Requirement of business analytics

Business analytics refers to the statistical methods and computing technologies for processing, mining and visualizing data to uncover patterns, relationships and insights that enable better business decision-making.

Business analytics involves companies that use data created by their operations or publicly available data to solve business problems, monitor their business fundamentals, identify new growth opportunities, and better serve their customers.

Business analytics uses data exploration, data visualization, integrated dashboards, and more to provide users with access to actionable data and business insights.

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Business intelligence (BI) enables better business decisions that are based on a foundation of business data. Business analytics (BA) is a subset of business intelligence, with business analytics providing the analysis, while the umbrella business intelligence infrastructure includes the tools for the identification and storage of the data that will be used for decision-making.

Business intelligence collects, manages and uses both the raw input data and also the resulting knowledge and actionable insights generated by business analytics. The ongoing purpose of business analytics is to develop new knowledge and insights to increase a company’s total business intelligence.

Business analytics can be used to answer questions about what happened in the past, make predictions and forecast business results. 1 An organization can gain a more complete picture of its business, enabling it to understand user behavior more effectively.

Data scientists and advanced data analysts use business analytics to provide advanced statistical analysis. Some examples of statistical analysis include regression analysis which uses previous sales data to estimate customer lifetime value, and cluster analysis for analyzing and segmenting high-usage and low-usage users in a particular area.

Business analytics solutions provide benefits for all departments, including finance, human resources, supply chain, marketing, sales or information technology, plus all industries, including healthcare, financial services and consumer goods.

Business analytics uses analytics—the action of deriving insights from data—to drive increases in business performance. 4 types of valuable analytics are often used:

As the name implies, this type of analytics describes the data it contains. An example would be a pie chart that breaks down the demographics of a company’s customers.

Diagnostic analytics helps pinpoint the root cause of an event. It can help answer questions such as: What are the series of events that influenced the business outcomes? Where do the true correlation and causality lie within a given historical time frame? What are the drivers behind the findings? For example, manufacturers can analyze a failed component on an assembly line and determine the reason behind its failure.

Predictive analytics mines existing data, identifies patterns and helps companies predict what might happen in the future based on that data. It uses predictive models that make hypotheses about future behaviors or outcomes. For example, an organization could make predictions about the change in coat sales if the upcoming winter season is projected to have warmer temperatures.

Predictive modeling 2 also helps organizations avoid issues before they occur, such as knowing when a vehicle or tool will break and intervening before it occurs, or knowing when changing demographics or psychographics will positively or negatively impact their product lines.

These analytics help organizations make decisions about the future based on existing information and resources. Every business can use prescriptive analytics by reviewing their existing data to make a guess about what will happen next. For example, marketing and sales organizations can analyze the lead success rates of recent content to determine what types of content they should prioritize in the future. Financial services firms use it for fraud detection by analyzing existing data to make real-time decisions on whether any purchase is potentially fraudulent.

Business analytics practices involve several tools that help companies make sense of the data they are collecting and use it to turn that data into insights. Here are some of the most common tools, disciplines and approaches:

Data management: Data management is the practice of ingesting, processing, securing and storing an organization’s data. It is then used for strategic decision-making to improve business outcomes. The data management discipline has become an increasing priority as expanding data stores has created significant challenges, such as data silos, security risks and general bottlenecks to decision-making.

Modern organizations need to be able to make quick decisions to compete in a rapidly changing world, where new competitors spring up frequently and customers’ habits are always changing. Organizations that prioritize business analytics have several advantages over competitors who do not.

Faster and better-informed decisions: Having a flexible and expansive view of all the data an organization possesses can eliminate uncertainty, prompt an organization to take action faster, and improve business processes. If an organization’s data suggests that sales of a particular product line are declining precipitously, it might decide to discontinue that line. If climate risk impacts the harvesting of a raw material another organization depends on, it might need to source a new material from somewhere else. It’s especially helpful when considering pricing strategies.

