Daily Archives: July 25, 2014

Could You Be Doing It Right? Part III: Big Data

In last Friday’s post, we asked if you were doing it wrong. In particular, we mentioned category management, supply chain risk monitoring, and big data, and asked if you were doing them wrong. We noted that even though a number of companies have jumped on these runaway bandwagons, most have yet to grasp the reigns and take control of the wagon and get it on the right track.

Why is that?

Fundamentally, it’s the same reason that there are no world class Procurement Organizations in Asia Pacific — the classic Triple-T problem.

  • Talent
    the organizations don’t have the right talent to properly manage the initiative
  • Technology
    the organizations don’t have the right platforms to capture the right data and support the right processes
  • Transition Management
    the organizations don’t have the right processes in place to handle the necessary organizational shift to properly manage the initiative

Once the talent, technology, and transition management is in place, the organization has what it needs to fully embrace the initiative and take it to the next level. And do it right.

Where should your Supply Management Organization start? By identifying the core capabilities that are required in each “T” category and finding the right talent, technology, and transitions management for the initiative, the organization will be well on its way.

In the rest of this post, we’re going to talk about the requirements for an organization to get on the right category management track.

Talent for Big Data

Good big data scientists need the following hard and soft skills:

  • Algorithms
    there’s no magic algorithm where big data is concerned as every problem is unique and requires a unique (variant of an) algorithm
  • Domain Knowledge
    the scientist needs to know when she can be confident in the data and when she can’t; if there is not enough data, or the data is too random or skewed from expected patterns, then the scientist needs to know to trust judgement over data
  • Technical Skills
    the scientist needs to use sophisticated tools to perform her analysis
  • Logic
    the data, and algorithms, are very precise and the data scientist needs to be as well
  • Teaching
    since the majority of organizational employees will not understand what the big data scientist does, she will have to be able to explain what is needed data-wise, what the meaning of the results are, and how confident the organization can be in the results in simple terms
  • Perseverance
    since big data isn’t as simple as just dumping a bunch of data into an algorithm and accepting the result; the first, second, and tenth try won’t always generate a useful result — sometimes the data scientist, like an archaeologist, has to dig, dig, dig

Technology for Big Data

Appropriate technology platforms for big data will have at least the following features:

  • Big Data Stack
    You need an infrastructure that is scalable, replicable, and fault-tolerant.
  • Domain Specific Algorithms
    That can run on the stack and analyze the right data in the right way to generate some useable facts.
  • Powerful Reporting Engine
    That can not only generate reports useful to the scientist but to others in the organization.
  • Powerful ETL Middleware
    As you will need to extract, transform, and load data from a wide variety of sources.

Transition to Big Data

In order to transition to an organization that properly uses big data, the organization needs to hire someone with good change management skills and give that person the tools and C-suite support he or she needs to get it done. That person also needs to be a natural born leader and someone who can work with teams to get it done.

This isn’t a complete (laundry) list of what is required for big data, but it’s a good starting point. Get the right talent, technology, and transition management in place, and your organization will be well on its way to big data* success.

* Especially if you hire a good big data scientist who recognizes that sometimes the data doesn’t have to be all that big to derive a useful fact!