“More firms will adopt Amazon EC2 or EMR or Google App Engine platforms for data analytics. Put in a credit card, by an hour or months worth of compute and storage data. Charge for what you use. No sign up period or fee. Ability to fire up complex analytic systems. Can be a small or large player” Ravi Kalakota’s forecast
Big data Analytics = Technologies and techniques for working productively with data, at any scale.
Analytics-as-a-Service is cloud based… Elastic and highly scalable, No upfront capital expense. Only pay for what you use, Available on-demand
The combination of the two is the emerging new trend. Why? Many organizations are starting to think about “analytics-as-a-service” as they struggle to cope with the problem of analyzing massive amounts of data to find patterns, extract signals from background noise and make predictions. In our discussions with CIOs and others, we are increasingly talking about leveraging the private or public cloud computing to build an analytics-as-a-service model.
Analytics-as-a-Service is an umbrella term I am using to encapsulate “Data-as-a-Service” and “Hadoop-as-a-Service” strategies. It is more sexy 🙂
The strategic goal is to harness data to drive insights and better decisions faster than competition as a core competency. Executing this goal requires developing state-of-the-art capabilities around three facets: algorithms, platform building blocks, and infrastructure.
Analytics is moving out of the IT function and into business — marketing, research and development, into strategy. As result of this shift, the focus is greater on speed-to-insight than on common or low-cost platforms. In most IT organizations it takes anywhere from 6 weeks to 6 months to procure and configure servers. Then another several months to load, configure and test software. Not very fast for a business user who needs to churn data and test hypothesis. Hence cloud-as-a-analytics alternative is gaining traction with business users.
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:
However, it took until 1980s when decision support systems (DSS) became popular and mid 1990s for BI started to emerge as an umbrella term to cover software-enabled innovations in performance management, planning, reporting, querying, analytics, online analytical processing, integration with operational systems, predictive analytics and related areas.
Gartner 2014 magic quadrant shows the key players in the BI market. The different players are differentiated based on five abilities— ability to handle large volumes of data, ability to deal with data velocity, variety (structured and unstructured), visualization capabilities and domain/vertical specific accelerators.
Analytics is becoming three different markets. First of all, there is the BI market which is actually going through quite a bit of change itself. This is a more consolidated market than we have seen in the past and there is a tremendous amount of work being done by Oracle, SAP, IBM and others to kind of retool it for the next generation of BI. So it is a growing market, lots of upgrade, replatform, modernization demand, lots of clients who are finally realizing that the tools (visualization etc.) are ready to give them some of the capability that they have historically cared about.
The second part of the market is what is called Advanced Analytics. Here you need PhD level data scientists who have backgrounds in machine learning, industry specific domain modeling, and different types of data science who can apply that in a very specific way to specific industry problems. This is a rapidly growing part of IT Services. Also, there are just not enough data scientists to go around.
The third part of the market is Analytics as a Service. This is about leveraging software-as-a-service platforms as opposed to on-premise. This is about a business model that is more like Business Process Outsourcing (BPO). Clients buy business outcomes; they don’t buy transactions and FTEs.
The analytics market has thousands of boutique consultants who are specialists in particular industries or specific technologies. It includes all the major technology providers, who are all trying to advance their business and capabilities that they are bringing to the market. And then there are vendors who are just bringing sheer capacity of data science skills to the market and they are coming in from a completely different angle of basically just renting the expertise of their data scientists into the market.
The market is incredibly fragmented. We are in the early stages of growth in the market. Every single one of our clients is building this capability internally and they are looking for more services from vendors, because the opportunity to apply analytics is in every single one function whether it is a customer analytics, industrial Internet, e-commerce platform, is growing. Analytics is embedded into literally every single business interaction.
BI, Analytics [and Big Data] Market Sizing
More recently to support a new generation of cost cutting and growth initiatives, corporations are investing heavily to gain near real-time actionable insights (historical and predictive), and from a mix of disparate spreadsheets and myriad of systems (legacy, internal silos, customer facing, suppliers, partners, etc.).