Business Analytics as a Service
In the present age, organisations offer similar products and services using comparable technology and geographical advantages, resulting in a crowded and highly competitive marketplace. All this has brought more focus on being able to execute business with increasing efficiency and effectiveness and make smarter timely business decisions, with the use of business analytics. With an aim of providing our customers with distinct business advantage, we at Right Steps Consultancy provide Business Analytics as a Service.
We have a pool of highly skilled and trained data scientists who use mathematical and statistical modelling techniques along with data mining processes to enable our customers to unearth valuable insights from their data. We adopt a consultative and agile approach to build an open source and advanced Excel-based analytics solutions.
Our Approach to Analytics
Analytics Process Maturity Assessment
Identify Problem Domain
Identify Parameters of Success
Identify Outcomes & Decision Variables
Data Cleansing & Transformation
Create Analysis Model
Validate & Test Model
Update Business Processes & Decision Models
Measure Process Performance
The above approach to build a sophisticated analytics capability for any organisation.
We start our engagement with a maturity assessment of the organisation's current analytics capability. This provides us an insight into the organisations current strengths and weaknesses and helps us design and implement an appropriate analytics processes. It is proposed that such an assessment should be performed once every 2-3 years to ensure that the organisation, by employing the most up-to-date tools and techniques, is able to use analytics for a competitive business advantage.
Next, based on the current metrics and process assessment, we will focus on defining the problem domain and measures used by the business to define success.
Based on the problem definition we will identify the appropriate data sources that need to be analysed and identify the most appropriate outcome(s) that will address the problem domain along with the decision variables that influence the outcome.
We will then prepare the data by undertaking data exploration, transformation & cleansing activities using advanced techniques like Principal Component Analysis, Feature Analysis and Cluster Analysis.
Once we have prepared the data we will start applying data mining and predictive analytical methods (e.g. Affinity Analysis, Classification, Regression, and Artificial Neural Network) to build analytical models for each outcome. Once a model is validated and tested we focus on deriving insights and the use of appropriate data visualisation techniques to present the results to the business stakeholders.
The derived insights then need to be incorporated into the organisation's business processes. Finally additional measures will need to be recorded and tracked to evaluate the performance of the model. These measures along with additional data will be used to further improve the model or generate new models to ensure continuous and sustained improvement/growth.