About Data Cleansing in Analytics and Business Intelligence
About Data Cleansing in Analytics and Business Intelligence – As corporate IT systems keep generating a massive amount of data every day, the influence of mismanaged data is rising more than ever across business functions and domains. Not only can bad or erroneous data impact decision-making, but it can also have severe and irreversible long-term consequences in other areas. Data is now all-pervasive, and businesses that do not practice robust data quality management must prepare to miss out on growth and innovation opportunities.
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About Data Cleansing in Analytics and Business Intelligence – Cleaner Data for Stronger BI:
Without adequate data visualization, all attempts to achieve business intelligence are sure to fail. Towards creating healthy BI and Analytics ecosystems, businesses must first focus on overcoming fundamental challenges that relates to data management:
- Eliminate uncertainties related to data residences like storage, security, and accessibility.
- Create efficient data pipelines.
- Create efficient data export/import and integration models.
- Create effective matching and profiling for data credibility.
Once the groundwork is improved, BI and analytics can work their magic and allow enterprises to do more with their business data. Here are some of the main aids of clean data to BI and Analytics.
About Data Cleansing in Analytics and Business Intelligence – 1. Digital Transformation Success
Clean data helps enterprises succeed in their digital transformation journey. Be it salesforce, or cloud computing or BI, data is at the very center of every new-age IT solution. Clean data ensures a smooth digital transformation and makes the journey cost-effective and goal-driven.
About Data Cleansing in Analytics and Business Intelligence – 2. Stronger And More Effective Collaborations
One of the prerequisites for clean data is a robust data governance policy. With good data governance, businesses can efficiently manage data across multiple cross-functions. It paves the way for more vital and compelling collaborations, improving workforce productivity and workflow efficiencies.
3. Accurate Exploratory Data Analysis (Eda)
EDA is now a fundamental aspect of data-driven decision-making. It involves early-stage analysis of business data to unlock hidden patterns, identify anomalies, and test the new hypothesis using summary stats and graphs. Data cleansing makes it easier to identify and remove data errors and inconsistencies. Subsequently, it ensures EDA accuracy and effectiveness.
4. Boosts Revenue
Clean data unlocks new opportunities while cementing existing customer relationships. More than 90% of enterprises say they have missed significant opportunities due to below-par customer data management policies. Clean data while boosting new revenue opportunities also optimize legacy workflows and processes for lower operational costs.
Geographical boundaries do not limit digital business. Today, serving customers in the world’s remotest corners is more accessible and affordable. But digital businesses need to be data compliant as data policies change consistently with geographical boundaries. Companies find it challenging to adhere to the varying data compliance policies without a proper data management policy. With clean data, businesses can create data ecosystems that align with compliance policies specific to different demographics.
About Data Cleansing in Analytics and Business Intelligence – Charles Babbage, the father of the computer, said, “Mistakes using insufficient data are much less than those consuming no data at all.” However, unlike in the past, computing power today has reached its true zenith, and businesses must use everything within their capacity to do more with their data. With Pratham’s data engineering services, enterprises can overcome their data challenges and turn their data into business assets. Contact us today for a qualified and experienced team of data engineers and transform your business data into stimulating business decisions.