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Posts from the ‘BI CoE’ Category

2
Nov

IBM CIO Study: BI and Analytics are #1 Priority for 2012/2013


“Running a company is an endless quest to find out things you don’t know“

– Jeff Immelt, CEO GE

What will 2012 bring?  Recently, I attended the CIO Executive Leadership Summit in Greenwich, Connecticut. I was particularly intrigued by the presentation by the new CIO of IBM, Jeanette Horan where she presented the projects she was tackling and how IBM is thinking about business analytics.

IBM is making a bet that “true leaders” will develop the capabilities required for making good and timely decisions in unpredictable and stressful environments.

IBM is adapting to this new data analytics reality by a rapid-fire acquisition strategy:  Cognos,  Netezza, SPSS, ILog, CoreMetrics, Algorithmics, OpenPages, Clarity Systems, Emptoris, DemandTec (for retail).  IBM also has other information management assets like Watson, DB2 etc.  They are building a formidable capability around the value chain: “Raw Data -> Aggregate Data -> Intelligence ->Insight -> Decisions” . They see this as a $20Bln opportunity. Read more »

3
Oct

Wanted: CIO – BI/Analytics


In a tough economy, a new tech-fueled BI and analytics arms race is on to create the next competitive advantage.

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 »

7
Sep

Do you have BI Performance Anxiety ?


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 »

4
Sep

Is Your BI Project in Trouble?


Enterprise Business Intelligence (BI) project failure can happen for a variety of reasons.  Sometimes it’s due to frequent scope changes with a fixed schedule constraint, unexpected and unplanned-for “must-have” requirements changes, loss of key team members onshore or offshore,  chronic effort under-estimation, lack of proper work breakdown structure, lack of QA, and so on.

Regardless of the causes, BI, Analytics, performance management failed projects waste billions of dollars (and hours) each year.

Over the years, I have seen a lot of well-intentioned custom development, commercial, off-the-shelf package customization – SAP, Oracle, Peoplesoft ERP, CRM, SCM – and other enterprise data-warehouse projects get into trouble for a variety of reasons.  Troubled projects usually need triage, recovery, and turn-around skills to straighten things out quickly.

I am afraid that BI and Corporate Performance Management is reaching a phase in its hype cycle where we are beginning to see growing demand for troubled project recovery. It doesn’t take genius to realize that BI/Analytics project demand is growing as it is one of few remaining IT initiatives that can make companies more competitive. However, demand doesn’t imply project success. Read more »

28
Aug

The Curious Case of Salesforce and Workday: Data Integration in the Cloud


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 »

24
Jul

Proactive Risk Management – New KPIs for a Dodd-Frank World


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.

Read more »

18
Jul

Mobile BI – Business KPIs and Dashboards “on-the-go”


 

mobile-applicationsWho doesn’t want to achieve faster “time-to-information” and shorter “time-to-decision” for executives and managers with mobile BI?  Who doesn’t want to disseminate insights or KPIs to front-line employees, such as field sales representatives, line of business managers, and field service employees?

The question is not whether Mobile BI is a good idea but how to execute this program in a low-cost way?  How to design and deploy eye-popping “wow” apps? How to support, maintain and enhance these apps which are constantly changing?  What technology and infrastructure to put in for a national or global deployment? Who is going to fund all this plumbing – corporate, LoB or IT?

Business Analytics solutions for “always-on” 3/4G enabled mobile devices – iPads, iPhones, tablets, smart phones – are becoming prevalent as the form factor becomes appropriate for BI.   We are increasingly seeing firms build state-of-the-art dashboard solutions for iPads. The “post-desktop” apps provide senior management with intuitive interactive access to the company’s most important business KPIs and dealing with data overload.

Tablets, 4G Wireless and next gen displays (+gesture based, verbal interfaces) have enabled new productivity improvements and better ways to consume information, perform ad-hoc querying and scenario planning. Dashboard, heatmaps and scorecards on the iPad, iPhones and Androids are intuitive, attractive, powerful, available at any time and any place: a perfect mix for top managers, sales teams and even customers.

