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.
the organizations don’t have the right talent to properly manage the initiative
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:
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
the data, and algorithms, are very precise and the data scientist needs to be as well
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
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!