We explore and reveal hidden opportunities with unstructured data that can leverage radical improvements for businesses. With a profound experience within various vertical markets, technology excellence and a passion for innovation, our experts can help you identify a successful data strategy for your business growth.
In order to survive in a highly competitive global marketplace and drive business growth, leading companies utilize innovative approaches to assist in effective decision-making. In many industries, including the retail, banking and insurance sectors, collecting and analyzing consumer behavior data helps define a winning marketing strategy.
ELEKS uses quantitative methods to garner useful insights from raw data, and then applies those insights to specific decision-making. With versatile domain knowledge that includes finance, insurance/reinsurance, banking and retail industries, we have a strong understanding of the business solutions our customers need to succeed. Our experts in statistics and machine learning build complex predictive models that can evaluate and visualize company performance. Additionally, all the analytics project activities are performed according to the CRISP-DM methodology.
In previous years, users who wanted to store and analyze data would store it in a database and process it via SQL queries. However, the Web has changed most of the assumptions of this era. Now, the data is both unstructured and large, and the databases can neither capture the data into a schema nor scale it to store and process it.
Hadoop MapReduce, together with the supportive set of projects, makes a good framework choice to process large datasets and perform ETL-type operations. We are currently exploring how to use Hadoop and its streaming capabilities to efficiently perform data storing, processing and analysis. In addition, we are building models that can perform massive text analysis using advanced solutions - such as Apache Mahout and Spark.
A recent advancement in machine learning algorithms called “Deep Learning” provides the ability to learn about the hierarchies of a feature in an unsupervised manner using unlabeled data.
We are currently researching how to apply Deep Learning techniques beyond computer vision to address business challenges in various industries. These cutting-edge techniques are allowing companies to gain an additional competitive advantage and excel in their respective markets. Increasing the relevance of information, targeting your customers directly or reducing the number of false cases in fraud detection systems, can save significant costs and in turn, allow businesses to invest in more strategic activities.