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Data Analytics & BI |
We live in the post-industrial era where data is one of the most important resources available. And in this process of continual digitization of business processes, companies have no other option but to leverage the power of data to acquire a cutting edge in this competitive market space.
This is why data analytics is of pristine importance to businesses especially when it comes to utilizing the array of tools and technological platforms available to have a more nuanced understanding of the quantitative indicators of consumer behavior, derive valuable insights, and stimulate business performance. Business intelligence is a popular mode of business analysis that remains instrumental in analyzing business data and although data analytics and business intelligence are often erroneously used interchangeably, there are multifarious differences between the two processes.
In this article, we shall discuss the underlying essence of the two terms data analytics and business intelligence along with a comprehensive articulation of the functionalities of both these techniques and elucidate the vital differences between these two methods of leveraging data for the effective conduct of diurnal business.
Business intelligence essentially refers to the method of transforming raw data into valuable insights about consumer behavior, purchasing patterns, and business performance to better understand business processes. This assists companies to gauge their productivity and efficiency. The essential elements of the business intelligence work process include identification of data points, data summarization, operationalizing data points, reporting results, creating dashboards, corroborating results to create graphs, and visualization of important data to derive key insights. The two most popular types of business intelligence have been discussed below:
Traditional Business Intelligence is a type of business intelligence method which prioritizes simple but precise reporting over other aspects such as speed. It is backed by a process and methodology that varies from business to business and even from department to department. This kind of business intelligence is leveraged in creating regulatory and financial reports where immaculate accounting is integral to a business.
Modern Business Intelligence is used where a fast turnaround time for the delivery of insights is of more importance as compared to the precision of the results. This technique is useful to businesses in evolving enterprises such as the e-commerce and FMCG sectors. This can substantially benefit such organizations by providing them with valuable data-backed insights into the behavioral pattern of a large target group of customers and also in identifying the predominant trends.
Data Analytics is the process of scrutinizing data by leveraging tools based on programming languages such as Python and Stata to guide businesses with tactical and strategic decisions. This process allows enterprises to derive insights that might have not been possible with business intelligence as it entails more advanced methods of data evaluation. A few of the essential types of data analytics have been discussed below:
This data analytics methodology is similar to business intelligence where raw data is analyzed to derive various descriptive statistical insights like mean, median, and mode. Descriptive data analytics does not include complex analytical processes and can be done using linear regression tools and other similar open-source software.
Diagnostic data analysis refers to correlating different variables from all available data sets and performing a root cause analysis. This is a pivotal method in the domain of data analysis especially when it comes to problem-solving and critical reasoning. This allows businesses to overcome any future hindrances and operationalize to meet their most crucial objectives.
Perspective data analytics is used to predict the future outcomes of business processes contingent on the changes that the managerial board is willing to incorporate. For instance, if the business is aware of certain outcomes through predictive data analytics it can undertake various reformative measures that are likely to perform better under the given set of circumstances.
Predictive data analytics is the process of leveraging historical data to derive predictive insights in an attempt to forecast business performance in the near future. Companies can amend erroneous business processes with predictive data analytics to make daily business processes more effective, and seamless to reduce costs and improve outreach.
In this section, we shall discuss the six major differences between business intelligence and data analytics so that businesses can make informed decisions to leverage the correct technique and derive the best results.
The first major difference between business intelligence and data analytics is the scope of their performance. While business intelligence is used to derive operational insights about business processes, data analytics is more useful when it comes to performing a wide range of analytical assessments based on a large set of data. Business intelligence allows us to create dashboards and generate comprehensive reports but data analytics lets us helps operationalize the various critical variables and establish correlations to perform a causal analysis.
The second point of difference between business intelligence and data analytics is the level of dependence on coding platforms and programming. While business intelligence does not require extensive programming due to the several tools to generate statistical reports and visualize data sets, data analytics is based on programming languages like Stata, Java, and Python which are used to create complex codes for an exhaustive analysis of large sets of data. Although several business intelligence tools include rudimentary analytical functions the scope of real-time analysis is still limited which necessitates data analysts to be thoroughly trained in programming languages.
The next major difference between business intelligence and data analytics is the type of data that can be analyzed using the two processes. While business intelligence is limited to the analysis of tabular data which is curated with the tools that structure data, data analytics can be leveraged to analyze both unstructured and structured data along with data in text, audio, or video formats. Data analysis can even generate insights from social media platforms such as Facebook, Instagram, and Twitter while business intelligence is limited to descriptive analysis of structured tabular data available via internal and external databases.
Data warehousing is an essential step in business intelligence which improves the quality of data for streamlined analytical tools. However, data analytics is not dependent on data warehousing techniques and a data analyst can directly generate information from data lakes or discrete data sources such as websites, social media channels, applications, and other data repositories. Another point of difference likes in the fact that data analytics uses the process of data wrangling which is not used in business intelligence. Data analysts often perform data cleaning which is outside the purview of business intelligence as well.
While it is still professionally possible to practice business intelligence without core math skills such as probability and linear algebra, performing data analytics requires at least some fundamental critical math skills. This is because data analytics involves conducting an extensive analysis of datasets which cannot be done through customized commands and standalone software. Moreover, it is possible to practice business intelligence actions with specific platform languages like Data Analysis Expressions (DAX) but sharp math skills are indispensable as far as data analytics is concerned.
Business intelligence is mainly based on basic descriptive statistics that derive rudimentary insights including the mean, median, mode, and standard deviation, along with data visualization. However, Data analytics combines both descriptive and inferential statistics to perform a wider range of statistical actions and perform inferential analysis of historical data which is essential for data analytics techniques such as diagnostic, predictive, and perspective data analytics. Statistics is also essential for A/B Testing allows businesses to make informed decisions about business processes and have a sustainable impact on revenue generation and customer satisfaction.
The lines between business intelligence and data analytics are getting more and more blurred as newer technologies shape up the modern-day business world. This makes it vital for businesses across all domains to understand their fundamentals, methods, and the possible outcomes for both these optimization methods. Knowing their major differences is one of the best ways to begin this journey and measure what is required by the organization and what is largely redundant.
Moreover, both business intelligence and data analytics leverages technology to help businesses derive data-backed insights that lead to more effective business processes, higher productivity, streamlined targeting for marketing and communications, reduction in costs, et al. To know more about the processes of data analytics and business intelligence, and to understand which is the best fit for your organization, connect with a top-tier technology consulting firm such as Focaloid Technologies today!