No two products are identical, there are many strengths that are designed into each product to suit specific needs within financial markets. As such most of the packages can be recommended to a specific audience based on their strengths. You may want descriptive analytics that look at what happened that provides hindsight to learn from. Diagnostic analytics will help explain why something happened and continued insight can be gained from predictive analytics that show what will happen. For full optimization, prescriptive analytics software will show how to make something happen, giving foresight into the trades. Analytic Superheroes’ data analysis services products include:
- Predictive analytics: These are software and/or hardware solutions that allow firms to discover, evaluate, optimize, and deploy predictive models by analyzing big data to improve business performance or mitigate risk.
- NoSQL databases: These are key-value, document and graph databases.
- Search and knowledge discovery: Tools and technologies that support extraction of information and new insights from unstructured and structured data from multiple sources including file systems, databases, streams, APIs, and other platforms and applications, collectively known as big data.
- Stream analytics: Stream analytics software can filter, aggregate, enrich and analyze big data.
- In-memory data fabric: Provides low-latency access and processing by distributing data across the dynamic random access memory (DRAM), Flash, or SSD of a distributed computer system.
- Distributed file stores: This is a computer network where data is stored on more than one node, often in a replicated fashion, for redundancy and performance.
- Data virtualization: A technology that delivers information from various data sources, including big data sources and distributed data stores in real-time and near-real time.
- Data integration: Tools for data orchestration across solutions such as Amazon Elastic MapReduce (EMR), Apache Hive, Apache Pig, Apache Spark, MapReduce, Couchbase, Hadoop, and MongoDB.
- Data preparation: Software that eases the burden of sourcing, shaping, cleansing, and sharing diverse and messy data sets to accelerate big data usefulness for analytics.
- Data quality: Products that conduct data cleansing and enrichment on large, high-velocity data sets (“big data”), using parallel operations on distributed data stores and databases.
Each of the above technologies are new, but are projected to be significant successes as they mature. Predictive analytics is estimated to fully mature within the next 10 years, meaning it will deliver high business value over the long term. The remaining technologies are expected to take a little longer.
Predictive analytics and NQSL databases have been assessed as providing high business value. Due to some of the technology being at an earlier stage of development stream analytics and in-memory data fabric have a medium business value and the remainder a currently low business value since they have the potential for damage and disruption that is a higher risk than the established, better-known technology.
Data Quality includes data security in addition to other features to ensure that decisions are based on reliable and accurate data. Current efforts surrounding data certification aim to guarantee that data meets expected standards for quality, security and regulatory compliance supporting business decision-making, business performance, and business processes.