BI-TAGS

a lot of articles calling data scientists and statisticians the jobs of the future

there are more immediate needs that, when addressed, will have a much greater business impact.

Right now we have huge opportunities to make the data more accessible, more “joinable” and more consumable. Leaders don’t want more data – they want more information they can use to run their businesses.

Big Data is forecast to drive $34 billion of IT spending in 2013 and create 4.4 million IT jobs by 2015, but it is currently still a solution looking for a problem, according to analyst firm Gartner.

While businesses are keen to start mining their data stores for useful insights, and many are already experimenting with technologies like Hadoop, the biggest challenge is working out what question you are trying to answer

You can’t learn data visualization by memorizing a set of rules. You must understand why things work the way they do.

you must be able to think statistically

This doesn’t mean that you must learn advanced mathematics, nor can you do this work merely by learning how to use software to calculate correlation coefficients and p-values.

Mistake 1.  Not putting a strong interdisciplinary team together. It is impossible to put together an analytics platform without understanding the needs of the customers who will use it.  Sounds simple, right?  Who wouldn’t do that?  You’d be surprised how many analytics projects are wrapped up by IT because “they think” they know the customer needs.  Not assembling the right team is clearly the biggest mistake companies make.  Many times what is on your mind (and if you’re an IT person willing to admit it) is that you are considering converting all those favorite company reports.  Your goal should not be that.  Your goal is to create a system—human engineered with customers, financial people, IT folks, analysts, and others—that give people new and exciting ways to look at information.  It should give you new insights. New competitive information.  If you don’t get the right team put together, you’ll find someone longing for the good old days and their old dusty reports.  Or worse yet, still finding ways to generate those old dusty reports. Mistake 2.  Not having the right talent to design, build, run and update your analytics system.  It is undeniable that there is now high demand for business analytics specialists.  There are not a lot of them out there that really know what to do unless they’ve been burned a few times and have survived and then built successful BA systems.  This is reflected by the fact you see so many analytics vendors offer, or often recommend, third-party consulting and training to help the organization develop their business analytic skills.  Work hard to build a three-way partnership between the vendor, your own team, and an implementation partner.  If you develop those relationships, risk of failure goes way down.

Mistake 3.  Putting the wrong kind of analyst or designer on the project. This is somewhat related to Mistake 2 but with some subtle differences.  People have different skillsets so you need to make sure the person you’re considering to put on the project is the right “kind.”  For example, when you put the design together you need both drill-down and summary models.  Both have different types of users.  Does this person know how to do both?  Or, for example, inexperience in an analyst might lead to them believing vendor claims and not be able to verify them as to functionality or time to implement. Mistake 4.  Not understanding how clean the data is you are getting and the time frame to get it clean.  Profile your data to understand the quality of your source data.  This will allow you to adjust your system accordingly to compensate for some of those issues or more importantly push data fixes to your source systems.  Ensure high quality data or your risk upsetting your customers.  If you don’t have a good understanding of the quality of your data, you could easily find yourself way behind schedule even though the actual analytics and business intelligence framework you are building is coming along fine. Mistake 5.  Picking the wrong tools.  How often do organizations buy software tools that just sit on the shelve?  This often comes from management rushing into a quick decision based on a few demos they have seen.  Picking the right analytics tools requires an in-depth understanding of your requirements as well as the strengths and weaknesses of the tools you are evaluating.  The best way to achieve this understanding is by getting an unbiased implementation partner to build a proof of concept with a subset of your own data and prove out the functionality of the tools you are considering. Bottom Line.  Think things through carefully. Make sure you put the right team together.  Have a data cleansing plan.  If the hype sounds too good to be true—have someone prove it to you.

Schema on write This is the traditional approach for Business Intelligence. A model, often dimensional, is built as part of the design process. This model is an abstraction of the complexity of the underlying systems, put in business terms. The purpose of the model is to allow the business users to interrogate the data in a way they understand.

The model is instantiated through physical database tables and the date is loaded through an ETL (extract, transform and load) process that takes data from one or more source systems and transforms it to fit the model, then loads it into the model.

The key thing is that the model is determined before the data is finally written and the users are very much guided or driven by the model in how they query the data and what results they can get from the system. The designer must anticipate the queries and requests in advance of the user asking the questions.

