In the race to generate value from data to provide businesses with a competitive advantage, business analytics has become a critical weapon. Today, companies stand out in the market by developing data-driven strategies instead of making decisions based solely on intuition. Business analytics is a systematic approach that transforms the raw data that underpins these strategies into meaningful insights. In this article, we will examine in detail what business analytics is, how it is used and the value it offers to organizations.
Business analytics is the set of methodologies, practices, and technologies that enable businesses to gain meaningful insights by examining their data and making strategic decisions based on those insights. According to Gartner's definition, business analytics is described as “operations using data, statistical and quantitative analysis, descriptive and predictive models, and data-driven management to improve decision-making processes.”
Business analytics not only draws meaningful conclusions from data, but also aims to integrate these results into business processes and strategic decisions. As digital transformation accelerates, the amount of data generated by enterprises is growing exponentially, which further emphasizes the importance of business analytics.
The scope of business analytics is quite wide and includes the following areas:
Business analytics is divided into four main categories according to its purpose and approach to use:
Descriptive analytics, “What happened?” is the type of analytics that seeks an answer to the question. By examining historical data, it provides an overview of the performance of the business. It helps to identify the current situation using historical data such as sales reports, customer behavior analysis, website traffic statistics.
For example, an e-commerce company may examine monthly sales figures, distribution by product categories, and customer segmentation through descriptive analytical methods. This type of analysis is usually presented through tables, graphs, and dashboards.
Diagnostic analytics, “Why did it happen?” focuses on the question. Explores the causes of situations detected in descriptive analytics. Correlation analyses reveal relationships between events using detailed data discovery and drill-down techniques.
A telecom company, after detecting the increase in customer losses (identifier), examines the reasons for this increase with diagnostic analytics. By analyzing the relationships between factors such as customer satisfaction scores, service interruptions, price changes, the main causes of losses are determined.
Predictive analytics, “What could it be?” aims to answer your question. Statistical models predict possible future outcomes using machine learning algorithms and data mining techniques.
According to a report by McKinsey & Company, companies that successfully implement predictive analytics can save 15-20% through data-driven decisions.
A manufacturing enterprise can anticipate equipment failures using machine learning algorithms and minimize production downtime with planned maintenance activities. Similarly, by modeling credit risk, financial institutions can identify in advance customers who may experience potential solvency difficulties.
Router analytics, “What should we do?” seeks an answer to his question. As the most advanced type of analytics, it determines the best action plan using optimization techniques and simulations.
For example, a logistics company can save fuel by optimizing delivery routes with router analytics. Similarly, a retail chain can improve its profitability by optimizing pricing strategies and inventory management.
The processes that must be followed for the successful implementation of business analytics projects are:
The first stage of the business analytics process is the collection and preparation of the data necessary for analysis. This stage is usually the one that takes the most time and is critical. Data scientists spend about 70% of their time preparing data.
In the process of data collection and preparation, the following steps are followed:
Various analysis techniques and modeling approaches are applied on the prepared data:
It is the stage of evaluating the results of the analysis from the business perspective and converting them into meaningful insights:
The most critical stage of business analytics is the transformation of the insights gained into concrete actions:
The professionals involved in the business analytics ecosystem and their responsibilities are:
Data analysts are professionals who transform raw data into meaningful information and present that information to decision makers. Their responsibilities include:
Business intelligence professionals are professionals who develop systems and reports to support strategic decisions by analyzing business data:
Data scientists assume the most technical role of business analytics as specialists developing advanced analytics, machine learning and artificial intelligence applications:
According to the LinkedIn 2024 Emerging Jobs report, demand for roles in data science and analytics has increased by an average of 35% over the past five years. Demand for data scientists, in particular, is growing 3 times faster compared to other IT roles.
The benefits of business analytics applications to organizations and the challenges encountered in this process are:
Business analytics provides businesses with several strategic advantages:
According to research by the Boston Consulting Group, companies that use data analytics effectively can achieve 5-6% higher efficiency and 3-4% higher profit margins than their competitors.
One of the biggest challenges facing business analytics applications is data quality and management:
Organizational factors are critical for the success of business analytics projects:
The current trends that stand out in the field of business analytics are:
The integration of artificial intelligence technologies into business analytics platforms enables automation of analytical processes and more complex analyses:
Self-service analytical solutions are becoming widespread, allowing non-technical business users to also perform data analysis:
According to IDC research, the use of self-service analytics tools reduces the burden on IT departments to provide analytics support to business units by 40%.
Businesses are turning to real-time analytical solutions for operational decisions beyond strategic planning:
According to the “Real-Time Analytics Market Outlook” report published by BusinessWire, the real-time analytics market will expand at a compound annual growth rate of 23.5% during the period 2024-2028.
In the process of corporate transformation, business analytics has become no longer a luxury, but a necessity. Business analytics enables businesses to generate value from raw data, enabling them to make more efficient operations, better customer experience, and more accurate strategic decisions. The increase in the amount of data and advances in technology are increasing the scope and importance of business analytics every day. To keep pace with this change, businesses must invest in the right technologies and transform their organizational structures with a data-driven understanding.
If you want to improve data-driven decision making in your organization and provide a competitive edge, take immediate action to review your business analytics strategy. Our team of experts will enable you to get maximum value from your data by developing business analytics solutions tailored to your business. IN
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