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Success factors for developing a data-driven organisation
Alireza Abedi
Email:
[email protected]
Abstract
In a swiftly transform global business environment, make accurate and appropriate decisions by taking advantage of business intelligence analytics is the demand on organizations. The ability to recognize challenges, spot opportunities, and adjust with agility is not a competitive edge, in contrast, is a necessary for survival. A data-driven organization has been characterized by collecting more high-quality data, have more skilled data analyst, and transparent about data. This study conducted an empirical research to analyze of influential factors for developing a data-driven organization and concentrate on exploring success and fundamental factors. Moreover, this study took advantage of collecting several papers, literature, books and case studies from different sources. This paper contains, an overview of "Characteristics of a data-driven Organization" and " Pre-requisition to building a data-driven organization" and presented six finding to support and define further about purposes of this study.
Keywords: Data-driven organization, big data, data culture, data quality, data governance, decision-making
Introduction
Background
The World is changing, organisations are shifting to swift and digitizing faster, moving online and mobile to business. Concurrently, organisations are witnessing a data revolution; they are gathering incredibly detailed data and generate knowledge from their users, suppliers, partners, and competitors. (Brynjolfsson, E., Hitt, L. M., & Kim, H. H., 2011) In particular, since 1993, most large organization have invested in extensive enterprise resource planning, Supply Chain Management, Customer Relationship Management and similar enterprise information technology (McAfee, 2002).
As apparent from these trends, a surge of data, capture and making sense of this information, have to be taken to manage. Information management and harnessing the extensive data is a crucial component to obtain the digital maturity purposes of an organization. (Brynjolfsson, E., Hitt, L. M., & Kim, H. H., 2011). On the other hand, many organizations start to generate many reports from their business intelligence applications or implementing many dashboards to shown; they are part of data-driven organization trend. While those activities are part of what organizations must perform in this era. (Anderson, 2015)
According to Brynjolfsson, E., Hitt, L. M., & Kim, H. H., (2011), by controlling all factors, real data-driven organizations have a 5%–6% higher output and productivity than other organization. They also had greater assets utilization and market values. A data-driven organization has been characterized by collecting more high-quality data, have more skilled data analyst, and transparent about data. (Brynjolfsson, E., Hitt, L. M., & Kim, H. H., 2011) Furthermore, these organizations can always have a reliable decision-making process (Brynjolfsson, E., Hitt, L. M., & Kim, H. H., 2011). All efforts will lead to achiving reliable data for an organization which will help develop an internal analytics program, determining what data to collect and wherehouse, how to make sense of these data and act on it. (Anderson, 2015).
The problem addressed in this study concerned, a plenty of research exists for representing how an organization can employ reliable data and taking advantage of business intelligence to victory in the competitive market , whereas there has been virtually no research concerning the factors influencing the success or failure of initiatives a data-driven organization.
This study is a step toward finding out the major factors for developing a data-driven organization, The findings of this study will be helpful for organizations which are trying to be collect and organize relevant data and take advantage of them in an agile and reliable decision-making process. Moreover, the objective of this study is to obtain a general overview, secondly what factors from this overview are crucial and finally, the relationship between reliable decision-making process and data-driven organization.
Research question
The research questions of this study can be formulated as:
Which factors are significant for the initiation and developing a data-driven organization?
What is the relationship between reliable decision-making process and data-driven organization?
Research method
Based on the aims and objectives of this study which was looking for various experiences, this study employed qualitative research since; a qualitative study is concerned with words instead of numbers, which is the considerably difference from quantitative research (Emma Bell, Alan Bryman, 2007). Moreover, this approach could provide detailed textual descriptions from collecting several papers, literature, books and different case studies. So, this process could help to approach accurate information that were required to mention in the study (Johannesson, P., & Perjons, E., 2012).
There are professional ethics that the researcher needs to grant them in the research. Generally, for performing a high-quality research, it must follow common standards that are "universality, altruism, and organized skepticism" (CODEX, Centre for Research Ethics & Bioethics, 2016). The researcher should be responsible for his/her research to be ethically legitimate. In addition, it should be considered that the personal opinions do not lead to a biased conclusion.
