Performing Data Mining and Analytics in a Shared Services Environment

Introduction

As mature Shared Services organizations look to expand their suite of service offerings, one area that seems to be well-suited is Data Mining & Analytics ^[1].  Being able to provide this type of expert service to the company as a whole can help to position Shared Services as much more than a transactional services provider and help to improve the value proposition of the overall Shared Services operation.  However, as is the case with any new area of expertise, the challenge faced by Shared Services organizations is the ability to develop capabilities in data mining and analytics on a smaller scale before rolling out the capabilities to other organizations in the company.  A track record of success is a great foundation, with the best opportunity to achieve a broader scope ^[2] that is combined with a lower risk of failure.

Company Experience

On a recent Peeriosity Peercast^TM, a global technology manufacturer with nearly $60 billion in revenue and more than 100,000 employees shared their approach to performing Data Mining & Analytics in a Shared Services environment.  The presenter, who is a Data Scientist working in their Shared Services hub in Central America, first provided some background information regarding Data Science, describing it as an interdisciplinary field about scientific methods, processes, and systems, that is based upon mathematics, statistics, information science, and computer science.  Of utmost importance was that Data Mining & Analytics needs to add value by driving change and increasing decision-making capabilities within the company.

The feature company placed a lot of emphasis on building a diverse toolbox for their Data Mining & Analytics function, with the following points being stressed:

• Spreadsheets can go a very long way as a tool
  • Extendable through add-ins
  • Fairly complete set of visualization options

• Take advantage of visual tools
  • Can help build quick prototypes of data mining models or complete versions
  • Enables analysts to do data mining without having to program

• Learn how to program
  • Take things to the next level!

After sharing several specific examples of how Data Mining & Analytics has helped to streamline their Shared Services operation, often in conjunction with the use of their Lean Six Sigma program ^[3], the presenter then shared how important business acumen is to this capability, including (1) Technical resources alone will not be able to generate as much value as when partnering with “the business” because opportunities may be missed or misidentified or technical resources may be limited, and (2) Learning how to do this “from the inside” can facilitate opportunity identification and prioritization.

Some of the lessons learned so far for this company in the area of performing Data Mining & Analytics in Shared Services include:

• Garbage in, garbage out (do you trust your data?)
• If possible, always start with a problem statement
• If not possible, make it a second step!
• Day-to-day, operational reports can be great data starting points
• Break data silos: data can’t be only “yours” (unless it’s Top Secret )
• All hands have to be on deck, from managers to individual contributors

While there is much work ahead for this company in developing their Data Mining & Analytics capabilities, they have made excellent progress so far in creating the foundation for this function and have already experienced several wins in improving their overall Shared Services operation.  In the future, they will likely expand their capabilities in this area and begin providing these types of services to other organizations in the company.

iPolling^TM Results Review

A poll was created in conjunction with this Peercast^TM, with some interesting results.  The first question identified the role Data Mining & Analytics currently has in Shared Services.  The most popular response at 50% was that it is utilized within the Shared Services operation, but is not a service offering outside of that operation.  Just 13% are currently providing Data Mining & Analytics as part of their service offering to the rest of the company, while the remaining 37% of the companies are currently not proficient in Data Mining & Analytics.

The second question in the poll then looked at the level of focus companies expect to place on increasing proficiency within Shared Services in the area of Data Mining and Analytics over the next two years.  Reviewing the results, 69% of the companies plan on placing either a high level of focus (30%) or a moderate focus (39%) on this capability.  Just 14% are placing a limited focus on this area, while the remaining 17% have no plans at all in developing their capability in this space.

Some of the comments associated with this poll include the following:

Retail Member: Shared Service organizations are in a unique position to consistently apply Data Mining & Analytics to issues facing any company. We are focused on further enhancing this skillset, as well as the ability to communicate findings and succinct recommendations to business partners.

Consumer Products & Services Member: This year, our focus is on Robotic Process Automation.

Other Industries Member: We currently publish a catalog of data mining reports to our internal customers and have continuous improvement in this area almost quarterly.

Closing Summary

Many Shared Services organizations ^[4] are in the early stages of developing their capabilities in the area of Data Mining & Analytics.  With the proper focus and prioritization of resources, this can become an important part of the operation and can provide the opportunity to expand the current service offering of Shared Services into an area that should experience explosive growth in the next few years.

What is the status at your company with respect to developing the Data Mining & Analytics function?  Is it time to place more focus on this important opportunity for your Shared Services operation?

Who are your peers and how are you collaborating with them?

“Peercasts^TM” are private, professionally facilitated webcasts that feature leading member company experiences on specific topics as a catalyst for broader discussion.  Access is available exclusively to Peeriosity member company employees, with consultants or vendors prohibited from attending or accessing discussion content.  Members can see who is registered to attend in advance, with discussion recordings, supporting polls, and presentation materials online and available whenever convenient for the member.  Using Peeriosity’s integrated email system, Peer Mail^TM, attendees can easily communicate at any time with other attending peers by selecting them from the list of registered attendees. 

 “iPolling^TM” is available exclusively to Peeriosity member company employees, with consultants or vendors prohibited from participating or accessing content. Members have full visibility of all respondents and their comments. Using Peeriosity’s integrated email system, Peer Mail^TM, members can easily communicate at any time with others who participated in iPolling.