How a company prices its goods or services is based on thousands of data points, many of which do not remain static over time. Whether a company has a fixed or dynamic pricing strategy, being able to access real-time data to make smarter short- and long-term pricing data is critical. For organizations that want to incorporate dynamic pricing, business analytics enables them to use thousands of data points to react to external events and trends to identify the most profitable price point as frequently as necessary.

Single-window view of information: Increased collaboration between departments and line-of-business users means that everyone has the same data and is talking from the same playbook. Having that single pane of glass shows more unseen patterns, enabling different departments to understand the company’s holistic approach and increase an organization’s ability to respond to changes in the marketplace.

Enhanced customer service: By knowing what customers want, when and how they want it, organizations encourage happier customers and build greater loyalty. In addition to an improved customer experience, by being able to make smarter decisions on resource allocation or manufacturing, organizations are likely able to offer those goods or services at a more affordable price.

Companies looking to harness business data will likely need to upskill existing employees or hire new employees, potentially creating new job descriptions. Data-driven organizations need employees with excellent hands-on analytical and communication skills.

Here are some of the employees that they need to take advantage of the full potential of robust business analytics strategies:

Data scientists: These people are responsible for managing the algorithms and models that power the business analytics programs. Organizational data scientists either use open source libraries, such as the natural language toolkit (NTLK) for algorithms or build their own to analyze data. They excel at problem-solving and usually need to know several programming languages, such as Python, which helps access out-of-the-box machine learning algorithms and structured query language (SQL), which helps extract data from databases to feed into a model.

In recent years, an increasing number of schools offer Master of Science or Bachelor’s degrees in data science where students engage in degree program coursework that teaches them computer science, statistical modeling and other mathematical applications.

Data engineers: They create and maintain information systems that collect data from different places that are cleaned and sorted, and placed into a master database. They are often responsible for helping to ensure that data can be easily collected and accessed by stakeholders to provide organizations with a unified view of their data operations.

Data analysts: They play a pivotal role in communicating insights to external and internal stakeholders. Depending on the size of the organization, they might collect and analyze the data sets and build the data visualizations, or they might take the work created by other data scientists and focus on building strong storytelling for the key takeaways.

To maximize the benefits of an organization’s business analytics, it needs to clean and connect its data, create data visualizations and provide insights on where the business is today while helping predict what will happen tomorrow. This usually involves these steps:

First, organizations must identify all the data they have on hand and what external data they want to incorporate to understand what opportunities for business analytics they have.

Unfortunately, much of a company's data remains uncleaned, rendering it useless for accurate analysis until addressed.

Here are some reasons why an organization’s data might need cleaning:

Incorrect data fields: Due to manual entry or incorrect data transfers, an organization might have bad data mixed in with accurate data. If it has any bad data in the system, this has the potential to render the entire set meaningless.

Companies can now query and quickly parse gigabytes or terabytes of data rapidly with more cloud computing. Data scientists can analyze data more effectively by using machine learning, algorithms, artificial intelligence (AI) and other technologies. Doing so can produce actionable insights based on an organization’s key performance indicators (KPIs).

Business analytics programs can now quickly take huge amounts of that analyzed data to create dashboards, visualizations and panels where the data can be stored, viewed, sorted, manipulated and sent to stakeholders.

Data visualization best practices include understanding which visual best fits the data an organization is using and the key points it hopes to make, keeping the visual as clean and simple as possible, and providing the right explanations and content to help ensure that the audience understands what they’re viewing.

Ongoing data management is conducted in tandem with what was mentioned earlier. An organization that embraces business analytics must create a comprehensive strategy for maintaining its cleaned data, especially as it incorporates new data sources.

Business analytics are useful for every type of business unit as a way to make sense of the data it has and help it generate specific insights that drive smarter decision-making.

Financial and operational planning: Business analytics provides valuable insights to help organizations align their financial planning and operations more seamlessly. It does this by setting rules for supply chain management, integrating data across functions, and improving supply chain analytics and demand forecasting.

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