Theoretical frame
The concept of "what success factors" might change and it can encompass several Interpretations. "Success" can be slightly ambiguous and multidimensional in a data-driven organization and consequently prospect and evaluations of the performance of outcome might differ among stakeholders. The general aspect and the combination of the main dimensions of success are defined by the Information Systems Success Model which was presented by DeLone & McLean. (DeLone, W. H., & McLean, E. R., 2002). This model showed that a system could evaluate in respect of information, system, and service quality. Subsequently, these components affect the use or purpose of using a system as well as user satisfaction.
Based on this model, this paper has investigated relevant information, system, and service quality in a data-driven organization which illustrated the result of using the different approaches would achieve particular advantages in various organizations. Following this view, in this paper presented key concepts by two dimensions which indicated the success factors for developing a data-driven organization.
1) Characteristics of a data-driven Organization
2) Pre-requisition to building a data-driven organization
1) Characteristics of a data-driven Organization
The speed of change and trends in IT needs that organizations react immediately to the changing requirements of customers, suppliers, partners, and competitors. As apparent in the following diagram, organizations are facing a several of priorities to apply data as a strategic asset in their marketplace.
Figure 1: Challenges facing organizations
Data must be leveraged to compete in the marketplaceCommoditized product offeringsSlow industry growthIncreased regulationPricing pressure from low cost competitorsShifting channels for interactionsData must be leveraged to compete in the marketplaceCommoditized product offeringsSlow industry growthIncreased regulationPricing pressure from low cost competitorsShifting channels for interactions
Data must be leveraged to compete in the marketplace
Commoditized product offerings
Slow industry growth
Increased regulation
Pricing pressure from low cost competitors
Shifting channels for interactions
Data must be leveraged to compete in the marketplace
Commoditized product offerings
Slow industry growth
Increased regulation
Pricing pressure from low cost competitors
Shifting channels for interactions
Note: Adapted from "The external control of organizations: A resource dependence perspective" book (Pfeffer, Jeffrey, Gerald R. Salancik., 2003)
In addition, organizations are facing a mixture of challenges internally to gain data maturity. The main challenges are the following:
Lack of ownership by business
One of the crucial aspects of many organizations is, how decisions are made and who gets to make them. In most case, data governance is IT-led in contrast with business-led. Frequently business-led don't have the technical skills, or they have a poor appetite to study and discover data problem. As a result, they are distancing themselves from these IT transformations. This results in IT-led governance programs and decisions and also insufficient buy-in from key stakeholders. (McAfee, A., Brynjolfsson, E., Davenport, T. H., Patil, D. J., & Barton, D., 2012)
Lack of trust in existing data
Usually, business managers do not trust data to make knowledgeable decisions. They believe, the quality of data is so poor, and for remediating the data and generate a consolidated report should take enormous effort and consume time. So, adopting evidence-based decision making is a complex cultural shift. (Ross, Jeanne W., Cynthia M. Beath, and Anne Quaadgras, 2013)
Multiple versions of truth
Some organizations do not have central enterprise-level data definitions and references that are consistently utilized and dispatched across the organizations. Besides, as each business unit creates siloed data to support requirements, there is no key committee to control the data and information. As a result, enormous tension on an organization to integrate into the single source of truth of the enterprise. (Ross, Jeanne W., Cynthia M. Beath, and Anne Quaadgras, 2013)
In the circumstances and regarding mentioned challenges are facing organizations in the journey towards data-driven maturity, it is significant to manage data as a critical strategic asset. In addition, managers should concentrate on targeted efforts to source data, build models, and transform organizational culture. Therefore, in this paper, the foundation required for the data-driven organization to have an information management backbone is discussed. The data infrastructure of any IT organization is arranged by enabling the seven foundations of data management as described in the visual below. In this paper is focused on data governance and data quality as empowering those functions that will organize the framework for discussing other characters of data infrastructure in the data-driven organization.