1) Do bad customer experiences cause people to switch brands? In a 2011 research project conducted by CX application vendor RightNow, 89% of consumers said that yes, a bad experience has spurred them to switch brands. But in the brand-new study of business-executive perceptions that’s the subject of this column, only 49% of the surveyed executives said yes.  QUESTION: What steps do you need to take to close this dangerous perception gap? 2) While 97% of executives say CX is critical to the success of their company, and 91% say they’re committed to making their company a CX leader, only 20% would rate their own CX initiatives as “advanced,” with a dedicated CX leader in place, initial projects pushed to the optimization phase, and the overall project extended to new channels and groups . QUESTION: What are the obstacles preventing you from aligning your actions with your words? If you say it’s a “budget” issue, aren’t you really talking about strategic priorities rather than line items? 3) Most companies have a clear and direct understanding of the looming CX challenge and the powerful interaction of social media. The study found that the top two drivers for CX initiatives are (a) rising expectations from customers (59%),  and (b) the impact of social media on customers’ ability to broadcast good and bad experiences (37%). Now, even if you’re able to somehow rationalize those findings, here’s one that not even the most-accommodating executive can dismiss:

4) Being a CX laggard can cost those companies many tens of millions or even hundreds of millions of dollars in lost revenue: executives estimated that the lack of positive, consistent, and brand-relevant customer experience can cause them to lose out on a staggering 20% in annual revenue.

Worse yet, all that money’s likely to wind up in the pockets of your competitors!

1. Project Sponsorship and Governance 2. Project Management 3. Development Team (Core Team) 4. Extended Project Team

1. Project Sponsorship and Governance IT and the business should form a BI steering committee to sponsor and govern design, development, deployment, and ongoing support. It needs both the CIO and a business executive, such as CFO, COO, or a senior VP of marketing/sales to commit budget, time, and resources. The business sponsor needs the project to succeed. The CIO is committed to what is being built and how.

2. Project Management Project management includes managing daily tasks, reporting status, and communicating to the extended project team, steering committee, and affected business users. The project management team needs extensive business knowledge, BI expertise, DW architecture background, and people management, project management, and communications skills. The project management team includes three functions or members: Project development manager - Responsible for deliverables, managing team resources, monitoring tasks, reporting status, and communications. Requires a hands-on IT manager with a background in iterative development. Must understand the changes caused by this approach and the impact on the business, project resources, schedule and the trade-offs. Business advisor - Works within the sponsoring business organization. Responsible for the deliverables of the business resources on the project's extended team. Serves as the business advocate on the project team and the project advocate within the business community. Often, the business advocate is a project co-manager who defers to the IT project manager the daily IT tasks but oversees the budget and business deliverables. BI/DW project advisor - Has enough expertise with architectures and technologies to guides the project team on their use. Ensures that architecture, data models, databases, ETL code, and BI tools are all being used effectively and conform to best practices and standards.

OBIEE 11g skills, both in terms of new functionality (mapping, analyses, KPIs and Scorecards etc) and new infrastructure (WebLogic, EM, OPSS etc) A smattering of Essbase skills, focused mainly on the integration with OBIEE and Essbase (and the many workarounds and gotchas) Good ODI skills, both in terms of the basics, but also being able to write knowledge modules, integrate with OBIEE, deployment and migration Solid database skills – OBIEE gave the illusion through aggregates etc that database tuning was redundant, but time has shown it’s by far the biggest success factor in a project – get the database design and optimisation wrong, and your project is toast. You need to know partitioning, materialized views, index types, and increasingly, you need to get yourself on an Exadata project as customers are buying the technology but you can’t teach it to yourself at home BI Apps skills, but watch out for everything changing when BI Apps 11g comes out, and be prepared to learn the Fusion Apps and JDeveloper if you want to stay in the game Looking to the future, keep an eye on technologies such as in-memory (TimesTen), mid-tier caching (Coherence), plus technologies such as Business Activity Monitoring (BAM), “big data” (Hadoop, large data sets, NoSQL), complex event processing and maybe products such as Qlikview, just in case Oracle buys them, or at least to know what the competition are up to, or more importantly pitching to your boss

The other thing to bear in mind of course, if you’re an Oracle BI developer, is that you need to have great business, communication and data modeling skills.

ERP vendor selection. As the following graph shows, the primary candidates for ERP software were SAP, Oracle, Microsoft, Epicor, and Infor:

The cloud question. Despite the hype, only 14 percent of respondents are using ERP delivered as Software as a Service (SaaS). Although the best cloud vendors can deliver superior security and reliability than most internal IT departments, market momentum to ERP in the cloud is not there yet, as the following diagram illustrates:

Important lessons. Implementing an ERP system is always complex because the deployment drives changes to both data and processes that extend across departmental boundaries inside the organization.

Hadoop is designed to efficiently process large volumes of information by connecting many commodity computers together to work in parallel. The theoretical 1000-CPU machine described earlier would cost a very large amount of money, far more than 1,000 single-CPU or 250 quad-core machines. Hadoop will tie these smaller and more reasonably priced machines together into a single cost-effective compute cluster.