RevSaks does assist clients to uncover strategic opportunities from the heaps of internal as well as external data. Focus is on solutions evaluation, enhancing e as well as integrating data to transform it into actionable insights.
RevSaks expertise lies in:
- End-to-End Capabilities from Process Analysis, KPI identification to of course Development
- Experience in a Full Range of Tools/Products that do Include: SAP suite, Cognos, Oracle, Qlikview, as well as Microsoft
- Strong Knowledge of applying Data Warehousing cum Data Modelling to solve complex data challenges
- Dedicated team of Certified BI Consultants
- Experience in providing Analytical as well as Predictive modelling expertise with the use of tools such as SAS and R Programming
Analytics happens to be the systematic computational analysis of data or statistics. It is made use of for the discovery, interpretation, as well as communication of meaningful patterns in data. It also does involve in applying data patterns towards in effect decision-making. It can also be valuable in areas rich with recorded information; analytics do rely on the simultaneous application of statistics, computer programming as well as operations research to quantify performance.
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Collect Data
Data collection does appear different for every organization. With today’s technology, organizations can gather both structured as well as unstructured data from a variety of sources — from cloud storage to mobile applications to nearly in-store IoT sensors as well as beyond. Some data can be stored in data warehouses where business intelligence tools cum solutions can access it easily.
Process Data
After data is collected as well as stored, it must be systematized properly to get accurate results on analytical queries, especially when it is large as well as unstructured. Available data is rather increasing at a fast pace thus making data processing a challenge for organizations. One processing option can be batch processing, which does appear at large data blocks over time. Batch processing is rather beneficial when there is a longer turnaround time between collecting as well as analyzing data. Stream processing does appear at small batches of data at a particular go, thus shortening the delay time between collection and analysis for quicker decision-making. Stream processing is more complex and often more expensive.
Clean Data
Data big or small does require scrubbing to improve data quality and also get stronger results; all data must be formatted correctly, and any sort of duplicative or irrelevant data must be eliminated or accounted for. Dirty data turn out to be obscure and misleading, creating flawed insights.
Analyze Data
Getting big data into a usable state does take time. Once it is ready, advanced analytics processes then can turn big data into big insights. Some of these big data analysis methods do include:
Data mining sorts via large datasets to identify patterns as well as relationships by identifying anomalies and also creating data clusters.
Predictive analytics does use an organization’s historical data to make predictions, identifying upcoming risks s well as opportunities.
Deep learning imitates human learning patterns by making use of artificial intelligence cum machine learning to layer algorithms and also helps find patterns in the most complex as well as abstract data.
Benefits & Advantages of Big Data Analytics
1. The process of converting large amounts of unstructured raw data can be retrieved from different sources to a data product made useful for organizations forming the core of Big Data Analytics.
2. Quicker as well as Better Decision Making within Organization
3. Big data analytics’ speed and efficiency are the highlights of ensuring that big data becomes a sensation in recent times. Earlier, businesses would rather take months to make a strategic decision based on data analytics.
4. Improvement of Customer Experience. Big Data analysis benefits in improving customer experiences.
5. Big data happens to be an extremely large volume of data and datasets that tend to come in diverse forms and also from multiple sources. Several organizations do recognize the advantages of collecting as much data as possible.
Nevertheless, it is not simply sufficient to collect and store big data—efforts need to be made to put it to use. With quickly growing technology, organizations can indeed use big data analytics to transform terabytes of data into actionable insights.