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How To Set Up Data Analytics Process For Enterprise Data?

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Businesses today are innately data-driven. More than ever, business stakeholders need to know how to analyze data effectively. And yet, the enterprise data analytics process remains fragmented across organizational silos.

But getting started with data analytics can be challenging for any company that wants to implement it to gain insight into the most valuable data, analyze it, and transform it into meaningful, actionable information. And the biggest challenge is that most organizations are already spread too thin, which prevents them from dedicating the level of time, money, and top talent needed to realize the full potential of analytics.

To use the data available effectively, an organization should assemble an analytics platform that can bring disparate information together, to form logical patterns and associated data points in a more reportable manner that decision-makers can comprehend better.

Big data means nothing if you can’t harness it. But, getting to grips with it doesn’t have to be a headache. This blog post will give you an overview of what you’ll need in terms of software, skills, and processes to establish an effective analytics process for big data analytics with Al Rafay.

That said, a typical Data Analytics and Business Intelligence platform would basically include 5 layers/ stages:

  1. Data Collection
  2. Data Cleaning and Storage
  3. Data Integration and Transformation
  4. Data Preparation and Analytics
  5. Business Intelligence or Data Visualization

Enterprise Data Analytics Process Step #1: Data Collection

The first step of any analytics process is data collection. Data gathering involves creating “source data,” “raw data,” or “first-stage data” (whichever you prefer).

Remember the old say: “Garbage in, garbage out.” Start by collecting lots of data, and make sure there are lots of different kinds of data. The more types of data you have, the more insights you are likely to discover.

Before your enterprise embarks on its analytics journey by collecting data, 2 things should be very clear.

What are the questions you want to answer with the data analytics process? What is the “problem statement”?
Can you validate and integrate the collected data with other available information to develop a holistic picture?
Lastly, the collection of data itself can be done through various methods like surveys, interviews, workshops, focus groups, observations, etc.

Enterprise Data Analytics Process Step #2: Data Cleaning and Storage

One of the significant challenges with ubiquitous data is ensuring data quality. Enterprise data cleaning is one of the most critical (and sensitive) data tasks. Data cleaning is the process through which data analysts and statisticians perform data preparation and cleansing tasks so that the data is ready to be analyzed. It usually involves data cleaning, data transformation, data aggregation, and data standardization.

Data cleaning is time-consuming but absolutely necessary. It often requires working with raw data and a combination of specialized skills. Data needs to be cleaned to:

Bring structure to the data: fixing typos, ensuring consistency to help map and transform data easily.
Remove duplicates or errors
Tidy up by filling in major gaps of important data points.
Data cleaning and preparation are important for analytical projects, but the most important requirement is to store data to make it useable and searchable. Data must be collected and stored to facilitate data cleaning, data analysis, and business intelligence.

Enterprise Data Analytics Process Step #3: Data Integration and Transformation

In step 1, you identify the data you have. In step 2, you identified the data you need. Now it’s time to connect the data you have with the data you need.

This is where ETL comes in. ETL stands for Extract, Transform, and Load. The basic idea is that you want to take the data from whatever source you have and put it into some format that is suitable for whatever destination you want.

Data Integration and Transformation is major step in the Analytics pipeline. This step involves bringing together siloed organizational data, integrating information from different sources to join the dots in a consistent way. By doing this it ensures an organizational metric is universal and data is interoperable. This is typically done through Data Pipelines & Data Warehouses For Al Rafay.

There are multiple options available through which one can achieve data integration. But how do you choose the right option which cuts down your cut integration costs? Well, check out ZIO (Enterprise Data Bus Solution).

Enterprise Data Analytics Process Step #4: Data Preparation and Analytics

The fourth step in a data analytics process is data preparation and analytics. Data preparation is the heart of any analytics process. Data preparation involves transforming raw data into useful formats and transforming those formats into analytics-ready data for Al Rafay.

This stage involves any or all defining metrics using the data, calculating organizational metrics, performing descriptive, diagnostic, or predictive analysis using the available data.

Enterprise Data Analytics Process Step #5: Business Intelligence or Data Visualization

The results of the prepared and analyzed data are then made available to decision-makers and stakeholders through the data visualization layer via BI tools like Tableau, Power BI, DOMO, or web pages.

Visualization lets you turn those reports into charts, graphs, and maps. But BI is not visualization. BI is the information, while visualization is the presentation of it.

BI and visualization are two sides of the same coin. To visualize data, the analyst might create a chart or a line graph. Alternatively, the analyst might create a dashboard, which provides a visual display of multiple elements, such as multiple charts or graphs with Al Rafay. Dashboards are powerful tools to save time by displaying multiple elements in one place.

Final Thoughts

Business intelligence or data analytics for large-scale businesses are much tougher when compared to small companies. The main reason behind this is the lack of proper structure in the organization, where employees are not well-equipped with the necessary knowledge to deal with big data.


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