Building a Data and Analytics Framework

data and analytics framework

We have been hearing for years that big data and a proper data analytics strategy for organizations are a key factor of success. But how successful are most companies at achieving this seemingly simple task? “Cross-industry studies show that on average, less than half of an organization’s structured data is actively used in making decisions – and less than 1% of its unstructured data is analyzed or used at all.” (What’s Your Data Strategy?). This means that many companies are missing a clear strategy for data analytics driving business decisions.  

Having a business/data analytics team is a good start, but developing an overall strategy for extracting valuable insight into business questions that drive decisions is imperative for success. To tell the full story of developing a strategy for your organization’s analytics framework we’ll cover common problems organizations experience, industry best practices for solving common problems, and weighing available data reporting/visualization tools. 

There are several common issues organizations face related to their analytics strategy. Overall, these issues generalize to undefined questions, data, or visualizations for meeting business goals. Common issues experienced are: 

-  Collecting the correct data from the correct locations, or poor quality of data

- Data not answering the key questions needed for business decisions

-Correct data is collected, but it is very manual or painful to easily extract value from the data (No clear visual representations of data)

-Lack of organizational support for analytics and business insight, or not receiving top and lower-level support for initiatives

-Shortage of skills in an organization

-Scaling data analysis as the organization and data collected grows

As you can infer, these common issues can cause organizations to lose a competitive edge in a data-driven world. So what is to be done if you are experiencing any of the listed common issues? 

There are several areas to focus improvement efforts to alleviate pain from your organization’s data analytics strategy. First, you’ll need to understand your data needs. Before you go down the path of curating your data, you’ll need to first define how you want to use the data.  

Next, you’ll have to consider how you will source the data. There are many ways to source your data. You can collect internal data, external data, or develop new data collection methods for your organization.  

After gathering the data, you’ll need to consider how to turn it into key insights. Well executed business decisions are often the result of turning the data into insights. To do this you will need to develop the correct analytical approach to the data set(s) you have.  

Evaluating your technology infrastructure requirements is another crucial task. Specifically, this is deciding on what software or hardware is needed for the analytical strategy that you decide on. Deciding how your organization wants to use, procure, analyze, and shape the infrastructure of your data can be considered easier tasks toward your ideal future state.  

One of the largest issues experienced by most organizations is the data competencies throughout their organization. Simply put, do the resources in your company have the proper skill set to achieve your analytical goals? Like most organizations, if the answer is ‘No’ then you have two options: work to train your in-house talent, or outsource talent for data analysis. Once your data analytics strategy is defined, you will need to ensure there are proper resources to handle the work, both technical and functional data analysts/data scientists. 

Now that we have defined the key components of the data analytics framework, let’s discuss some of the tools available in the marketplace for analyzing and visualizing your data to provide the insights your organization desires. The main four marketplace competitors for data analytics and visualization are Kibana/ Elastic, Tableau, Power BI, and Cloudera. When considering these solutions there are a few main criteria to evaluate these by: 

-Is it built for big data? – Consider Kibana or Cloudera

-Does it have delivered visualizations? – Consider Kibana

-Is it designed for smaller, transactional data? – Kibana, Tableau, and Power BI all apply

-Is it open-sourced to meet configurable needs? – Consider Kibana or Cloudera

As you can see, each of the four analytics and visualization tools each has their own strengths, however, the ELK Stack/Kibana package is a true industry leader for most business needs. Kibana is a very powerful data analytics and visualization tool mainly because of the way the data is being pulled. Kibana doesn’t utilize the typical relational database structure for pulling the data because this can take up a lot of your system power, plus larger data sets will take up more and more time. Kibana instead utilizes indexes, similar to the way Google can search on massive amounts of data. This main differentiator for Kibana puts it ahead of other market competitors.  

To learn more about Kibana, view Elire’s recent webinar, “Kibana and Elasticsearch: Improving Analytics through Data Visualization” or view the library of session recordings from the 2021 Elire Strategy Summit here.

For more information about Elastic/Kibana Service, visit our PeopleSoft Services page here and download our Kibana Analytics informational PDF.


  • Maddie Caron

    Ms. Caron serves as Elire's Marketing Specialist, specializing in content writing and digital media communications. Maddie works to deliver relevant industry updates and technical blog posts to educate and engage Elire's audience.

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