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Posts tagged ‘BI CoE’

8
Mar

Spend BI and Analytics


What is your spend IQ or data maturity?

Procurement organizations tend to swim in data. One of the most important strategies for any best-in class procurement organization is spend analytics. In conjunction with sourcing, category, contract management and purchasing, spend analytics provides a window into spend behavior to drive cost reduction and cost avoidance efforts.

As a result, we are seeing a lot of interest around Spend BI and Analytics projects. Chief Procurement Officers and other Sourcing/Procurement leaders of Global, large and even mid-market firms are increasingly focusing on spend data analytics as part of a new wave of spend rationalization projects. 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 »

3
Aug

Big Data, Analytics and KPIs in E-commerce and Retail Industry


  • How to convert Lookers to Bookers…
  • How to create unique and effective Digital Experiences that impact probability of purchase or likelihood of return.
  • What offers might result in higher “take rates”

The change in consumer behavior and expectations that e-commerce, mobile and social media are causing is hugely significant – big data and predictive analytics will separate brand/retail winners from losers. This won’t happen overnight but the transformation is for real.

Retail Industry makes up a sizable part of the world economy (6-7%) and covers a large ecosystem –  E-commerce, Apparel, Department Stores, Discount Drugstores, Discount Retailers, Electronics, Home Improvement, Specialty Grocery, Specialty Retailers and Consumer Product Goods suppliers.

Retail is increasingly is looking like a barbell – a brand oriented cluster at the high-end, a very thin middle, and a price sensitive cluster at the low end. The consumerization of technology is putting more downward pricing pressure in an already competitive “middle” retail environment. The squeeze is coming from e-commerce and new “point, scan and analyze” technologies that give shoppers decision making tools — powerful pricing, promotion and product information, often in real-time. Applications in iPhones and Droid, like  Red Laser can scan barcodes and provide immediate price, product and cross-retailer comparisons. They can even point you to the nearest retailer who can give you free shipping (total cost of purchase optimization). This will lead to further margin erosion for retailers that compete based on price (a sizable chunk of the market in the U.S, Europe and Asia).

Data analytics is not new for retailers. Point of sale transactional data obtained from bar-codes first appeared in 1970s. A pack of Wrigley’s chewing gum was the first item scanned using Universal Product Code (UPC) in a Marsh Supermarket in Troy, Ohio in 1974.  Since then, retailers have been applying analytics to get even smarter and speedup the entire industry value chain.

Consumer Goods Value Chains

More recent use cases of retail analytics  include: 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 »

23
Jun

Harry Potter, The Elephant, The FBI and The Data Warehouse


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?

Read more »

15
May

New Tools for New Times – Primer on Big Data, Hadoop and “In-memory” Data Clouds


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

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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.

Read more »

3
May

Executing a BI and Analytics CoE


Most Organizations are Data Rich and  Information Poor

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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 »

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