Data is very important for any business. Storage, management and use has helped them in their work. Making decisions based on data through the application of Data Warehouses or BI has been implemented for more than a decade and is increasing the interest more and more for managers. Technological developments and the addition of new trends in BI make it a technology that will be used for a long time. In this scientific paper we will analyze self-service BI qualified as a wish fulfilled for businesses. This study aims to explore study the benefits and challenges and how have they evaluated it different scientific researchers.
Intensive technology developments in the last
two decade have greatly increased the amount of
data in businesses. With the increase of data, the
demand of businesses to manage them increases.
Delivering the right information, to the right
people, at the right time, is one of the key
requirements for today's businesses [
Gartner (2013) defines BI as "an umbrella activity that combines architectures, infrastructures, tools, databases, analytical tools, programs and
methodologies to improve and optimize decisions and performance".
It is a content-independent expression, so it means different data to different people. The main objective of BI is to enable real-time access to data, to allow data manipulation and to provide managers and business analysts with the opportunity to transmit adaptive analysis. Albania ORCID:
2023 Copyright for this paper by its authors. Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0).
Through the analysis of historical, current, situational employees decisions.
and make The performance
data, more informed BI process is and based strategy better on transforming data into information, then into decisions, and finally into actions. The application of BI can have a significant impact on a business by improving decision-making, faster and easier access to information, increasing operational efficiency, providing better customer insights, enhancing competitiveness, improving financial performance and savings in IT infrastructure cost
Data-driven decision making is increasing the use and reach of BI across all industries [17]. The importance of data and its use in decision making have become very important in the digital age.
In the Figure 1 according to Precedence Research The global business intelligence market size was exhibited at USD 27.24 billion in 2022 and is projected to hit around USD 54.9 billion by 2032[19]. The usage of BI has increased different sectors such as large organizations or SME, manufacturing companies, healthcare, banking, telecommunications, financial services, insurance, and various public sectors are increasing the usage of BI on the incoming years.
TechTarget presents the most important stages of BI developments as in the table 1. make better decisions with self-service
analytics capabilities Salesforce acquires Tableau, Google purchases Looker and Sisense buys Periscope; AI machine learning and natural language features are all
becoming standard Vendors expand low/code/no-code, mobile and multi-cloud capabilities Collaborative BI combines different tools including online tools and social
media.
NLP (Natural Language Processing) Self-service and analyzes with written or spoken words. Tableau’s Ask Data;
Power BI’s Q&A.
Microsoft presented Power BI in Microsoft Teams for improvement
experience
Even with the challenges brought about by the
pandemic, the BI landscape is rapidly shifting
[
Usually, large businesses have their own databases and data warehouses developed, they use them as the main sources for BI. BI is based on these data warehouses to provide accurate, timely and reliable intelligence. The term BIDW became indivisible to show the efficiency of using BI when we have a DW. But should small and medium businesses invest in Data warehouse and then in BI or is it better to focus on their data and their study. BI is considered more user-friendly to business users than the data warehouse.
DWBI is a more traditional approach to BI that emphasizes centralization and standardization of data, while self-service BI is a more agile and user-driven approach that empowers individual users to analyze data in an easier and more intuitive way.
The use of Datawarehouse for BI requires the
implementation of ETL (extract, transform, load)
process. In ETL data is extracted from multiple
sources, transformed into standard formats for
analysis, and loaded into the data warehouse. The
IT department should assist the analysts with ETL
processes if this method is used. Getting data in
is the most challenging aspect of BI, requiring
about 80 % of the time and effort and generating
more than 50 % of the unexpected project costs
[
The successful implementation of information system (e.g., Enterprise Resource Planning (ERP), Customer Relationship Management (CRM), Warehouse Management System (WMS), Data Management Systems (DMS), etc.), typically requires a strategic approach that will correctly anticipate SME needs and requirements for analytical tools such as BI [14].
