Business Insights

Analytics propel your business forward, giving you greater understanding of your products and customers in real time

Most organizations are inherently reactive in how they analyze their data. The process of Extract, Transform and Load (or Extract, Load and then Transform) into your enterprise data lakes and warehouses means that data summarizations and visualizations are generally just presenting hindsight better.

What organizations need, however, is to skew their teams to focus on building a better foresight. In reality though, analytical teams spend almost entirety of their time on governing, securing, arranging and reporting on enterprise data. And as data and technology scales, an even lesser amount of time and effort is available to focus on predicting the future from existing data.

At moment4, we work with our clients to automate tasks associated with data governance and aggregation, freeing up valuable resources to spend their time on extracting higher value from their organizations data assets.

Thinking of monetizing your data assets? Are your analytical platforms in a position to help you capitalize on new digital opportunities?

Ever feel like your analytics focus on summarizing and visualizing what you already know? How well does your data inform you about where you are headed?

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Spotlight Solutions

Data Warehouse

Choosing the right architecture, database(s) and technology stack is an absolute necessity to avoid rework. The right Data warehousing solutions will help aggregate and house information from enterprise resources to facilitate analysis in real-time and at scale.

Data Lake

Data lakes are central repositories that efficiently store and aggregate heavy volumes of data independent of format, sources, structures into a single repository. A strategically configured data lake puts all of that in one accessible, efficient and scalable repository enabling broad data exploration and analytics on all available data.

Data Engineering

Have automated repetitive execution of long-standing data preparation and ingestion tasks while allowing users to define custom success or failure criteria. Process your datasets and build business-critical pipelines consistently and reliably using the cloud data engineering tools and engines of your choice, such as Apache Hadoop, Hive, Spark, Presto, Airflow, or others.

Data Science

Build algorithms that use real-world data to help businesses understand the most probable outcome of a given action and empower executives to take a more proactive, data- driven approach to business strategy and decision making. Analytics applications should be able to simulate how variables may affect outcomes, providing guidance on the best course of action for your company.

Data Applications

Equip users to access, analyze, and act on integrated information through analytic application consisting of a series of logically integrated and interactive reports and dashboards. Analyzing the different data streams and collecting previously unavailable data to build applications which can analyse it in real-time to simplify business decisions.

Data Monetization

Automated process of collecting, enriching, transforming, and deriving insights from data is crucial to maximize organization’s monetization potential. Additionally new data collection techniques could be adopted to build future use cases.