Extracting useful insights from data required good business understanding and the ability to drive conclusions from an array of numbers and values. There are some challenges associated with creating an efficient data analytics process: 

1. When the data becomes big data. It’s hard for excel to handle and there will be a need for more specialized tools.
2. When the data is noisy and needs consistent data cleaning. Automating the cleaning is essential. 
3. When the data comes from multiple sources and needs to be merged together in a meaningful way.
4. Shortage in skills: if the team has limited skills in producing data analytics at scale. 
5. Data privacy and security: When the data is sensitive and needs to be anonymized or aggregated before showing insights.
6. Lack of knowledge on other open-source datasets that can multiply the value of your data.
7. When there is a need to automate the process of insights extraction 

Those challenges along with others make the process of extracting meaningful analytics difficult for nonspecialists and harnessing the full potential of their company’s data to increase revenue, cut down costs or find new business opportunities. This service includes: 

• Understanding the business, the data, and the key operations. 
• Assessing existing data storage and creating data quality assessment reports.
• Listing and confirming the questions that need to be answered from the data.
• Creating charts, tables, and diagrams that answer and explain different metrics from the data and adding text conclusions and explanations.  
• Drafting executive-level slides with the output of the analysis. 
• Creating documents such as e-books or short articles on the findings from the data either for internal use or external use.  
• Automating the process of data cleaning and insights extraction.