Figure 2: Data management capabilities
Enterprise Data ManagementEnterprise Data Management
Enterprise Data Management
Enterprise Data Management
Note: Adapted from "Data Governance: How to Design, Deploy, and Sustain an Effective Data Governance Program" book - Chapter 2 (Ladley, 2012)
Data governance
Data governance is a discipline formed from enterprise information management. There are different frameworks which trigger the requirement for organizations to migrate from traditional data management to formal data governance (Ladley, 2012). Due to data integration in an organization, these frameworks reduce organizations expanding size and complications for managing data silo. An efficient data governance framework will formulate a business-led and IT-enabled road map for an organisation (Plotkin, 2013). According to Ladley (2012), Data governance framework contains four guiding principles: 1) Illustrate data as a strategic asset; 2) Link to strategic approaches; 3) Enable a complete change management plan; 4) Be driven by business value.
In addition, Ladley (2012) illustrated advantages of data governance which was in 4 categories:
Improve efficiency
Establish consistent metrics for ensuring that data stewardship, data standards, and policies are arranged.
Improved collaboration among business units to eliminate inefficient information management.
Facilitate insights into data, particular for business units, products, and services.
Improve compliance
standardize data ownership and stewardship across functions
Decrease the risk of noncompliance with legal requirements and regulatory.
Establish central data management of subject areas, organize information standards and processes and setup cross business /cross-function
Improve control
Consolidation and accurate report improve by providing single-source high-quality data as well as decrease risks and increase risk analytics.
Execute controls surrounding how data is created, managed, and apply across functions.
Decrease cost
Cost reduction through standardized processes.
Federation of various standalone applications worked globally for same functions.
Data quality
Data quality issues have cost more than $600 billion per year for U.S. businesses. (Campanelli, 2002) Most executives are unaware of the data quality challenges. It has several impacts on their organization's ability to respond swiftly to changes. For instance, adverse effect on regulators and impact on organisation's credibility among the market. (Scannapieco, M., Missier, P., & Batini, C., 2005)
They (Scannapieco, M., Missier, P., & Batini, C., 2005) also stated, a performance of business functions and technical requirements can guaranty by efficient data management which will provide reliable data. Regarding Plotkin (2013), in general, data quality fully is characterized regarding accuracy, integrity, relevance, consistency, serviceability, accessibility. Data quality issues of an organization will illustrate by three angels: People, Process and Technology. (Plotkin, 2013)
Concerning people, for delegating accountability, roles, and responsibilities, the data quality framework determine to allocate the proper data owner, data architect and data steward (Plotkin, 2013). Moreover, the organization's approaches determine by process perspective which attempts to data profiling, data remediation and data cleansing (Plotkin, 2013). Lastly, the technology factor proposes the data quality tool beside the relevant scorecards to measure data quality compliance is ongoing (Plotkin, 2013).
The final target and advantage of data quality contained by providing accurate decision-making. As a result, an enterprise enables to proceed a single source of truth, actual conclusions and correct decisions (Chengalur-Smith, I. N., Ballou, D. P., & Pazer, H. L., 1999). Regulatory compliance increased customer satisfaction, reduce operational cost, greater confidence in analytics is also part of advantages of data quality for an organization (Chengalur-Smith, I. N., Ballou, D. P., & Pazer, H. L., 1999).
This paper has clearly shown that, it is so easy to equip organizations with latest data management and analytics tools and provide the superior dashboard for executives and stakeholders to build and make decisions. However, these elements will not bear fruit if there is the lack of trust and multiple versions of truth in existing data. A data-driven organization must guarantee that data is accurate and clean. By determining value and accountability for the data, it would be measurable, and create synergy to discover data gaps and keep information aligned with business strategies.
From the outcome of data-driven organization characteristics and relevant definitions, it is possible to conclude that, the roadmap toward a data-driven organization can be obtained once, users and information consumers trust the data. Moreover, in an organization must be accurately determined ownership, responsibility, and accountability to remediate bad data.
