Data growth curve: Terabytes -> Petabytes -> Exabytes -> Zettabytes -> Yottabytes -> Brontobytes -> Geopbytes. It is getting more interesting.
Analytical Infrastructure curve: Databases -> Datamarts -> Operational Data Stores (ODS) -> Enterprise Data Warehouses -> Data Appliances -> In-Memory Appliances -> NoSQL Databases -> Hadoop Clusters
In most enterprises, whether it’s a public or private enterprise, there is typically a mountain of data, structured and unstructured data, that contains potential insights about how to serve their customers better, how to engage with customers better and make the processes run more efficiently. Consider this:
- Online firms–including Facebook, Visa, Zynga–use Big Data technologies like Hadoop to analyze massive amounts of business transactions, machine generated and application data.
- Wall street investment banks, hedge funds, algorithmic and low latency traders are leveraging data appliances such as EMC Greenplum hardware with Hadoop software to do advanced analytics in a “massively scalable” architecture
- Retailers use HP Vertica or Cloudera analyze massive amounts of data simply, quickly and reliably, resulting in “just-in-time” business intelligence.
- New public and private “data cloud” software startups capable of handling petascale problems are emerging to create a new category – Cloudera, Hortonworks, Northscale, Splunk, Palantir, Factual, Datameer, Aster Data, TellApart.
Data is seen as a resource that can be extracted and refined and turned into something powerful. It takes a certain amount of computing power to analyze the data and pull out and use those insights. That where the new tools like Hadoop, NoSQL, In-memory analytics and other enablers come in.
What business problems are being targeted?
Why are some companies in retail, insurance, financial services and healthcare racing to position themselves in Big Data, in-memory data clouds while others don’t seem to care?
World-class companies are targeting a new set of business problems that were hard to solve before – Modeling true risk, customer churn analysis, flexible supply chains, loyalty pricing, recommendation engines, ad targeting, precision targeting, PoS transaction analysis, threat analysis, trade surveillance, search quality fine tuning, and mashups such as location + ad targeting.
To address these petascale problems an elastic/adaptive infrastructure for data warehousing and analytics capable of three things is converging:
- ability to analyze transactional, structured and unstructured data on a single platform
- low-latency in-memory or Solid State Devices (SSD) for super high volume web and real-time apps
- Scale out with low cost commodity hardware; distribute processing and workloads
As a result, a new BI and Analytics framework is emerging to support public and private cloud deployments.
The “Raw Data -> Aggregated Data -> Intelligence -> Insights -> Decisions” is a differentiating causal chain in business today. To service this “data->decision” chain a very large industry is emerging.
The Business Intelligence, Performance Management and Data Analytics is a large confusing software category with multiple sub-categories — mega-vendors (full stack, niche vendors, data discovery, visualization, data appliances, Open Source, Cloud – SaaS, Data Integration, Data Quality, Mobile BI, Services and Custom Analytics).
But the interest in BI and analytics is surging. Arnab Gupta, CEO of Opera states why analytics are taking center stage, “We live in a world where computers, not people, are in the driver’s seat. In banking, virtually 100% of the credit decisions are made by machines. In marketing, advanced algorithms determine messages, sales channels, and products for each consumer. Online, more and more volume is spurred by sophisticated recommender engines. At Amazon.com, 40% of business comes from its “other people like you bought…” program.” (Businessweek, September 29, 2009).
Here is a list of vendors who participate in this marketspace: