Smart Use of Data: How to Design a Supply Chain and Use it Properly

Mar 20, 2015
Written by
Ton Baas
Ton Baas

Smart Use of Data: How to Design a Supply Chain and Use it Properly

If we look at current IT-trends it is clear that everybody has heard of Big Data. Although there are some known successes (for example US retailer Target which through its extensive data could predict pregnancy faster than the person involved) many companies spend millions (or even billions) of dollars hoarding big data, without using it properly. According to Gartner, 85% of Fortune 500 organizations won’t be able to exploit their big data usefully in 2015. Now the key to using data at all, is knowing that you don’t necessarily need all data. As long as you know which data can be useful to your company – and maybe even more importantly – WHERE it is useful within your company, you don’t need to spend half of your budget on storing information.

It’s all in a good start

So far, big data does not deliver on its own promise of creating insight into your business, customers and partners (which should help you make better informed business decisions). All too often, we see that many companies can’t see the wood for the trees – they have a difficult time making sense of their data, because their systems are so cluttered with information.
On the other hand we also see that there is some sensible thinking going on too: focusing on less data – but the right data, in combination with creating steady and good flows of it (much like a supply chain) through the organization. Based on these observations, there are some things that should be kept in mind when handling data and creating a solid supply chain. This may help lower spending, and improve information flows and decision-making within your organization. Here are some key aspects to consider when starting to process and use big data:

1. Identify your goals

As with any major investment, it is wise to start out with a few basic questions you should ask yourself. What do I want to achieve with this investment? What does it require from my business? What does it cost me? How does it benefit me? Most of these questions all lead up to the same thing: what is my goal?

This also goes for your data collection and analysis: Why am I collecting it? Where is it supposed to go? What do I need to do to make it work? If you can answer these questions you can scope your data management – saving you from spending a lot of time and money analyzing data you are not going to use or manage.

2. Quality over Quantity

As goes for most things in life, quality is more important than quantity. If you spend a lot of energy, money and time gathering enormous amount of data that has little to no added value for your organization, you will not get the results/insight you analyzed for. A phrase that proves to be true time and time again is: garbage in = garbage out. So I advise you to gather data that is of quality. This will most likely lead to less observations, but far more valuable insights and results.

3. Build a pipeline – get rid of silos

It is essential to make a pipeline that ensures collaboration. Get rid of data silos within different departments (HRM, CRM, finance); instead process all data the same way and make sure that the relevant information goes to the right place within your organization. To do this, try to get rid of manual steps in the collection, refinery, analysis and distribution of data. You can set-up a pipeline by using data architecture to create an overview of all information collected, its users, its providers and how it travels through your organization. The pipeline should cover:

collecting different types of data on different platforms – as long as it is clear why!
refining, integrating, cleansing data so you can analyze it
analyzing, exploring, and discovering trends and visualizing them
distribution and management of data

4. Data Architecture

It might be wise to start building a Data Architecture to make sure that you have a clear overview of your data, know where it is supposed to go, and in general have a mapping to build your data supply chain on. A Data Architecture generally sets data standards for all its systems and interactions within a view/model.

“A data architecture, in part, describes the data structures used by a business and its computer applications software. Data architectures address data in storage and data in motion; descriptions of data stores, data groups and data items; and mappings of those data artifacts to data qualities, applications, locations etc.”

5. Make insights operational

Make sure that the alignment between your data analysts and business development team is set – they need to work together to make sure the data supports your decisions. If you spend a lot of money on collecting and analyzing data, it is imperative that it should help you accomplish the goals set out by your business. If your data-team doesn’t share insights obtained in a clear or timely manner, it seems like you have invested a lot in something that never fulfills its purpose.
With a Data Architecture it’s easy to have oversight and create a good flow of data, making sure it ends up with the right person at the right time.

Ready? Set, Aim, Fire!

In this blog we have seen that there is a shift from working hard (gathering bulk-data with the hopes it gives us some insights) to working smart (gathering quality-data which delivers much better information for decision making). However this shift requires us to think differently about how to address the questions we ask and want to answer – as well as how to structure our organization so we can make the new approach work. Start out by asking yourself the right questions, creating structure and oversight in your data landscape – and end up with a nice flow of information adding value to your decision-making.

  • Do you know what your goal is? (Ready?)
  • Do you have quality data? (Set)
  • Do you have the required structure in your organization? (Aim)
  • Translate your data into insight! (Fire!)

Feel free to share your comments, questions or insights regarding data management or decision making below.