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1) Better understanding of customers and prospect
Big data analytics applications gives businesses a complete picture of their customers. What makes them act, what type of products they buy and when, how they interact with businesses, and why do they choose a certain company/product over others.
2) Finding and creating market trends
Identify and track patterns and behaviors tells businesses where their growth is headed, demand and supply is changing over time. The technology removes “instinct” prediction about trends.
3) Monitoring competition and market sentiment
Traditionally, understanding competition moves has been limited to activities like reading business news, pretending to be a customer to get insights into processes, etc. Today, however, you get every information you need about the competition without even leaving the desk.
4) Better business operations
Big data technology is in demand for optimizing business processes and operations. By being incorporated in every data-heavy business operations like production line, customer ordering systems, etc. the technology is being used to define efficiencies, finding anomalies, and highlighting when the process needs improvement.
These benefits of big data analytics are being used heavily across sectors like retail, supply chain, telecom, healthcare, and other similar industries.
5) Tweaking business models for current and future market place
Big data analytics applications have been used by companies to update their current products while coming up with new products and business lines. With a massive set of market data at their disposal, businesses are able to define what their customers are looking for and which businesses are catering to their needs. This information is being used to define new products and business models.
1) Business case evaluation
The Big Data analytics lifecycle begins with a business case, which defines the reason and goal behind the analysis. Our team will perform a thorough understanding session with your management team to better understand your nature of business, and to ascertain your analytics goals.
2) Identification of data
Here, a broad variety of data sources are identified, internally and externally to develop the analytical model/scenarios.
3) Data filtering
All of the identified data from the previous stage is filtered here to remove corrupt data, this is a very critical stage as the accuracy of data will affect the accuracy of insights production.
4) Data extraction
Our team of data engineers will massage data that is not compatible with the tool is extracted and then transformed into a compatible form.
5) Data aggregation
In this stage, data with the same fields across different datasets are integrated. This is to create a relationship between data.
6) Data analysis
Data is evaluated using analytical and statistical tools to discover useful information. Our team of data scientists and industry subject experts will come in to provide insights and discovery to present to our customers for further proofing and assessment.
7) Visualization of data/insights
With our customised bigdata analytics tools, we will present graphic visualizations of the analysis for the ease of understanding to most audience, and to plan action forward.
8) Final analysis result
This is the last step of the Big Data analytics lifecycle, where the final results of the analysis are made available to business stakeholders who will take action.
As business world in constant fast pace of change, our services is to allow customer to have near real time analytics and actionable insights on engagement basis, an Analytics as A Service (ASAS) model. This will eliminate the challenges of investment, resources, time and expertise needed by any company that gearing toward Data-Driven Business Model