are used throughout the business processes. In reference to the issue of quality in the data, it was possible to identify that there is no preventive approach, in addition, there are no defined processes focused on the cleaning and purification of the data that help improve the quality of these. Although the company is clear about the importance of data quality, there are no defined roles responsible for defining, preserving, and ensuring the quality of the data and there is no training on the subject. Regarding the level of culture in data of the organization, it was identified that it is a high pain point since it is necessary to have a knowledge of the business data in a transversal way that facilitates the understanding of what the business does and the importance of the quality of the data when making decisions based on these. The leaders of the business units seek to make better decisions based on the information generated throughout the processes and seek to manage data as an asset. However, in the company you do not have a 360 view of the data and its origin, some analyses are done periodically, but you do not have much confidence in the information that is generated. In addition, now there are no data-focused trainings that allow a better knowledge about data management.
Based on the results of the
survey conducted in Figure 2, it is shown that the level of maturity in data in relationship
management is 2.6, which indicates that it is at the level defined as
"Repeatable". Currently in the organization is acquiring a level of
awareness about the importance of quality in data and a correct management of
the information generated in the company, however, there are opportunities for
improvement in the areas of knowledge evaluated. 4. DISCUSSION Day by day, there are more
companies that want to manage their data as an asset, in different scenarios
the power of the data that is created and managed throughout the processes that
are carried out in the daily operation of an organization has been evidenced. The
growth in volumes of information for companies has an exponential trend that
brings with it already known challenges related to the ability in hardware to
store all the data that is generated daily. Society today is very changing and
demanding with the services or products offered by companies through the web
and this is something that has also generated a great impact on different
sectors of the economy worldwide and one of those is the financial sector. This
sector has been presenting an important growth at the level of services offered
virtually and with this has left some figures at the transactional level that
support that growth. From this, companies in the financial sector have been
making a large investment at the technological level, seeking to have greater
competitiveness and positioning in the market, improving their products or
services offered based in some cases on the knowledge they obtain from their
customers based on the data that they themselves have been generating. Defining a data strategy,
establishing a management plan for defined data, and ensuring its governance is
today a challenge that is increasingly necessary to achieve the survival of
organizations, considering the best standards and good practices that allow to
have accuracy, opportunity, and quality in the information that is generated
from these. In the company that was taken
as a source of study for the present research, the data that is currently
generated in the processes of management of the demand of the maintenance and
evolution of the software that supports the daily operation of the company were
identified. These data are used to present to the different business units the
traceability of the technological requirements that leverage their initiatives,
and likewise, they are used to supporting decision-making related to the
capabilities of business and IT resources that intervene in the life cycle of
the implementation of these initiatives. In the research process, the above
data were detailed and in the same way it was possible to know the importance
that these have in the decision-making of the business unit that are related to
the successful and timely implementation of the requirements defined in favour
of its objectives. At the same time, data was mapped that helps strengthen the
information that is needed as an input for that decision-making and that is not
being collected in the processes that management currently has defined. On the other hand, the points
at which the data is created, updated, and stored were detailed in the
processes of the relationship management. These processes allowed showing that
the data is stored in different sources and each manager has the responsibility
of guarding the files in which the data of each requirement are located. It was
also possible to evidence the little documentation that exists on the software
testing processes, now there is only one format in which the test that was done
and the result of this and in some cases, the evidence of the test performed
are documented. Likewise, it identified manual tasks necessary to consolidate
part of the data that are managed when performing descriptive analyses based on
said data and when visualizing the information generated were identified. In
turn, the low use of the information generated information in the process of
prioritizing requirements was evidenced. Indeed, there are opportunities for
improvement in the revised processes, at this time the way the data is being
managed prevents having a better accuracy, quality, and timeliness in the
information that is generated from these. For any business it is
necessary to have the availability, opportunity, and accessibility to the data
generated in its operation. Therefore, it is necessary to design the data
strategy and implement it by defining efficient processes and focused on always
ensuring that each of these needs is met. Currently, there are companies that
continue to make their decisions based on the judgment of experts and do not
have the capabilities to do otherwise. The design of efficient data strategies
has been evolving for more than a decade and among the number of positive
things that have been achieved from their implementation and monitoring, is the
fact of eliminating the dependence on the knowledge of some people in the
company and ensuring the use of these from the design of the data strategy and
the management of these. It should be clarified that to achieve a correct data
management, it must be shielded with the implementation of standards and good
practices that allow to meet those needs of internal or external information
that the business has. If companies understand the power, they have in the data
that is generated throughout their processes, they will begin to preserve,
protect, and safeguard it as one of their assets. The information of the
companies is built in the business units from the data that is managed in one
or many of their processes and in some cases based on external data that are
generated by other organizations or that are part of the universe of open data.