By using Self-service BI tools, analysts can directly access the data, prepare the data from multiple sources, get insights, model the data and choose the most suitable GUI for the data to make faster business decisions. The tools are typically intuitive and interactive and let users explore data beyond what the IT department has curated.
By implementing a smart BI architecture system, IT staff will be largely released from the tedious work of producing reports giving them enough time to focus on other core issues such as cyber security and operation of right of the business system [15].
SAP, Power BI, Tableau, and Qlik are some of the examples of the most used BI tools. On the other hand, the most popular providers of data warehouses include Azure Synapse, Google Big Query, and Amazon Redshift. 2.1.
Self-service Business Intelligence (BI) refers
to a data analysis approach that enables business
users to access and analyze data without the need
for IT or data analytics experts. It provides
business users with tools and technologies that
allow them to create reports, dashboards, and
visualizations from various data sources quickly
and easily [
Self-service business intelligence (BI) has
been on organizations’ wish lists for a long time,
and data from the BARC BI Trend Monitor
2017 confirms that it is still a high priority[
With self-service BI, business users can perform ad-hoc analysis, explore data, and gain insights into business performance in real-time, without relying on IT departments. This approach democratizes data and empowers business users to make data-driven decisions quickly and effectively.
Power users are even compiling their
dashboards using layout components from
different sources, adjusting and combining them
for their own use and more often for the needs of
their teams [
Self-service BI tools typically include features such as data visualization, drag-and-drop interface, and natural language querying, which allow users to interact with data intuitively and with little training. These tools may also include features for data cleansing, data blending, and predictive analytics to help users analyze data more effectively.
Self-service BI allows organizations to become more agile, responsive, and data-driven, enabling them to make better decisions faster, gain a competitive advantage, and drive business growth.
Table 2 shows some features of traditional BI compared to self-service BI.
According to BARC the most common BI problems from the perspective of user and vendors are shown in the figure.
It is important that the vendors work closely
with customers during implementation and data
integration where data quality may be a prevalent
issue [
Practitioners need to realize that implementing
Self-Services BI(SSBI) is not an easy matter and
that SSBI comes with three different categories
challenges as [
1. Becoming a self-reliant user; Business users are not self-reliant when they need the data although they can create their own reports. In this case they need IT help.
2. Creating SSBI reports; Creating multiple reports and then reusing them can compromise data security if they are not created by users with good analytical skills.
3. SSBI education; Business users must be trained and educated in data analysis if they are to be allowed to use SSBI and perform complex tasks.
To avoid or overcome such challenges, an
organization must start with a well-planned BI
strategy, use pilot groups, including a solid BI
architecture that establishes technology and
governance standards, identify user groups and
their data needs. Those foundational elements can
help ensure that the organization has the right data
sets and the infrastructure to support
enterprisewide use of self-service BI tools [
Lennerholt suggest nine main factors for
managing the SSBI challenges: use pilot groups,
use champions, identify user groups and their data
needs, allow end users to change faulty data,
create common data definitions, serve
readymade standard reports, let business govern SSBI
content, integrate IT in the business department,
educate users [
With more and more businesses planning to
use BI to promote data-driven culture, the
selfservice business intelligence trend will only gain
more traction [
The implementation of BI in companies helps and facilitates strategic and operational decisionmaking. BI environments are changing rapidly. Challenges caused by the pandemic, technological changes and improvements, such as cloud computing and artificial intelligence require the integration of appropriate BI tools to remain competitive.
Self-service BI is being used more and more by businesses. There are many BI platforms that have included self-service BI from the leaders of database technologies such as Oracle and Microsoft and dedicated BI tools facilitating the integration of a lot of data to improve decision making among a wider group of users. Employees who are well-informed and key in the generation of reports find it easier to use them and feel satisfaction in building reports according to their requirements. The challenges in the integration of SSBI affect both the poor data quality and the knowledge of the employees in the analysis of the data. Identifying target groups, choosing and implementing the right tools are the main columns on a successful self-service BI. 4. References