Azure Data Lake

Data is gold and every enterprise wants to monetize it. However, how to harness the power of data is still the biggest challenge. Some of the challenges are building infrastructure to store the data, identifying use cases, acquiring data sources, hiring skilled professional who can mine the data, and domain expertise to drive insights. Microsoft has recently released Azure Data Lake service on Azure Cloud Platform which addressed data storage and management problem by offering Big Data as Service. In this paper, we will discuss how Azure Data Lake can enable enterprise to accelerate Big Data adoption.

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Ecommerce Analytics

Ecommerce is a fast-moving and dynamic system. It is much different than regular commerce systems. The customers in ecommerce system have very small barrier to exit. It’s just one click away. On the other hand, once a customer enters a regular store various marketing methods (including visualization, touch, smell etc.) could be utilized to keep him engaged in the store.

It is very important in ecommerce to keep the customer engaged and interested. The statistics on ecommerce show that there is a big demand and large payoff for personalizing the offerings each of your customers gets when they visit your site.

• 59% of online shoppers believe that it is easier to find more interesting products on a personalized online retail store
• 56% are more likely to return to a site that recommends products
• 53% believe that retailers who personalize the shopping experience provide a valuable service
• 45% are more likely to shop on a site that offers personalized recommendations

Personalization extends beyond remembering the customer’s last purchase and showing recommendation based on their last purchase. The organization needs to use all the data that is available about the customer (social media interaction, offline purchase patterns, marketing campaign response, survey response, geographic information, public records, weather data etc.) and create a compelling experience for the customer that engages them and encourages them to take action and visit again.

This is a challenge in organizations where the legacy systems could not handle the volume and variety of the data that needs to be analyzed to create personalized recommendations for the customer. We have specialized in using Big data technology to handle precisely the same problem.

This white paper goes thru some of the standard offerings we provide as part of our Big data ecommerce analytics offerings. Apart from the ones listed here we also provide standard consulting engagements customized for the needs of the customer.

Supply chain Analytics

The increasing complexity of supply chain in recent years warrants analytics for supply chain for any company that wants to

• Save organizational cost of maintaining high quality business
• Increase profitability
• Increase client satisfaction by reliable timely and right products
• Increase flexibility of the organization to respond to fast and unexpected change in market demands

We in InnoLitica have deep industry experience, theoretical background, analytical tools and solid framework to help you succeed in your supply chain analytics and optimization opportunities.

We use an optimized framework for all our supply chain analytics processes…

Multi-channel Attribution Modelling using Big Data and

Hadoop

Digital and ecommerce is a very fast moving and dynamic system. Things could change in matter of hours. A tweet, a facebook update or a YouTube video can start a viral event in matter of hours. It’s very important for executive to stay on top of happenings.

Multiple events (promotions, coupons, marketing campaign, product launch, competitor action etc.) influence sales. It is important for an organization to accurately measure these influences. When these influences are modelled and tracked, the corporation can use them to increase the positive influences and reduce or eliminate the negative influences.

In this case study we demonstrate how Big data technology is used, to quickly analyze large volume of online activity (clickstream) data with large volume of off line sales (point of sales) data to gain business insights in an iterative manner.

Big data technology not only enable an organization to analyze large volume of data, but can also enable organization to analyze variety of data such as structured (sales, inventory), semi-structured (clickstream, log files) and unstructured (text, email, surveys) data. This capability creates unique competitive advantage for an organization in this digital age where information is abundant.

Apart from multi-channel attribution modelling, other types of analysis could also be performed on these types of dataset that includes; site optimization, product recommendation, campaign and promotion uplift models. These results are used as decisions support for marketing campaigns, and marketing mix model optimization.

Alerts can also be built on top of this analysis that can trigger email, text based notification to all the stakeholders when the result violates any pre-determined range.

High Performance Computing for Genealogy Calculation in

Pharmaceutical Industry using Big Data and Hadoop

Pharmaceutical manufacturing is a highly regulated industry. In this industry, traceability and genealogy are very critical to the manufacturing process. For best in class companies, automated, accurate, and timely traceability and genealogy calculations are critical.

One very large pharmaceutical client automated and implemented genealogy calculations for one of their products. These calculations are performed on a weekly basis, are complex in nature, and are a source of major performance and scalability concerns. This analysis uses distributed computing (using Hadoop and Pig UDF) to address the scalability and performance issues for genealogy calculations.