That is why having processes focused on the management of the data identified
in the data strategy as a business focus, becomes an indispensable skill for
the management of the business, customers and the market making intelligent
decisions, being more competitive and ensuring permanence in the market.
Likewise, other companies are looking to improve their data management
processes from different frameworks that help them be more effective and
efficient throughout the data lifecycle. With this, it seeks to guarantee the
generation of quality, timely and accurate information that supports
decision-making, improvement, or the offer of new products or services. On the other hand, in the
research process it was also possible to identify that at the level of the
relationship management descriptive analyses of the data collected in their
processes are made, with which they seek to have a reference to make the
planning of the capacities of resource to meet the backlog of requirements. In
the same way, it seeks to identify from these analyses’ bottlenecks throughout
the process in order to make decisions about it and be able to solve the
capacity problem that is detected. Descriptive analyses allow you to summarize
historical data and evidence patterns that normally have some meaning through
measures of central tendency, frequency analysis, and measures of dispersion
and position. Any type of analysis you want to do requires information, and the
quality of the information generated in these processes depends on the quality,
accuracy, and timeliness of the data they use for analysis. Therefore, it is
essential to define processes focused on guaranteeing these characteristics
necessary for the analysis to be cleaner and the output of information to
generate a high level of trust to decision makers. Another benefit of a correct
data strategy and data management is the improvement that can be had in the
processes of building reports and visualizing information. Just as it has evolved
in different frameworks focused on data management, there has also been an
important evolution in the tools that help improve and automate these
information presentation processes. With the present research, it was possible
to show that the processes of information visualization in the relationship
management take a long time due to its manually and the lack of opportunity to
have the data. Despite having processes, formats, files, and applications
defined to collect information, in some cases the data is not available at the
time it is needed, and these must be searched so that the report or
presentation is not incomplete. This shows a low quality in the execution of
the processes at the points where some managed data necessary for the reports
must be registered or modified. Having specialized tools in
data visualization is an opportunity for improvement that is evident in the
processes that are currently in the relationship management, in which the
manually generates inopportune and low level of response to the needs of the
business. Similarly, by automating tasks that are currently executed manually,
you are obtaining a reduction in response times and an optimization of
resources. These tools also allow you to consolidate information from different
sources and have defined dashboards, reports, or presentations that are
required to be viewed. Likewise, these tools allow decision makers to have a
view from different perspectives on the panorama of the company or the business
unit, and now it is something that takes a lot of time to build the
relationship team. Decision-making for any company is a fundamental and
transcendental task because they define the way forward and the future of the
business. Managing data under standards and good practices requires the
implementation of business processes that are focused on data, it is also vital
to highlight the importance of permeating the value of data in the company's
culture. Because of this, many organizations that are making large investments
in the definition of processes that are focused on guaranteeing the accuracy of
the information that is generated in the company through proper data
management. 5. CONCLUSIONS In the process of designing a
data strategy, it is essential to identify the objectives that the business has
so that these are the basis of the plan that you want to define. At the company
level, initiatives are defined from the business units that leverage the
achievement of the aspirations that are had at the organizational level. A key point
in the strategy design process is to identify the data that is required to
support the implementation of these initiatives so that they are the main input
