Data Analytics

Since information is scattered in various forms and the data is huge, Industries can’t make decisions based on the scattered data and since most of the reports are based on transactions. Tools that are being used are not using the right dimensions to capture the data and enterprise data is left unused.

Innowids offer smarter solutions where top management can see more details and take faster descession. The solutions provided by Innowids helps the management to improve their cash statement, controlled expenses, manage head count and employee performance, stream line spend and supply chain operations and track the financial performance of major projects. Innowids uses latest tools & technologies like OTBI, DWBI, ETL, Predictive & Prescriptive Analytics which provides the customers accurate reporting to make decisions.

At Innowids we have the below expertise to provide solutions to various analytics (On Premise & Cloud) offers broad suite of capabilities for finance and customer satisfaction.

  • Oracle Business Analytics
  • Microsoft Repo

Machine Learning

Data is been scattered in an un orthodox manner where it does not help in descession making by the top management.

Machine Learning is the Study of algorithms that improve their performance at some task with experience. Machine learning Optimize a performance criterion using example data or past experience by way of Predictive Analaysis & Data Mining. Role of Computer science in Efficient algorithms to Solve the optimization problem & Representing and evaluating the model for inference. Machine learning is used for Data Analysis that automates model building and can help make decessions without human intervention.

Steps involved in Processing Data
  • Collect Data
  • Process Data
  • Clean Data
  • Train
  • Build ML Models
  • Test
  • Data Visualization
  • Make Decisions
  • Build Data Product

Machine learning algorithms can best be understood through the lens of the bias-variance trade-off.

Bias are the simplifying assumptions made by a model to make the target function easier to learn. Generally, parametric algorithms have a high bias making them fast to learn and easier to understand but generally less flexible. In turn, they have lower predictive performance on complex problems that fail to meet the simplifying assumptions of the algorithms bias. Decision trees are an example of a low bias algorithm, whereas linear regression is an example of a high-bias algorithm.

Variance is the amount that the estimate of the target function will change if different training data was used. The target function is estimated from the training data by a machine learning algorithm, so we should expect the algorithm to have some variance, not zero variance. The k-Nearest Neighbours algorithm is an example of a high-variance algorithm, whereas Linear Discriminant Analysis is an example of a low variance algorithm.

Modern AI: Applications

Some of the modern AI applications built using Machine Learning.

  • Finance, Banking
  • Retail, eCommerce
  • Travel, Hospitality
  • Marketing, Sales
  • HealthCare, LS
  • Media, Entertainment

As a consulting organization, Innowids consultants have strong domain expertise providing full life cycle implementation. We have experienced consultants in consulting Data Analytics and Data Science. Innowids consultants come from the Industry experience and are highly experienced in analysing the data using the language R for Machine learning. Innowids can help various Industry in reducing data redundancy, decision making and meet their challenges.