Business Analytics

Business analytics is a set of disciplines and technologies for solving business problems using data analysis, statistical models and other quantitative methods. It involves an iterative, methodical exploration of an organization’s data, with an emphasis on statistical analysis, to drive decision-making.

Data-driven companies treat their data as a business asset and actively look for ways to turn it into a competitive advantage. Success with business analytics depends on data quality, skilled analysts who understand the technologies and the business, and a commitment to using data to gain insights that inform business decisions.

Process:

• Determine the business goal of the analysis.
• Select an analysis methodology.
• Get business data to support the analysis, often from various systems and sources.
• Cleanse and integrate data into a single repository, such as a data warehouse or data mart.

A workflow for the business analytics process is as follows:

• Data collection: Wherever data comes from, be it IoT devices, apps, spreadsheets, or social media, all of that data needs to get pooled and centralized for access. Using a cloud database makes the collection process significantly easier.
• Data mining: Once data arrives and is stored (usually in a data lake), it must be sorted and processed. Machine learning algorithms can accelerate this by recognizing patterns and repeatable actions, such as establishing metadata for data from specific sources, allowing data scientists to focus more on deriving insights rather than manual logistical tasks.
• Descriptive analytics: Descriptive data analytics answers these questions to build a greater understanding of the story behind the data.
• Predictive analytics: With enough data and enough processing of descriptive analytics business analytics tools can start to build predictive models based on trends and historical context. These models can thus be used to inform future decisions regarding business and organizational choices.
• Visualization and reporting: Visualization and reporting tools can help break down the numbers and models so that the human eye can easily grasp what is being presented. Not only does this make presentations easier, these types of tools can help anyone from experienced data scientists to business users quickly uncover new insights.

The main components of a typical business analytics dashboard include:

• Data Mining: Data mining for business analytics sorts through large datasets using databases, statistics, and machine learning to identify trends and establish relationships.
• Data Aggregation: Prior to analysis, data must first be gathered, organized, and filtered, either through volunteered data or transactional records.
• Association and Sequence Identification: The identification of predictable actions that are performed in association with other actions or sequentially.
• Text Mining: Explores and organizes large, unstructured text datasets for the purpose of qualitative and quantitative analysis.
• Forecasting: Analyzes historical data from a specific period in order to make informed estimates that are predictive in determining future events or behaviors.
• Predictive Analytics: Predictive business analytics uses a variety of statistical techniques to create predictive models, which extract information from datasets, identify patterns, and provide a predictive score for an array of organizational outcomes.
• Data Visualization: Provides visual representations such as charts and graphs for easy and quick data analysis.
• Optimization: Once trends have been identified and predictions have been made, businesses can engage simulation techniques to test out best-case scenarios.

Benefits of Business analytics

Business analytics benefits impact every corner of your organization. When data across departments consolidates into a single source, it syncs up everyone in the end-to-end process. This ensures there are no gaps in data or communication, thus unlocking benefits such as:

Easy visualization: Business analytics software can take unwieldy amounts of data and turn it into simple-yet-effective visualizations. This accomplishes two things. First, it makes insights much more accessible for business users with just a few clicks. Second, by putting data in a visual format, new ideas can be uncovered simply by viewing the data in a different format.

Data-driven decisions: With business analytics, hard decisions become smarter and by smart, that means that they are backed up by data. Quantifying root causes and clearly identifying trends creates a smarter way to look at the future of an organization, whether it be HR budgets, marketing campaigns, manufacturing and supply chain needs, or sales outreach programs.

Modeling the what-if scenario: Predictive analytics creates models for users to look for trends and patterns that will affect future outcomes. This previously was the domain of experienced data scientists, but with business analytics software powered by machine learning, these models can be generated within the platform. That gives business users the ability to quickly tweak the model by creating what-if scenarios with slightly different variables without any need to create sophisticated algorithms.

Go augmented: All of the points above consider the ways that business data analytics expedite user-driven insights. But when business analytics software is powered by machine learning and artificial intelligence, the power of augmented analytics is unlocked. Augmented analytics uses the ability to self-learn, adapt, and process bulk quantities of data to automate processes and generate insights without human bias.

Disadvantages of Business Analytics

Lack of Commitment

Since the solutions that are prefabricated from the analysts are not particularly difficult to implement; they can be very costly and the ROI is not immediate. By nature, these analytics models are prepared to improve accuracy over time but it is a complex model that requires dedication to implement the solution. Because the business users do not see the promised results immediately, they lose interest which results in loss of trust as a result of which the models fail.

Lack of alignment, availability and trust

In most organizations, the analysts are organized according to the business domains. Unfortunately, the analysis is shared with the top executives and thus the results are not easily communicated to the business users for whom they provide the greatest value.

Low quality of underlying transactional data

Implementation of the solutions provided by the business analysts fail because the data is not available, the data sources are too complex or they are poorly constructed.

Business analytics requires a dedicated and coherent approach and a good level of maturity. In order to become a good business analyst, you need to take a business analytics course. Nowadays, there are several online business analytics certifications available. These business analytics courses help you a great deal with adopting the best measures to identify data sources based on mapping analytical requirements.