of this. To collect this data, it is very important to identify the key people
in the business, such as a manager or area director, who has a transversal
vision of the company and can provide the data that allows mapping those
business objectives. On the other hand, data
models are a very useful tool when it comes to mapping and understanding
business concepts. These allow to have a better understanding of the data
through graphic representations in which the relationship that exists between
the different concepts can be evidenced. Another support tool that was used to
identify the data required in the present research is the data inventory. With
this instrument, the concepts that were graphed in the conceptual diagram were
detailed, as well as the attributes of each of these. It is key to this
process, to have clarity about what is the use that will be given to the
identified data, so as not to collect data that will not have any use or that
does not make sense for the objective of the strategy. In the same way, maturity
models played a substantial role in the design of the strategy, since through
these it was possible to identify the level in the company's capabilities to
manage information under standards and good practices. Based on the critical
points and opportunities for improvement that were identified from the maturity
assessment, the areas of interest that are to be improved with the designed
strategy were defined. At the same time, an analysis of the weaknesses,
opportunities, strengths, and threats that exist at the data level in the
company was carried out, which served as an input to define the type of
strategy to be designed. With the above, it was possible to identify that in
the relationship management, there is not all the information identified in the
investigative process. As well, opportunities for improvement in the
information management process that is done in management were identified,
which will be mitigated through the guidelines defined in the roadmap for the
implementation of an information management program that is articulated by a
data governance program. As a result of this research work, future research focused on the implementation of the projects derived from the designed data strategy can be defined. Likewise, we can work on predictive analysis models that allow generating forecasts related to the capabilities necessary for the execution and implementation of business initiatives. REFERENCES Blasco Mira, E. J., & Perez Turpín, J. A. (2007). Metodologías De Investigación En Educación Física Y Deportes : Ampliando Horizontes. Editorial Club Universitario. http://rua.ua.es/dspace/handle/10045/12270?locale=en Dama I. (2020). Guía Del Conocimiento Para La Gestión De Datos (Segunda Ed). Technics Publications. Https://Books.Google.Com.Co/Books?Id=5fnvdwaaqbaj Fernando, D. L. D. G. (2002). Gestion De La Informacion : Guia Practica Con Excel. Bogotá : Alfaomega. https://archive.org/details/gestiondelainfor0000davi Gallant And Fleet, K. (2018). The Data Strategy Playbook. Marr, B. (2017). Data Strategy : How To Profit From A World of Big Data, Analytics And The Internet of Things. Kogan Page. https://www.abebooks.co.uk/9780749479855/Data-Strategy-Profit-World-Big-074947985X/plp Marr, B. (2020). How Do You Develop A Data Strategy ? Here’re 6 Simple Steps That Will Help. https://bernardmarr.com/how-do-you-develop-a-data-strategy-herere-6-simple-steps-that-will-help/ Mei, Z., Wu, X., Li, X., & Zhu, C. (2018). Research on Battery Data Compression Method For Electric Vehicles. International Journal of Electric And Hybrid Vehicles, 10(1), 57–69. Https://Doi.Org/10.1504/Ijehv.2018.093066 Papiernik, D. K., Nanda, D., Cassada, R. O., & Morris, W. H. (2000). Data Warehouse Strategy To Enable Performance Analysis. Transportation Research Record, 1719(1), 175–183. Https://Doi.Org/10.3141/1719-23 Rae (2011). Diccionario De La Lengua Española. Madrid. https://dle.rae.es/ Sampieri, R. H. (2018). Metodología De La Investigación : Las Rutas Cuantitativa, Cualitativa Y Mixta. Mcgraw-Hill Interamericana. https://books.google.co.uk/books/about/METODOLOG%C3%8DA_DE_LA_INVESTIGACI%C3%93N.html?id=5A2QDwAAQBAJ&redir_esc=y Tena, M. (2018). La Transformación “Data Driven” De Bbva Ya Es Una Realidad. Https://Www.Bbva.Com/Es/Marco-Bressan-La-Transformacion-Data-Driven-Bbva-Ya-Una-Realidad/ Varshney, S. & Allen, K. (2020). Implementing Data Governance How To Develop A Strategy, Determine A Value Proposition, And Build A Business Case. https://media.trustradius.com/product-downloadables/CG/A1/XT5J1RG7AL0I.pdf
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