BI (and Information Management) is a natural fit for mobile devices.  Managers, blue and white workers spend a majority of their time away from their desks. Most are traveling, walking about or driving from site to site. And it’s these mobile workers who need the most up-to-date information. They need mobile BI to retrieve data to make on-the-spot decisions, monitor operational processes and review KPI, and work-in-process dashboards.

Read more »

12
Jul

Are you one of these — Data Scientist, Analytics Guru, Math Geek or Quant Jock?


“The sexy job in the next ten years will be statisticians…”
‐ Hal Varian, Google

Analytics Challenge — California physicians group Heritage Provider Network Inc. is offering $3 million to any person or firm who develops the best model to predict how many days a patient is likely to spend in the hospital in a year’s time. Contestants will receive “anonymized” insurance-claims data to create their models. The goal is to reduce the number of hospital visits, by identifying patients who could benefit from services such as home nurse visits.

The need for analytics talent is growing everywhere. Analytics touches everyone in the modern world. It’s no longer on the sidelines in a support role, but instead is driving business performance and  insights like never before.

Job posting analysis indicate that market demand for data scientists and analytics gurus capable of working with large real-time data sets or “big data” took a huge leap recently.  The most common definition of “big data” is real-time insights drawn from large pools of data. These datasets tend to be so large that they become awkward to work with using on-hand relational database tools, or Excel.

It’s super trendy to be labeled “big data” right now – but that doesn’t mean the business trend’s not real.  Take for the instance the following scenario in B2B supply chains. Coca-Cola Company is leveraging retailers’ POS data (e.g., Walmart) to build customer analytical snapshots, including mobile iPad reporting, and enable the CPFR (Collaborative Planning, Forecasting, and Replenishment) process in Supply Chain. Walmart alone accounts for $4 bln of Coca-Cola company sales.

Airlines, hotels, retail, financial services and e-commerce are industries that deal with big data. The trend is nothing new in financial services (low latency trading, complex event processing, straight thru processing) but radical in traditional industries.  In trading, the value of insights depends on speed of analytics.  Old data or slow analytics translate into losing money.

As data growth in business processes outpaces our ability to absorb, visualize or even process, new talent around Business Analytics will have to emerge. New roles such as Data Scientists, Analytics Savants, Quant Modelers are required in almost every corporation for converting the growing volumes of data into actionable insights.

Look at these data stats.

Read more »

3
May

Executing a BI and Analytics CoE


Most Organizations are Data Rich and  Information Poor

——————————

Data overload is becoming a huge challenge for businesses and a headache for decision makers.  Public and private sector corporations are drowning in data — from sales, transactions, pricing, supply chains, discounts, product, customer process, projects, RFID smart tags, tracking of shipments, as well as e-mail, Web traffic and social media.

I see this data problem getting worse. Enterprise software, Web and mobile technologies are more than doubling the quantity of business data every year, and the pace is quickening. But the data/information tsunami is also an enormous opportunity if and only if tamed by the right organization structure, processes, people and platforms.

A BI CoE (also called BI Shared Services or BI Competency Centers) is all about enabling this disciplined transformation along the information value chain:  “Raw Data -> Aggregated Data -> Intelligence -> Insights -> Decisions -> Operational Impact -> Financial Outcomes -> Value creation.”  A BI CoE can improve operating efficiencies by eliminating duplication and streamlining processes.

In this posting we are going to look at several aspects of executing a BI CoE:

  • What does a BI CoE need to do?
  • Insource or Outsourcing the BI CoE
  • Why do BI CoE’s Fail?
  • BI CoE Implementation Checklist

Read more »

2
May

BI, Analytics, Reporting Center of Excellence (CoE)


Everyone has data, but the more elusive goal is getting value out of that data  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?

Evolution of Data Platform

Leadership Challenge

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.

BI CoE Slide

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.

BusinessChallengeFortune500

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:

ElementsofCoE

Read more »

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