2) Pre-requisition to building a data-driven organization
The main corporate agenda in most organizations are "big data" and "analytics". They promise to shift the organization road to deliver value added and boost their performance.
According to previous part, entirely extracting data and analytics need three fundamental auxiliary capabilities: Firstly, the organization must be able to identify, unify and control various data source. Secondly, they require the infrastructure to shape analytics models and methodologies for modifying and optimizing output. Finally, and most critical, stakeholder must push organization to transfer their culture so that the organizations are moving to better decision making. Two fundamental features can characterize these organizations with these capabilities: 1) a clear strategy for how to take advantage of data and analytics and 2) the distinct deployment procedure for allocating proper technology architecture and capabilities.
No matter how wisely shapes these organizations with skill, a data strategy is defective without data-oriented culture. Data culture and data oriented are interlock and connected, and together, they will build a data-driven culture strategy and organization. A data-driven culture is an environment that employs a reliable approach to strategical decision making via empirical and emphatic data proof.
So, for building a data-driven organization following pre-requisition can determine:
Data oriented outlooks will support KPI and infrastructure.
The data-driven organization determines processes that reinforce KPI (key performance indicators) primary to their businesses enterprise; they dispatch these measurements to their workforce for delegating accountability, roles, and responsibilities. An Econsultancy's survey reported (Econsultancy, 2013) that about 360 IT and other companies' start-ups which are established in western Europe and Canada stated, more than seventy percent of these immature companies assigned at least one person to capture and analysis information.
Data must centralize, arranged and managed.
To ensure data is novel and relevant, data-driven organization must capture and organize data. Federating data is permitting for continual updates, keeping data up-to-date among the enterprise. Organization must be attentive about how much data gather. Regarding Econsultancy (2013) report, overloading data is a significant threat in organizations in this era.
Policies can lead an organization to govern data access.
Formal policies for controlling user access level is one of another key of data-driven organization for managing proper data. These organizations mitigate risk by deploying levels of access and specify how and by whom data should be modified. Establish consistent metrics for ensuring that data stewardship, data standards, and policies are arranged is a success key for data-driven organization (Ladley, 2012).
Data access should be layered.
Data-driven organizations have a layered and sophisticated approaches to organizing KPI. Managing key performance indicators and communicated transparently to the human resources should well-defined (Anderson, 2015). Consequently, each metric is explicitly associated with organizational function and desired result. Also, it will approach central enterprise-level data definitions and references that are consistently utilized and dispatched across the organizations. (Ladley, 2012)
Analytics assets must integrate into enterprise resources.
To support and boost human resources efficiency at data-driven organizations, cloud and mobile devices must serve analytic tools. These devices tend to be among the most innovative tools available (Anderson, 2015). Analytic tools are usually embedded into existing and current tools to make them more efficient to be used and intuitive. Also, a federation of various standalone applications worked globally for same functions is a primary reason for integrating these tools.
Become a wiser company by making better decisions
A quick decision making becomes more vital in each organization and analytics is boosting and re-shaping smart decisions in this era (Brynjolfsson, E., Hitt, L. M., & Kim, H. H., 2011). The purpose of becoming a data-driven company is to lead a wiser company by making timely and reliable decisions. The decision-making discipline is not merely a matter without fitting analytics into a corporate culture (Brynjolfsson, E., Hitt, L. M., & Kim, H. H., 2011). Analytics can lead to cultural change and enhanced performance for human resources as well as organizations to step up rational decision-making.
As a result, this approach may fail and becomes worthless if solely data is exchanged and communicated across an organization. Data-driven organizations can create data value by leveraging analytic to provide relevant tactical data when and where it is required. Furthermore, extensive analytics usage also enhanced productivity, decrease risks, and conduct an organization to make wise and agile decisions.
Discussion
The aim of this part of the study is to respond the central research questions which are " Which factors are significant for the initiation and developing a data-driven organization?" and "What is the relationship between reliable decision-making process and data-driven organization?"
