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April 17, 2023

Why Is Business Analytics Important?

Microsoft Cloud Solutions

April 17, 2023

Why Is Business Analytics Important?

Business analytics is the practice of using data and analytical techniques to gain insights that help inform business decisions. With the right approach, organizations can unlock a wealth of valuable information from the data they are already generating to make faster, more informed strategic choices. It has become an essential tool for business leaders as they seek opportunities for growth and a competitive edge for their companies.

What Is Business Analytics?

This refers to a complex process that involves gathering, analyzing and interpreting data to drive decision-making. It is a multidimensional approach to data analysis that encompasses business intelligence, data mining, and predictive modeling techniques. With the help of advanced analytical tools, organizations can identify trends, patterns, and outliers that hold the key to solving their toughest challenges.

 

What Are the Benefits?

Through proper analysis, companies can find patterns, relationships and trends that enable them to make faster, better decisions in their operations. The ability to spot trends and react quickly creates 'strategic agility' that can provide a competitive advantage.

 

How Does This Inform Business Decisions?

Decision-making becomes much faster and far less subjective when using a defined, consistent approach for reacting to objective inputs (various data sets). This mitigates the substantial risk that comes with leading companies by intuition alone.

 

How Is This Different from Business Intelligence, Data Analytics, and Data Analysis?

Business Intelligence

Business intelligence (BI) is about understanding what happened in the past. Companies can see how they did in the past and use that information to plan for the future.

Data Analytics

Data analytics is a general term that refers to the concept of leveraging data to make better decisions.

Data Analysis

Data analysis is a subset of data analytics where specific processes define how data is inspected, processed, and interpreted. More sophisticated approaches apply statistical and computational techniques as well as machine learning in order to handle larger data sets, accelerate its analysis, and represent it visually.

 

How Does Business Analytics Work?

These programs encompass a series of processes that translate raw data into insight. Business data from a variety of internal and external sources is aggregated, analyzed, and presented in a format that allows decision makers to quickly understand it. They can then collaborate and align on the corresponding course of action in very short order.

Direct integration of the data feeds and dashboards can accelerate these efforts to the point where data driven decision making is nearly automatic.

 

How Is This Approach Typically Applied?

The potential uses are only limited by the scope and volume of available data. With the right inputs, analysis can uncover actionable insights within customer behavior, market trends, sales performance, or operational efficiency. Companies of any size or industry stand to gain a material advantage by developing an effective data analytics strategy.

 

What Are the Different Types of Analytics?

Descriptive Analytics

Descriptive analytics uses historical business data to describe what happened in the past.

Diagnostic Analytics

Diagnostic analytics, or 'drill-down analytics', is used to uncover patterns, trends, and correlations in historical data that explain why something happened.

Predictive Analytics

Predictive analytics identifies patterns in data to predict future outcomes. Methods such as regression analysis, decision trees, neural networks, and clustering can help establish and quantify the cause-and-effect relationships between any number of business dynamics.

Prescriptive Analytics

Prescriptive analytics goes a step further by recommending what should be done in a given situation to achieve a desired outcome. Mathematical modeling, simulation, machine learning, and optimization are often part of these frameworks.

 

 

Descriptive Analytics vs Diagnostic Analytics vs Predictive Analytics vs Prescriptive Analytics

 

 

What Are the Most Common Tools?

Spreadsheet software such as Microsoft Excel is the typical starting point for basic data analytics. However, most companies quickly opt for more powerful data analytics tools that are capable of more robust and faster analysis.

Through systems integrations and the development of custom applications, companies can virtually eliminate the time, effort, and potential for errors inherent with manual data management.

Microsoft's Power BI is one of the most popular solutions for data visualization. It provides dynamic dashboards that empowers users to see and dig into data in real time. They can be customized and configured to show the most important metrics in the most intuitive way.

 

 

Why is Business Analytics Important - Sample BI Dashboard Configurations

 

 

What Are the Most Critical Skills?

