The goal of these appliances (engineered systems) is to help IT groups further shrink data center costs, increase system utilization and enable better application integration. All goals that CIOs everywhere continue to struggle with. CIOs now face an interesting decision matrix: Exalytics/Logic/Data systems versus traditional build from components versus hosted.
With ExaSystems, Oracle has a tremendous market advantage. Oracle owns most of the software that enterprises need today. Via acquisitions, Oracle owns the whole stack! Web tier, Middleware, Database software, Database tier, Storage tier. With Sun Microsystems it’s ideally positioned to maximize the platform capabilities. It’s easy for Oracle make its own software play nice on the Exalytics, Exalogic and Exadata platforms.
Everyone is beginning to look beyond the status quo in BI, analytics, Big Data, Cloud Computing etc to fundamentally change how they discover fresh insights, how they can make smarter decisions, profit from customer intelligence and social media, and optimize performance management.
The headache for corporations is not the technology aspects but the leadership side. Who is going to lead this effort, corral the vendors and formalize and execute a more structured program.
Who is going to lead the effort to create the right toolset, dataset, skillset and mindset necessary for success?
As BI and Analytics moves from “experiment and test” lab projects to commercial deployments, companies are going to need more leadership and program management capabilities. They need leadership that can provide strategic, expert guidance for using powerful new technologies to find patterns and correlations in data transactions, event streams, and social media.
Some firms are making moves. In insurance, AIG – Chartis Inc. unit appointed Murli Buluswar to the new post of chief science officer. This aims to enhance Chartis’ focus on analytics… he “will be responsible for establishing a world-class R&D function to help improve Chartis’ global commercial and consumer business strategies and to deliver more value for customers.” This focus on analytics involves “asking the right questions and making science-driven decisions about strategies—whether it’s related to underwriting decisions, product innovation, pricing, distribution, marketing, claims or customer experience—with the end result of improving the scope of what Chartis delivers for customers”.
As a result of where we are in the maturity cycle and to support the business units better, we are seeing a new emerging role “CIO – BI” that is dotted lined to the global CIO or a shared services leader. Let’s look at a representative job posting from GE Capital, which always seems to be a step ahead of most companies. Read more
BI is key to enabling companies to turn oceans of data into predictive models and actionable decisions. However, a survey of 353 executives in large companies, reported that their chief BI concern was the performance of various BI solutions.
Development, support and enhancement teams are typically deployed to address BI performance challenges with varied success. But most companies don’t have a dedicated focus on performance.
A BI Center of Excellence (BI CoE) measured by performance KPIs and service metrics is one solution to this problem. This is not an area that traditionally draws high-level attention or is featured in a dedicated CoE initiative, yet in the right circumstances it offers unique value. Read more
“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 financial crisis of 2007–2011 is driving widespread changes in the U.S regulatory system. Dodd-Frank Act addresses “too big to fail” problem by tightening capital requirements and supervision of large financial firms and hedge funds. It also creates an “orderly liquidation authority” so the government can wind down a failing institution without market chaos.
Financial institutions will be spending billions to strengthen, streamline and automate their recordkeeping, risk management KPIs and dashboard systems. The implications on Data Retention and Archiving, Disaster Recovery and Continuity Planning have been well covered. But leveraging Business Analytics to proactively and reactively manage/monitor risk and compliance is an emerging frontier.
We believe that Business Analytics and real-time data management are poised to play a huge role in regulating the next generation of risk and compliance management in Financial Services industry (FSI). in this posting, we are going to examine the strategic and structural challenges, the dashboards and KPIs of interest that provide feedback, and what an effective execution roadmap needs to be for every organization.
In the ancient Indian parable of the elephant, six blind men touch an elephant and report six very different views of the same animal. Compare this scenario to a data warehouse that is getting data from six different sources. “Harry Potter and the Sorcerer’s Stone” as a field in a database can be written as “HP and the Sorcerer’s Stone” or as “Harry Potter I” or simply – “Sorcerer’s Stone”. In the data warehouse these are four separate movie titles. For a Harry Potter fan, they are the same movie. Now increase the number of movies to cover the entire Harry Potter series and further include fifty languages. You now have a set of titles which may perplex even a real Harry Potter aficionado.
What does this have to do with data analytics?
Data is moving from something you use outside the workstream (support-mode) to becoming a part of the business app itself. The growing challenge in corporations is how to organize for “data as a platform.” What is the right organizational structure that will help monetize data?
John Wanamaker, considered a pioneer in modern advertising, said: “Half the money I spend on advertising is wasted; the problem is I don’t know which half.” Today, we can say the same of enterprise investment in business intelligence (BI), analytics, and big data.