The primary purpose of this paper is to exploring "Success factors for developing a data-driven organization." Organizing big data and lack of trust were important subjects in this paper. This study expressed that the big unorganized data and poor data output are primary limitations for avoiding to deliver significant value to users. The following findings were identified in the study. These findings have the effect on exploring success factors for developing a data-driven organization and relationship between reliable decision-making process and data-driven organization.
Big victory with big data
Big data is apparently produced exact value to organization users who have fulfilled a project, SAS and the Harvard Business Review (Harvard Business Review Analytic Services, 2012) reported in their survey that the vast majority of global executives (92 percent) of all participants stated, the business outcomes are satisfaction, and 94 percent reported their big data implementation covered their requirements.
Big data users who are challenging and completing projects recognize efficient outcome and great value. In addition, organizations understand big data should be crucial for a broad spectrum of strategic corporate purposes; it can start new revenue generation and niche market to improve the enterprise-wide performance and boost customer experience. Whereas some organizations are still standing on the sidelines.
Big award gain from thinking big
According to, SAS and the Harvard Business Review (Harvard Business Review Analytic Services, 2012), It evident that enterprises are entirely qualified to take advantage of big data by considering big data as very influential and a fundamental asset to their digital strategy.
Enterprises' human resources have important roles to start a small-scale and realistic project with their pre-defined prospects, frequently help, involve direct CIO and strong ERP application support. They are not trying to do everything and perform all steps at once. Instead, they concentrate on resources to prove data value in just one field and allowing the outcome cascade across the enterprise. Their journey was started local and end global, as users concentrated on useful applications such as CRM, internal support, and focus on seeking outcomes.
In summary, the enterprises rolled out to be significant beneficiaries of first big data implementations, because: 1) A comprehensive knowledge of big data is elements and value reference; 2) A concentrate on useful ERP systems and business result; 3) Greater commitment in talent.
Big data require in-depth knowledge and practice
Many organizations are only starting to investigate initial big data project phases; they discover implementing big data is facing significant challenges, and there is much matter to learn and practice about data as a resource and analytics. For instance:
1) Many organizations have the various meaning of big data, data sources and uses.
2) Valuable data sources are cross out or overlooked.
3) they are facing different barriers, e.g. the lack of talent, security concerns, and budget concerns.
In this case, SAS and the Harvard Business Review (Harvard Business Review Analytic Services, 2012) reported: More than 36 percent reported significant data need an enormous investment. 37 percent believe organizations can obtain exceedingly massive cost-savings by utilizing big data and 26 percent think organizations are demanded to using big data across the enterprise at once.
Big data technologies are essential.
The successful story of big data's expertises is delivering big data technologies and big data together to drive the business result. An output concentrates on demands a capability to mobilize data from across the enterprise, to examine that data strongly to approach its value, and define what data is relevant and what data is not; and need the discipline to govern it therefore that it manages several parts in the enterprise.
Learn and remain open to possibilities.
The big data allocate organizations in a life cycle which are potentially lead them to innovation and inadvertent invention. Capturing data resource and usage patterns and perceive business use case provide basic patterns into the proper approaches, solutions, and technologies that will be used to deliver an outcome. Becoming a learning enterprise help organizations to implement multiple solutions and technologies as well as stand as open to the possibilities approaches. Once, organizations can learn what works best; they can make perfect their resources for evaluating everything and learn swiftly.
Equip organization by new IT structures and a new culture.
Legacy IT structures may prevent to capture new types of data sourcing, warehouse, and analysis. It shows, existing IT architecture may limit the alliance of siloed information and manage unstructured data which usually remain behind the legacy IT structures. Many legacy systems were developed to deliver data in batches. Consequently, they cannot provide constant flows of data for real-time decisions.
