5 BEST PRACTICES FOR ENHANCED USE OF DATA ANALYTICS 

data analytics

According to a report by Forrester Consulting, data-driven companies are 58% more likely to beat revenue goals than those who are not focused on data. A study by Forbes gives more context into this. It says that after analyzing 100 million subscribers, Netflix influenced 80% of the content viewed thanks to accurate data insights. 

Data analytics is the process of deriving valuable insights from a disarrayed set of data. Data analytics strategy is used by businesses in creating targeted content, product development, and to improve the efficiency of their operations. In short, it helps businesses in enhancing their business intelligence.  

BEST DATA ANALYTICS PRACTICES TO FOLLOW FOR YOUR BUSINESS 

Simplify access to data:More the data, better the predictions made. The adage, bigger the better is especially true when it comes to how much data is available for your business analysts and data scientists. Having access to more data makes the task of determining the data that predicts an outcome easier. 

  • Using advanced analytic techniques: You can use advanced analytics techniques like the following: 
  1. Frequency analysis. 
  2. Correlation.  
  3. Summary statistics. 
  • Shape data using adaptable processing methods: In order to prepare data for analytics, it is necessary to combine, transform, de-normalize, and occasionally aggregate your data from several sources into one enormous database. It is necessary to have a logical, graphical interface that makes data transposition simpler.  
  • Share metadata between data management and analytics domains: A shared metadata layer allows you to repeat your data preparation procedures in a consistent manner. It encourages collaboration, offers lineage information on the data preparation procedure, and facilitates model deployment. Better productivity, more accurate models, quicker cycle times, greater flexibility, and auditable, transparent data are the other benefits of sharing metadata across data management and analytics domains. 
  • Scrub data to improve the quality of the processes: Poor data can cause strategic processes to fail. Having a data quality platform enables you to integrate data cleansing directly into your data integration cycle. According to the analytical technique you’re applying, scrubbing also eliminates erroneous data and enriches data using binning (that is, grouping together data that was originally in smaller intervals). 

Conclusion 

There is an increase in the diversity of data sets. Along with this, the pressure to generate meaningful conclusions from the appropriate data at the right time is also real. Due to this reason, business leaders must incorporate data analytics into their overarching data management strategy. Skillmine’s data visualization and analytics solution DATA V enables businesses to integrate internal and external datasets into their daily routine, thereby shortening the time to generate insights. It arranges data in a structured format and adds a logical layer to it.

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