I concluded my March 2013 report on the role of software assets in business innovation by proposing that “The combination of software assets, strong domain expertise, analytics, and as-a-service delivery models will increasingly allow traditional service providers to reinvent the way they deliver business value to their clients.” I was glad to hear that IBM recently announced a deal with L’Oréal that directly supports this position. The announced engagement actually includes all these components:
The procurement domain expertise of IBM Global Business Services addresses business pain points. L’Oréal USA grew rapidly over the past few years via an aggressive acquisition strategy that caused indirect procurement processes to remain highly disparate. The company knew that there was a significant gap between negotiated savings and realized savings in its indirect procurement operations. IBM GBS consultants brought strong procurement expertise to work with L’Oréal’s existing sourcing team to transform existing processes. IBM Global Process Services (GPS) category experts are working with L’Oréal to develop and implement category sourcing strategies.
As result of “big data” mania, there is an explosion of interest in business intelligence solutions and advanced analytics techniques. In particular, organizations of all sizes want to sharpen their ability to track the health of customer relationship management (CRM) business processes. A common question that I get from my clients is: "What are the best sales metrics that we should track, and how do we do it?"
Recently, my colleague Boris Evelsonand I responded to an inquiry on this topic. Our answer is summarized below.
"How do we set up BI dashboards for a sales-focused company? We currently have Cognos, IBI, and various cubes around a 6 (+) year old Teradata warehouse. We are upgrading our Teradata to its latest technology and have purchased IBI's BI suite to use in conjunction. Our focus is on sales -- How did other organizations start out? We would like to know what works best for different roles from the CEO down to an inside sales rep?"
We believe the answer to your question relies in adopting best practices around analytical sales performance management. You should take a top-down approach that has five steps:
1. First, define the overall sales strategy.
2. Then, identify goals and objectives that you need to achieve in order to make your sales strategy successful.
The premise: HR analytics have taken on new importance as companies work to find, develop, and retain top talent. Using analytics requires asking the right questions that address key organizational pain points and determining the metrics and best practices that will move the company toward greater productivity. We anticipate that this report will help guide HR professionals as they focus on analytics to support recruiting, performance, and learning.
HR faces a challenge of proving its value in helping to set business priorities. Data from technology solutions now give HR the opportunity to become a valued business partner in determining the appropriate metrics to help the executive suite and people in other lines of business make important talent management decisions. The tactical role of advertising for and finding employees, negotiating the hires, and bringing employees on board is no longer enough; HR must become a strategic business partner.
We recommend that you start with solid foundational components including data sources, data extraction and integration processes, master data management (MDM), and an HR data mart as the official HR data repository. Once that’s in place, you need to build queries, reports, and dashboards. Medium-size organizations may use a packaged solution, but large global enterprises with many business units will have to assemble these components.
OK, out of respect for your time, now that I’ve caught you with a title that promises some drama I’ll cut to the chase and tell you that I definitely lean toward the former. Having spent a couple of days here at Oracle Open World poking around the various flavors of Engineered Systems, including the established Exadata and Exalogic along with the new SPARC Super Cluster (all of a week old) and the newly announced Exalytic system for big data analytics, I am pretty convinced that they represent an intelligent and modular set of optimized platforms for specific workloads. In addition to being modular, they give me the strong impression of a “composable” architecture – the various elements of processing nodes, Oracle storage nodes, ZFS file nodes and other components can clearly be recombined over time as customer requirements dictate, either as standard products or as custom configurations.