The “real meat and potatoes” use cases behind big data actual adoption might be around B2B machine data management and Industrial analytics enabled by wireless, battery-free sensor platforms.
While social, consumer, retail and mobile big data get a lot of PR, the big data business cases around industrial machine data analytics or “things that spin” actually make economic sense. These projects tend to show tangible Return on Investment (ROI).
The concept of Internet-connected machines that collect telemetry data and communicate, often called the “Internet of Things or M2M” has been marketed for several years:
– I.B.M. has its “Smarter Planet” initiative
– Cisco has its “Internet of Everything” initiative
– GE has its “Industrial Internet” initiative.
– Salesforce.com has its “Internet of Customers” theme
To compete with GE….Hitachi, United Technologies, Siemens, Bosch, Schneider Electric, Phillips and other industrial giants are all getting on the band-wagon as the vision of M2M is now viable with advances in microelectronics, wireless communications, and microfabricated (MEMS) sensing enabling platforms of rapidly diminishing size.
The Bosch Group has embarked on a series of initiatives across business units that make use of data and analytics to provide so-called intelligent customer offerings. These include intelligent fleet management, intelligent vehicle-charging infrastructures, intelligent energy management, intelligent security video analysis, and many more. To identify and develop these innovative services, Bosch created a Software Innovations group that focuses heavily on big data, analytics, and the “Internet of Things.”
Similarly, the Schneider Electric focuses primarily on energy management, including energy optimization, smart-grid management, and building automation. Its Advanced Distribution Management System, for example, handles energy distribution in utility companies. ADMS monitors and controls network devices, manages service outages, and dispatches crews. It gives utilities the ability to integrate millions of data points on network performance and lets engineers use analytics to monitor the network.
Industrial Internet – making smart use of sensors, networked machines and data analytics – is the big vision, but the business driver is in no unplanned downtime for customers.
Over the past seven years, we’ve seen a massive regulatory overhaul and an industry-wide push to enhance trust and confidence and encourage investor participation in the financial system.
To roadmap Wall Street regtech priorities, we have been having ongoing meetings with MDs and leading architects in global banks and investment services firms. RegTech (e.g., regulation as a service) is a subset of FinTech. Companies include
- Fintellix offers a data analytics platform allowing banks to convert internal data into regulatory reporting formats
- Suade offers banks “regulation as a service” interpreting real time regulatory knowledge so that banks can better manage and respond to regulation
- Sybenetix combines machine learning with behavioral science to create a compliance and performance tool for traders
No longer business as usual. It is clear that banks are devoting more resources to Know Your Customers (KYC), Anti-Money Laundering (AML), fraud detection and prevention, Office of Foreign Assets Control (OFAC) compliance. FINRA is at the beginning stages of the process for building the Consolidated Audit Trail, or CAT for trading surveillance.
To enable compliance with variety of Risk/Regulatory initiatives, AML and KYC initiatives…the big RegTech related investments are:
- Strengthening the Golden Sources – Security Master, Account Master and Customer Master.
- Standardized, common global business processes, data, systems and quantitative solutions that can be leveraged and executed across geographies, products, and markets to manage delinquency exposures, and efficiently meet Regulatory requirements for Comprehensive Capital Analysis and Review (CCAR), FDIC Reporting, Basel, and Stress Loss Testing.
- Various enterprise data management initiatives – Data Quality, Data Lineage, Data Lifecycle Management, Data Maturity and Enterprise Architecture procedures.
Regulatory reporting improvements via next generation Enterprise Datawarehouses (EDW) (using Oracle, IBM, NoSQL or Hadoop)– Reporting on top of EDW addresses the core problems faced by Finance, Risk and Compliance when these functions extract their own feeds of data from the product systems through which the business is conducted and use differing platforms of associated reference data in support of their reporting processes.
Lot of current investments are in the areas of Finance EDW which delivers common pool of contracts, positions and balances, organized on an enterprise wide basis and completed by anointed “gold” sources of reference data which ensure consistency and integration of information.
Crawl, walk, Run seems to be the execution game-plan as the data complexity is pretty horrendous. Take for instance, Citi alone….has approximately 200 million accounts and business in 160+ countries and jurisdictions. All risk management is made incredibly complex by the numerous banking mergers that took place over the past 3-4 decades.
The type of data challenges global banks like Citigroup, Goldman, Wells Fargo, Bank of America and JP MorganChase are wrestling with include: Read more
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
The growing enterprise adoption of Salesforce SFA/CRM, Workday HR, Netsuite ERP, Oracle on Demand, Force.com for apps and Amazon Web Services for e-commerce will result in more fragmented enterprise data scattered across the cloud.
Automating the moving, monitoring, securing and synchronization of data is no longer a “nice-to-have” but “must-have” capability.
Data quality and integration issues — aggregating data from the myriad sources and services within an organization — are CIOs and IT Architects top concern about SaaS and the main reason they hesitate to adopt it (Data security is another concern). They have seen this hosted data silo and data jungle problem too many times in the past. They know how this movie is likely to unfold.
Developing strategic (data governance), tactical (consistent data integration requirements) or operational (vendor selection) strategies to deal with this emerging “internal-to-cloud” data quality problem is a growing priority in my humble opinion. Otherwise most enterprises are going to get less than optimal value from various SaaS solutions. Things are likely to get out of control pretty quickly. 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?