How to use analytics to boost your bottom line

October 14, 2021 | 5 minute read
Jason Jacquez
Director, Product Marketing
Text Size 100%:

Data is growing faster than ever, and today organizations in all industries have high expectations for how to use their data as operations are carried out.  There is an abundance of information available to any organization that is ready to empower their employees to improve the quality of operational decision-making and business experience.

Analytics is empowering businesses to drive change for better outcomes while also supporting growth.  The greater number of people who answer questions from their data - the better the results for the organizations to drive the future.  It’s become even more critical for all organization's decisions to be ground with solid data. 

Analytics can enable businesses to take data to the next level – understand progress, evaluate options, and quickly help make the right fact-based decision.  The data an organization uses may include numerous types of information such as customer audience demographics, interests, financials, behaviors, and more.  Cross-functional analytics help eliminate data silos within the organization with a single extensible data model for analytics across multiple applications.  One example of cross-functional analytics is uniting finance and HR data in order to boost productivity and profitability.  Business applications that include integrated analytic capabilities, generally referred to as packaged analytic applications, help organizations improve the quality of operations and decisions, enable users to generate self-service insights, and leverage the power of cloud-based software to reduce time-to-value. 

For all businesses, analytics can be the difference between growth or decline.  Analytics empowers employees to access relevant data, identify patterns and relationships for evaluating outcomes, and helps make decisions quickly.  Here are some questions to consider to help strengthen your transformation using analytics. 

       1.  What are you doing to improve the business data strategy?

Collecting and analyzing large amounts of cross-functional data – that is, data drawn from more than one department or business unit – helps organizations take the next step forward in their analytics journey.  Data analytics based on a range of information that extends beyond the limits of the department can enable all business groups to make decisions and take actions for the organization to best meet its strategic and operational objectives.  

       2.  What are the benefits of cross-functional analytics? 

The most common benefit from supporting cross-functional data usage is gaining a competitive advantage.  Companies report lowering costs and improving the customer experience – both which improve overall organizational performance.  Cross-functional analyses provide a better understanding of how an organization is performing and creates better communication by sharing same sets of information across different departments.  For example, to be effective management may need to monitor data from specific regions and business groups.  This would not be achievable if departments were unable to access, combine and analyze data from other areas of the business. 

       3.  Is there an overall understanding of how to access your data?

While providing cross-functional data to employees demonstrably has value, it often requires a significant amount of work to bring this data together from across the entire business and prepare it for analytics.  Data preparation is often where the most time is spent, as data from one application is rarely sufficient and data from multiple sources is often not in the form that is required.  Commonly this process is tasked to IT, which can create bottlenecks in the analytics process as employees submit requests and wait for IT to respond.  Organizations require stable, resilient, and secure access to information always, with analytics solutions that withstand the pivots and scalability demands of cloud transformation initiatives. Users must be able to explore data self-sufficiently and understand the output of machine learning (ML) models by using familiar terminology. 

       4.  How can you make smarter predictions and better decisions easier and faster?

With the volume, variety, and sources of data constantly growing, machine learning (ML) helps users discover unseen patters from data.  ML that is built into analytics removes human bias and enables users to easily interpret possible outcomes and opportunities.  Business users do not need special technical or programming skills to use ML.  Embedded machine learning and natural language processing technologies help increase productivity and help build analytics-driven culture in organizations.  Every department can use ML to build custom, business-specific models for better decision making    

       5.  How can you deliver valuable insights to make quick and accurate decisions?  

Incorporating cross-functional and external data sources can enhance an entire businesses metrics and provide a more thorough extended analyses and better-connected insights.  Diverse data sets can enable the generation of strategic, executive-level analytics instead of just more tactical analyses.  For example, delivery data in supply chain can be combined with data from sales returns and warranty claims to produce more meaningful information improving procurement.  Using all relevant sources with machine learning (ML) driven recommendations can help make more accurate decisions about how to grow revenues, decrease costs and improve profitability.    

       6.  Can you build customized reports and dashboards for specific analyses? 

Yes.  Vendors should design their software systems anticipating that customers will want to make modifications, yet not all vendors provide both IT delivered and controlled, and business led self-service analytics.  Both services are essential for an organization and businesses should reject systems that don’t provide these capabilities.  To be competitively useful, an application should make it possible for decision-making mechanisms to directly impact business operations. 

       7.  How can we receive timely analytics and rely less on IT?

Adopting packaged cloud-based analytics applications can further help organizations accomplish the goal of self-service because when applications are delivered via the cloud, many of the installation, configuration and management functions are shifted to the software vendor.  With cloud-based applications that include packaged analytics, executives can cost-effectively achieve the broader, more strategic view that analytics sourced from a single application cannot provide.

       8.  What are some best tips to help your organization grow using analytics?

  • Focus on enabling cross-department analysis
  • Consider packaged analytic applications to empower the organization
  • Ensure packaged applications can be extended to meet the organizations needs
  • Prioritize self-service with cloud-based tools

Organizations require stable, resilient, and secure access to information, and with the use of analytics solutions, they can strengthen their transformation initiatives.  Users must be able to explore data self-sufficiently and understand the output of machine learning (ML) models by using familiar terminology. This is what Oracle Analytics delivers - empowering all employees to access relevant data; identify patterns and relationships for evaluating outcomes; and make decisions quickly.

For more information about modernizing with data, visit here  

To learn how you can benefit from an analytics solution from Oracle visit Oracle.com/ analytics, and follow us on Twitter @OracleAnalytics

Jason Jacquez

Director, Product Marketing


Previous Post

Achieve a fleet-wide view of database security with Oracle Data Safe

Bettina Schaeumer | 5 min read

Next Post


Why Arm-based application development is best on Oracle Linux in Oracle Cloud Infrastructure

Julie Wong | 5 min read