Given this finding, data-informed analytics shape and support important strategic decisions as well as an expanding number of appropriate operational processes. Business intelligence becomes a means which employed by the organization's human resources. They are more eager to adopt the changes and technical assets that data-driven approach enables it to obtain the competitive advantage. Placing Business intelligence in the hands of each decision maker will adjust organization's aims, illustrate there are no "different stories of the truth and guarantee everyone is on the same page with the refresh data in real-time."
In like manner, most organizations frequently require new IT architectures roadmap besides appropriate technologies to work with large volumes of data swiftly. Adjusting culture and mind-sets to think about data as an asset need a multifaceted strategy which is a combination of training, role modelling by executives, well-defined purposes, and applying a great metrics to reinforce the KPI.
After all, everyone and every organization can load of data. However, the data-driven organization is those who have the expertise, motivation and capacity to take advantage of it altogether and to drive bottom-line outcome across the entire enterprise. These organizations reinforce their data value by bringing up "where the origin of it," "who has/should access," and "how has been used." At data-driven organizations, data is not only an asset but rather a way of organization's life.
As a result, this paper illustrates how to think about data as an asset and how to be a learning enterprise. Above findings were the principal purpose of the study to draw attention to explicate success factors for developing a data-driven organization and how to use the data to make better and agile decisions.
Conclusion
In conclusion, it is evident that this study has shown, most challenges the organization is facing is that data will silo in several places, e.g., department or systems and it makes restriction cross-functional access to source of data and poor data analysis output. Making matters worse, organization angels (People, Process and Technology), could not allocate appropriately, so organization is challenging with lack of trust and multiple versions of the truth. Consequently, top managers cannot concentrate on the source of data, modeling their requirements and reshape organization culture.
In the other word, often young organizations were born from data, so they have an inherent competitive advantage over old generation organizations in their market. Their businesses are associated with facts and share data is inseparable of their DNA. The traditional organization was born off-line, they characterize data as tools to run their businesses instead of planning and delivering their strategy. Regarding an investigation by the Economist Intelligence Unit (Economist Intelligence Unit, 2013), organizations that evaluated and rated their performances by using of data are times more success and ahead than the organizations measure themselves by financial efficiency as more advanced.
As the organization grows in analytics and data maturity, it starts to extend its purpose to ever-broader data sets. As data volume, variety, and velocity rise, accordingly, Data-driven organizations have ability to make use of it. Analytics resources and skills are not restricted to some departments, and everybody from supply chain to sales to marketing is supporting and using this benefits. Decision making is expanded all over the company since Business Intelligence is supplying users with the proper data they demand to make better and agile decisions and confidently take action.
To overcome mentioned problems, this study conducted an empirical research to analyze of influential factors for developing a data-driven organization and concentrate on exploring success and fundamental factors. Moreover, this study took advantage of collecting several papers, literature, books and case studies from different sources.
The research contributions of this study are divided into 2 part:
Firstly, an overview of "Characteristics of a data-driven Organization" and " Pre-requisition to building a data-driven organization" and the affordances that these statements presented six finding to support and define further about success factors for developing a data-driven organization and relationship between reliable decision-making process and data-driven organization. Secondly, we have represented some research issues for the development and evaluation of this paper.
Regarding the explorative nature of this study, the findings could not generalize to a large organization, due to a shortage of time, broad exist the solutions for governance, measure data quality, and business intelligence application, and a small number of study which were randomly selected and the larger sampling size will give more accurate results. It is worth noting that the findings may change dynamically depending on culture and the maturity of an organization.
Future research
More research on influenced factors on developing a data-driven organization is still required before proposing a final dynamic model under the specific conditions. This analysis does not enable to determine the overall factors for the initiation and developing a data-driven organization and new performance measures. Therefore, a new study to capture an in-depth understanding of a relationship between reliable decision-making process and data-driven organization is needed.
Furthermore, this study could not investigate how to create a data-driven culture into an organization at the same time with developing a data-driven organization. Researchers who intend to use this study, need to investigate and draw attention to the effect of talent shortage and cross-cultural issues in a data-driven organization as well.
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Data
governance
Data conversion, retention,
and archiving