In addition to software and systems, organizations need business analysts with a broad set of skills in order to conduct analysis properly:

  • Data Analysis: The ability to analyze data is the foundation of business analytics. Analysts must be able to clean and transform unstructured data, build models, and use apply statistical analysis to derive insights.
  • Business Acumen: Successful analysts understand their business, including organizational goals, challenges, and competitors. By knowing what drives the business, they can focus on the most relevant metrics.
  • Critical Thinking: The ability to think critically is essential to making accurate and meaningful insights from data. Analysts must be able to question assumptions, test hypotheses, and consider alternative explanations to avoid drawing erroneous conclusions.
  • Communication: Skilled analysts can translate technical analyses into plain language and explain their implications for different audiences, including decision-makers, who may or may not be data-savvy.
  • Visualization: Thoughtful and visually intuitive charts, graphs and other dashboards are critical in providing stakeholders the insights they need quickly.
  • Project Management: Effectively managing projects, prioritizing tasks, and meeting deadlines ensure that analysts not only produce valuable insights but also do so efficiently.
  • Programming: The ability to manipulate data, build models, refine algorithms, and automate business processes is very useful in managing more advanced programs. Aspiring business analysts should build competency with programming languages such as Python and R.

How Can Companies Build Out Analytical Capabilities?

Enabling analytical capabilities requires a systematic approach that involves the integration of technology, data management, and human expertise. Companies need to invest in the right resources and infrastructure to support continuous improvement and innovation.

Here are some critical steps that companies can take to develop these capabilities internally:

  • Set objectives: Start by defining clear business objectives that align with their overall strategic goals. The associated key performance indicators (KPIs) will be used to track progress and measure success.
  • Invest in data infrastructure: Technology such as data lakes, data warehouses, and data visualization software will be required to collect, manage, and analyze data.
  • Develop a data governance framework: A data governance framework must be established to ensure data quality and integrity. This includes defining data standards, ensuring data privacy and security, as well as establishing roles and responsibilities for data management.
  • Hire the right talent: Teams comprised of data analysts, data scientists, and visualization experts must have the expertise to properly interpret data, extract insights, and contribute to making data-driven decisions.
  • Commit to continued development: Ongoing training is critical. Companies should encourage employees to take courses, attend workshops, and earn certifications that bolster their skills and keep them current with the latest industry trends.
  • Foster a data-driven culture: Companies need to encourage innovation, experimentation, and continuous improvement. This involves promoting data literacy, where everyone in the organization understands the value of data and how to use it to make informed decisions.

What Are the Common Challenges?

It is easy to get excited about the potential impact of leveraging data to fuel business growth, but there tend to be some challenges in standing up these programs.

Here are some of the hurdles that companies tend to encounter:

  • Data Quality: One of the most foundational considerations is ensuring the accuracy and reliability of the data used for analysis. It must be clean, standardized, and up to date. Otherwise, the insights derived from the data can be misleading or incorrect.
  • Data Privacy and Security: Businesses must also consider data privacy and security concerns when collecting and analyzing data. New data privacy regulations like GDPR and CCPA create an increasingly complex regulatory landscape that companies must successfully navigate in order to avoid costly fines and reputational damage.
  • Skills Gap: Expertise in data science and analytics is in short supply, making it difficult to recruit employees with the needed skills.
  • Legacy Systems: Outdated legacy systems may not be compatible with modern analytics tools. This can lead to problems with data integration, processing, and accessibility.
  • Culture: Establishing business analytics requires a cultural shift towards data-driven decision-making. This can be a significant challenge in organizations where decision-making is still based on intuition, experience, or seniority.

Leaders are willing to face these challenges because they realize the potential upside of revolutionizing how their companies run. Few have the time, resources, and expertise to do that on their own and avoid all the pitfalls along the way. That's where we come in.

 

How Does Aspirant Help Clients Harness the Power of Their Data?

Aspirant's data and analytics experts know how to anticipate, identify, and resolve problems that would otherwise limit data analytics programs or prevent them from ever being implemented.

We can also develop custom applications and/or integrations to install a comprehensive data collection infrastructure.

 

Aspirant is a Microsoft Partner

 

As mentioned above, there is much more to cultivating analytical capabilities than technology. Aspirant's delivery model is based on Integrated Expertise, which provides direct access to all the cross-functional support needed to stand up a state-of-the-art, difference-making business intelligence program.

 

Learn more about Aspirant's Integrated Expertise

 

 

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