Even after doing their best for over 20 years to build centralized, scalable information architecture, I found that only a small percentage of organizations’ data is actually converted to useful information in time to leverage it for better insight and decisions.
At both strategic and tactical levels, much of this gap can be explained by the fundamental disconnect in goals, objectives, priorities, and methods between IT professionals and the business users they should ideally serve.
The other challenge facing leadership is the rapid evolution of the data platform (see below.) How do you create strategies that adapt to a changing landscape?
How do you become a world-class data-driven firm? What portfolio of projects do you execute to mature the capabilities?
If you’re an executive, manager, or team leader, one of your toughest responsibilities is managing and organizing your BI, Reporting or Analytics initiative. While the nuances – skillsets, toolsets and datasets — are different for each initiative, the fundamentals of managing, organizing and structuring are pretty much the same.
Almost every Fortune 1000 company’s management is increasingly focused on monetizing small data, big data or fast data, and how to gain a real-time competitive edge from their information. How can firms achieve positive returns on their analytic investments by taking advantage of the growing amounts of data?
So what’s the right organizational model that will help them achieve the “ten second advantage”? Competency Centers, Centers of excellence (CoE) or Shared Services models are execution models to enable the corporate or strategic vision to create an enterprise that uses data and analytics for business value.
The goal of every World-class CoE is the same – enable the right combination of toolsets, skillsets, mindsets and datasets for better, faster, cheaper and more repeatable analytics, reporting or platform development.
Evolution of BI/Reporting/Analytics
- Data is Growing Faster than Budgets
- Demand is Growing, Speed to Insight is Crucial
- Modifying large, existing applications is NOT the path forward.
- Skills are lagging.. New tooling
As a result, Enterprise BI and Analytics strategies need to evolve. The evolution tends to happen in 3 phases:
- Department Solutions – Many companies deploy Analytics (and BI) applications as departmental solutions, and in the process, accumulate a large collection of disparate BI technologies – SAP Business Objects, IBM Cognos, Microstrategy, Oracle OBIEE, Microsoft, Qlikview, Tableau, Spotfire etc. – as a result. Each distinct technology supported a specific user population and database, within a well-defined “island of analytics.” At first, these dept islands satisfied the initial needs of the business, but early success in departmental deployment sowed the seeds for new problems as the applications grew.
- Successful applications and platforms always expand. The second phase of Analytics (and BI) is where there is tremendous growth and platform solutions are longer isolated islands. Instead, they overlap in user populations, data access, and analytic coverage. As a result, organizations are now faced with an untenable situation. The enterprise is getting conflicting versions of the truth through the multiple disparate BI systems, and there is no way to harmonize them without an extraordinary ongoing manual effort of synchronization, validation and quality checks. Equally problematic is the fact that business users are forced to use many different BI tools depending on what data they want.
- The third phase of Analytics (and BI) is one where the executives had enough. They simply make a decision to rationalize to a single platform or a centralized model that is sold as a “magic nirvana” solution…delivers one version of the truth (golden source of data) to all people across the enterprise. It can access all of the data, administer all of the people, eliminate repetitive data access, reduce the administrative effort, and reduce the time to deploy new BI applications.
“Time to decisions, scope of decisions, disconnected toolsets and cost of decisions” is deemed unacceptable within & across functional areas. This typically drives a new phase… centralized BI, Reporting or Analytics CoE.
For example, at a Fortune 500 company, costly self-service environment, static reports, departmental solutions and other issues (shown below) forced them to re-think and re-engineer their enterprise BI solution. The firm set new target objectives…(1) Shorter time to insights; (2) Greater leverage for analytics team; (3) Accelerated product innovation and (4) 20% reduction in BI support costs.
While centralization of BI, Reporting and Analytics can enable organizations to reduce their IT delivery costs by up to 40%. However, a failure to align the level of BI, Reporting and Analytics centralization closely to long-term business and IT strategic goals and to manage the transition to centralized delivery carefully can not only erode expected savings from centralization, it can increase the cost of delivering IT services by up to 30-45% compared to a pre-centralization baseline. This where good management can make a big difference.
BI CoE Elements for Faster, Better, Cheaper Execution
BI CoE (could be Analytics CoE, Big Data CoE or Integration CoE) is an organizing mechanism to align People, Process, Technology and Culture. The target benefits include:
- Better collaboration between Business and IT
- Increased adoption and use of BI and Analytics in the lines of business.
- Better data management, quality and reporting
- Cost savings from eliminating redundant functions
CoE elements include:
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.).